sample_size:\n\t\t\t\trows_to_sample = range(0, nrows)\n\t\t\t\tsampled_rows = numpy.random.choice(rows_to_sample, sample_size)\n\t\t\telse:\n\t\t\t\tsampled_rows = range(0, nrows)\n\n\t\tdf_subset_filtered = df_subset.iloc[sampled_rows,:]\n\n\t\tsampled_documents = pandas.concat([sampled_documents, df_subset_filtered])\n\n\tsampled_documents.groupby([\"authoritativeMN\"]).aggregate(['count']).to_csv(sample_statistics_filepath, encoding = \"utf-8\")\n\tsampled_documents.to_csv(sampled_documents_filepath, index = False, encoding = \"utf-8\")\n\n\treturn","def existing_data(self):\n # Set the directory and file name\n data_summary_dir = op.join('../logs', self.name, 'data_summary')\n file_name = 'Train_Test_Summary_generative.csv'\n\n # Read the csv and obtain the train data list\n df = pd.read_csv(op.join(data_summary_dir, file_name))\n train_data = df['Train Data'].dropna().values.tolist()\n test_data = df['Test Data'].dropna().values.tolist()\n\n train_data_list, test_data_list = [], []\n for single_train in train_data:\n data_name = single_train.split('_')[0]\n if data_name == 'LTRC':\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4]\n else:\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4] + '_' + single_train.split('_')[5]\n full_data_name = single_train.split('_')[0] + '_' + single_train.split('_')[1] + '_' + single_train.split('_')[2] + '_' + series\n train_data_list.append(full_data_name)\n\n for single_test in test_data:\n data_name = single_test.split('_')[0]\n if data_name == 'LTRC':\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4]\n else:\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4] + '_' + single_test.split('_')[5]\n full_data_name = single_test.split('_')[0] + '_' + single_test.split('_')[1] + '_' + single_test.split('_')[2] + '_' + series\n test_data_list.append(full_data_name)\n\n # Obtain the label map and CT list and file names\n label_map_list = glob(op.join(self.save_root_dir, 'source_data_2', '*'))\n ct_list = glob(op.join(self.save_root_dir, 'target_data_2', '*'))\n\n label_map_files = [single_file.split('/')[-1] for single_file in label_map_list]\n ct_files = [single_file.split('/')[-1] for single_file in ct_list]\n label_map_files.sort(), ct_files.sort()\n\n # Initialize empty list\n existing_train_lm, existing_train_ct = [], []\n existing_test_lm, existing_test_ct = [], []\n\n for single_lm, single_ct in zip(label_map_files, ct_files):\n\n ct_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\n lm_data_name = single_lm.split('_')[0] + '_' + single_lm.split('_')[1] + '_' + single_lm.split('_')[2]\n\n assert ct_data_name == lm_data_name, 'Data is not the same.'\n\n data_name = single_ct.split('_')[0]\n if data_name == 'LTRC':\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4]\n else:\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4] + '_' + single_ct.split('_')[5]\n full_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\\\n + '_' + series\n\n if full_data_name in train_data_list:\n existing_train_lm.append(single_lm)\n existing_train_ct.append(single_ct)\n if full_data_name in test_data_list:\n existing_test_lm.append(single_lm)\n existing_test_ct.append(single_ct)\n existing_train_data = [existing_train_lm, existing_train_ct]\n existing_test_data = [existing_test_lm, existing_test_ct]\n return existing_train_data, existing_test_data","def metadata_reporter(self):\n logging.info('Creating summary report')\n header = '{}\\n'.format(','.join(self.headers))\n # Create a string to store all the results\n data = str()\n for sample in self.metadata:\n # Add the value of the appropriate attribute to the results string\n data += GenObject.returnattr(sample, 'name')\n # SampleName\n data += GenObject.returnattr(sample.run, 'SamplePlate')\n # Genus\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\n # SamplePurity\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\n # N50\n n50 = GenObject.returnattr(sample.quast, 'N50',\n number=True)\n if n50 != '-,':\n data += n50\n else:\n data += '0,'\n # NumContigs\n data += GenObject.returnattr(sample.quast, 'num_contigs',\n number=True)\n # TotalLength\n data += GenObject.returnattr(sample.quast, 'Total_length',\n number=True)\n # MeanInsertSize\n data += GenObject.returnattr(sample.quast, 'mean_insert',\n number=True)\n # InsertSizeSTD\n data += GenObject.returnattr(sample.quast, 'std_insert',\n number=True)\n # AverageCoverageDepth\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\n number=True)\n # CoverageDepthSTD\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\n number=True)\n # PercentGC\n data += GenObject.returnattr(sample.quast, 'GC',\n number=True)\n # MASH_ReferenceGenome\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\n # MASH_NumMatchingHashes\n data += GenObject.returnattr(sample.mash, 'nummatches')\n # 16S_result\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\n # 16S PercentID\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\n # CoreGenesPresent\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\n # rMLST_Result\n try:\n # If the number of matches to the closest reference profile is 53, return the profile number\n if sample.rmlst.matches == 53:\n if type(sample.rmlst.sequencetype) is list:\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\n else:\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\n data += rmlst_seq_type\n else:\n # Otherwise the profile is set to new\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_Result\n try:\n if sample.mlst.matches == 7:\n if type(sample.mlst.sequencetype) is list:\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\n else:\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\n data += mlst_seq_type\n else:\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_gene_X_alleles\n try:\n # Create a set of all the genes present in the results (gene name split from allele)\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\n for gene in sorted(gene_set):\n allele_list = list()\n # Determine all the alleles that are present for each gene\n for allele in sample.mlst.combined_metadata_results:\n if gene in allele:\n allele_list.append(allele.replace(' ', '_'))\n # If there is more than one allele in the sample, add both to the string separated by a ';'\n if len(allele_list) > 1:\n data += '{},'.format(';'.join(allele_list))\n # Otherwise add the only allele\n else:\n data += allele_list[0] + ','\n # If there are fewer than seven matching alleles, add a ND for each missing result\n if len(gene_set) < 7:\n data += (7 - len(gene_set)) * 'ND,'\n except AttributeError:\n # data += '-,-,-,-,-,-,-,'\n data += 'ND,ND,ND,ND,ND,ND,ND,'\n # E_coli_Serotype\n try:\n # If no O-type was found, set the output to be O-untypeable\n if ';'.join(sample.ectyper.o_type) == '-':\n otype = 'O-untypeable'\n else:\n otype = sample.ectyper.o_type\n # Same as above for the H-type\n if ';'.join(sample.ectyper.h_type) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = sample.ectyper.h_type\n serotype = '{otype}:{htype},'.format(otype=otype,\n htype=htype)\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\n except AttributeError:\n data += 'ND,'\n # SISTR_serovar_antigen\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\n # SISTR_serovar_cgMLST\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\n # SISTR_serogroup\n data += GenObject.returnattr(sample.sistr, 'serogroup')\n # SISTR_h1\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\n # SISTR_h2\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\n # SISTR_serovar\n data += GenObject.returnattr(sample.sistr, 'serovar')\n # GeneSeekr_Profile\n try:\n if sample.genesippr.report_output:\n data += ';'.join(sample.genesippr.report_output) + ','\n else:\n data += 'ND,'\n except AttributeError:\n data += 'ND,'\n # Vtyper_Profile\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\n # AMR_Profile and resistant/sensitive status\n if sample.resfinder_assembled.pipelineresults:\n # Profile\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\n r_set=';'.join(sorted(list(resistance_set))))\n data += ','\n # Resistant/Sensitive\n data += 'Resistant,'\n else:\n # Profile\n data += 'ND,'\n # Resistant/Sensitive\n data += 'Sensitive,'\n # Plasmid Result'\n if sample.mobrecon.pipelineresults:\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\n data += '{plasmid}({details});'.format(plasmid=plasmid,\n details=details)\n data += ','\n else:\n data += 'ND,'\n # TotalPredictedGenes\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\n number=True)\n # PredictedGenesOver3000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\n number=True)\n # PredictedGenesOver1000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\n number=True)\n # PredictedGenesOver500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\n number=True)\n # PredictedGenesUnder500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\n number=True)\n # AssemblyDate\n data += datetime.now().strftime('%Y-%m-%d') + ','\n # PipelineVersion\n data += self.commit + ','\n # Name of the database used in the analyses\n data += os.path.split(self.reffilepath)[-1] + ','\n # Database download date\n data += self.download_date\n # Append a new line to the end of the results for this sample\n data += '\\n'\n # Replace any NA values with ND\n cleandata = data.replace('NA', 'ND')\n with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:\n metadatareport.write(header)\n metadatareport.write(cleandata)","def combine_summary_and_sets(sequence_read_set_file, summary_file):\n summaries = {}\n with open(summary_file) as f:\n for line in f:\n summary = json.loads(line)\n sample = summary.pop(\"sample\")\n reference_organism = summary.pop(\"reference\").lower()\n if summaries.get(sample):\n if summaries[sample].get(reference_organism):\n summaries[sample][reference_organism][\"summary_stats\"] = summary\n else:\n summaries[sample].update({reference_organism: {\n \"summary_stats\": summary\n }})\n else:\n summaries[sample]={reference_organism: {\"summary_stats\": summary}}\n f.close()\n with open(sequence_read_set_file) as f:\n for line in f:\n read_set = json.loads(line)\n sample = read_set.pop(\"sample\")\n read_set[\"urls\"] = read_set.pop(\"urls\")\n if summaries.get(sample):\n summaries[sample].update({'metadata': read_set})\n else:\n continue\n return summaries","def sample_names(self):\n with open(self.sample_sheet) as sample_sheet:\n for line in sample_sheet:\n if 'Sample_ID' in line:\n for subline in sample_sheet:\n data = subline.split(',')\n self.samples.append(data[0])","def create_samples(self):\n for s_id in range(len(self.data[\"sample\"])):\n self.samples.add(Sample(s_id, [self.data[key][s_id] for key in self.data.keys() if key not in WRONG_KEYS],\n self.data[\"label\"][s_id]))","def _prepare_samples(args):\n if args.galaxy:\n system_config = args.galaxy\n else:\n system_config = os.path.join(_get_data_dir(), \"galaxy\", \"bcbio_system.yaml\")\n config = yaml.load(open(system_config))\n config['algorithm'] = {}\n data = []\n vcf_files = [fn for fn in args.files if fn.endswith('vcf')]\n bam_files = [fn for fn in args.files if fn.endswith('bam')]\n fastq_files = [fn for fn in args.files if is_fastq(fn)]\n if not fastq_files:\n fastq_files = vcf_files\n for sample in fastq_files:\n dt = {}\n dt['name'] = splitext_plus(op.basename(sample))[0]\n dt['config'] = config\n dt['fastq'] = op.abspath(sample)\n if bam_files:\n dt['bam'] = _find_bam(bam_files, sample)\n data.append([dt])\n return data","def collect_metrics(grouped_samples, projroot, tgtdir, ext, grouping=\"sample\"):\n metrics = []\n for item_id, itemlist in grouped_samples.items():\n item = itemlist[0]\n # FIXME: tgtdir should be docroot!\n pfx = os.path.relpath(itemlist[0].prefix(grouping), os.path.dirname(tgtdir))\n mfile = glob.glob(pfx + \".*\" + ext)\n if mfile:\n metrics.append((item_id, mfile[0]))\n return PicardMetricsCollection(metrics)","def samples(self):\n if not self._samples:\n self._samples = defaultdict(list)\n blobs = self.blobs()\n sequencing = self._get_entities('sequencing')\n for s in self._get_entities('sample'):\n s = sample_factory(s, workspace=self, blobs=blobs, sequencing=sequencing, avro_path=self.avro_path)\n self._samples[s.subject_id].append(s)\n if s.missing_sequence:\n self.missing_sequence = s.missing_sequence\n return self._samples","def samples():\n f = open(config['samples'], \"r\")\n samp=[]\n for line in f:\n samp.append(line.strip().split()[0])\n return samp","def _compute_samples(self, samples):\n return samples","def gatherSamplesPerLane(self):\n if self.verbose > 0:\n msg = \"gather samples per lane...\"\n sys.stdout.write(\"%s\\n\" % msg)\n sys.stdout.flush()\n \n groupJobId = self.makeGroupJobId(\"%s_gbs-step4-gather\" % self.project2Id)\n if self.verbose > 0:\n msg = \"groupJobId=%s\" % groupJobId\n sys.stdout.write(\"%s\\n\" % msg)\n sys.stdout.flush()\n iJobGroup = JobGroup(groupJobId, self.queue2, self.lResources)\n self.jobManager.insert(iJobGroup)\n \n for laneId,iLane in self.dLanes.items():\n outDir = \"%s/%s\" % (self.allSamplesDir, iLane.id)\n iLane.gather(self.jvmXms, self.jvmXmx, self.tmpDir,\n outDir, iJobGroup)\n \n self.jobManager.submit(iJobGroup.id)\n self.jobManager.wait(iJobGroup.id, self.rmvBash, self.verbose)","def get_samples_file(foldername, filter=None):\n samples = []\n for file in os.listdir(foldername):\n if filter and file.find(filter) == -1:\n continue\n for sample in sfile(foldername + '/' + file, None).get_samples():\n samples.append(sample)\n return samples","def gatherfiles(self):\n\t\tfrom subprocess import Popen,PIPE\n\t\timport os\n\t\timport tarfile\n\t\timport glob\n\t\t\n\t\tprint \"=== \",self.nameID,\": Joining all the files in one\"\n\t\t# FIXME: Only there are 1 file, not needed the hadd\n\t\tfinalfile = os.path.join(\"Results\",self.outputfile)\n\t\t# FIXED BUG: just cp when there is only one file, otherwise\n\t\t# there are problems with the TTree\n\t\tif len(self.outputfiles) == 1:\n\t\t\t# Note that when there is only 1 file, always its #task=1\n\t\t\tcommand = [ 'cp', self.outputfiles[1], finalfile ]\n\t\telse:\n\t\t\tcommand = [ 'haddPlus', finalfile ]\n\t\t\tfor f in self.outputfiles.itervalues():\n\t\t\t\tcommand.append( f )\n\t\tp = Popen( command ,stdout=PIPE,stderr=PIPE ).communicate()\n\t\t# Checking if everything was allright\n\t\ttotalevts = self.getevents(finalfile,True)\n\t\tif totalevts != self.nevents:\n\t\t\tmessage = \"\\033[33;1mclustermanager.gatherfiles: WARNING\\033[0m the total file\"\n\t\t\tmessage += \"'\"+finalfile+\"' do not contain all the events:\\n\"\n\t\t\tmessage += \"Total events to be processed:\"+str(self.nevents)+\"\\n\"\n\t\t\tmessage += \"Total events in '\"+finalfile+\"':\"+str(totalevts)+\"\\n\"\n\t\t\tprint message\n\t\t\treturn \n\t\t# If everything was fine, deleting the files \n\t\t# and cleaning the directory\n\t\tfor f in self.outputfiles.itervalues():\n\t\t\tos.remove( f )\n\t\t# Taring and compressing\n\t\tfilestotar = glob.glob(\"./*.*\")\n\t\tfilestotar.append( \".storedmanager\")\n\t\ttar = tarfile.open(os.path.basename(self.cwd)+\".tar.gz\",\"w:gz\")\n\t\tfor f in filestotar:\n\t\t\ttar.add(f)\n\t\ttar.close()\n\t\t# if everything was fine, deleting the files\n\t\tif os.path.exists(os.path.basename(self.cwd)+\".tar.gz\"):\n\t\t\tfor f in filestotar:\n\t\t\t\tos.remove(f)\n\t\telse:\n\t\t\tmessage = \"\\033[33;1mclustermanager.gatherfiles: WARNING\\033[0m I can't manage\\n\"\n\t\t\tmessage += \"to create the backup .tar.gz file\\n\"\n\t\t\tprint message\n\n\t\tprint \"Created \"+finalfile\n\t\tprint \"========= Process Completed =========\"","def read_input_files(self):\r\n\r\n for input_file in self.list_of_input_files:\r\n input_file.read_header_of_file()\r\n self.list_of_header_objects.extend(input_file.list_of_header_objects)\r\n self.list_of_header_objects_without_ID.extend(input_file.list_of_header_objects_without_ID)\r\n self.list_of_contigs.extend(input_file.list_of_contigs)\r\n\r\n self.list_of_header_objects = list(toolz.unique(self.list_of_header_objects, key=lambda x: x.tag_and_ID))\r\n self.list_of_header_objects_without_ID = list(\r\n toolz.unique(self.list_of_header_objects_without_ID, key=lambda x: x.line))\r\n self.list_of_contigs = list(toolz.unique(self.list_of_contigs, key=lambda x: x.line))\r\n self.list_of_header_objects.extend(self.list_of_header_objects_without_ID)\r\n self.list_of_header_objects.sort(key=lambda x: x.line)\r\n self.list_of_header_objects.extend(self.list_of_contigs)\r\n self.list_of_header_objects.sort(key=lambda x: x.tag, reverse=False)\r\n self.create_body_header_line_for_output()\r\n self.write_header_in_output_file()\r\n\r\n list_of_chrom = list(self.indices.keys())\r\n list_of_chrom.sort(key=lambda x: self.alphanum_key(x))\r\n for chrom in list_of_chrom:\r\n self.list_of_body_objects.clear()\r\n for input_file in self.list_of_input_files:\r\n input_file.read_specific_chrom_body_of_file(chrom)\r\n self.list_of_body_objects.extend(input_file.list_of_body_objects)\r\n\r\n self.adjust_body_records_to_samples()\r\n self.list_of_body_objects = list(toolz.unique(self.list_of_body_objects, key=lambda x: x.line))\r\n self.list_of_body_objects.sort(key=lambda x: self.alphanum_key(x.line))\r\n self.verify_and_merge_body_records()\r\n self.write_specific_chrom_in_output_file()","def parse_picardCollect_summary(sample, file):\n with open(file, 'r') as fh:\n for l in fh:\n if l.startswith('#'):\n continue\n if l.startswith('PF_BASES'):\n parsed = l\n parsed += next(fh)\n break\n\n if len(parsed) == 0:\n return None\n else:\n df = pd.read_csv(StringIO(parsed), sep='\\t')\n df.index = [sample]\n return df","def concatenate(self, use_trim=False, top_db=None):\n spkr_num = len(self.raw_data_dir)\n print(\"No.speakers : %d\" % spkr_num)\n f_info = open(os.path.join(self.out_data_dir, 'info.pkl'), 'wb') # f to write down information of processed data\n info = []\n f_setting = open(os.path.join(self.out_data_dir, 'setting.txt'), 'wt') # f to write down settings of processing pipe\n\n # Concatenating......\n print(\"Concatenation begins...\")\n if use_trim:\n print(\"Removing nonspeech samples is activated.\")\n for i, folder in enumerate(self.raw_data_dir):\n spkr_id = self.get_spkr_name(folder_name=folder)\n print(\"%d: %sth speaker processing...\" % (i, spkr_id))\n utter_all = []\n for root, _, utter_names in os.walk(folder):\n if len(utter_names) > 0:\n utter_names = list(filter(lambda x: x.endswith(self.file_suffix), utter_names))\n for utter_name in utter_names:\n utter_path = os.path.join(root, utter_name) # absolute path of each utterance\n utter = self.load_audio(utter_path)\n utter_all.append(utter)\n utter_all = np.concatenate(utter_all, axis=0)\n\n if use_trim:\n utter_trim = []\n intervals = librosa.effects.split(utter_all, top_db=top_db)\n for interval in intervals:\n utter_trim.append(utter_all[interval[0]: interval[1]])\n utter_trim = np.concatenate(utter_trim, axis=0)\n sf.write(os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_trim, self.sr)\n info.append((os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_trim.shape[0], i))\n else:\n sf.write(os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_all, self.sr)\n info.append((os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_all.shape[0], i))\n # Concatenating succeed\n\n print(\"Writing necessary files...\")\n pickle.dump(info, f_info)\n f_info.close()\n\n info = sorted(info, key=lambda x: x[1])\n print(\"Speaker number: {}\".format(spkr_num), file=f_setting)\n print(\"Minimum length: {}\\t{}\".format(info[0][0], info[0][1]), file=f_setting)\n print(\"Maximum length: {}\\t{}\".format(info[-1][0], info[-1][1]), file=f_setting)\n print(\"sr: {}\".format(self.sr), file=f_setting)\n print(\"Use trim: {}\".format(use_trim), file=f_setting)\n print(\"top_db: {}\".format(top_db), file=f_setting)\n f_setting.close()","def format_preparation_files(run_dir, sample_sheet, output_dir, pipeline,\n verbose):\n sample_sheet = KLSampleSheet(sample_sheet)\n df_sheet = sample_sheet_to_dataframe(sample_sheet)\n\n if pipeline == 'atropos-and-bowtie2':\n click.echo('Stats collection is not supported for pipeline '\n 'atropos-and-bowtie2')\n else:\n stats = run_counts(run_dir, sample_sheet)\n\n stats['sample_name'] = \\\n df_sheet.set_index('lane', append=True)['sample_name']\n\n # returns a map of (run, project_name, lane) -> preparation frame\n preps = preparations_for_run(run_dir, df_sheet, pipeline=pipeline)\n\n os.makedirs(output_dir, exist_ok=True)\n\n for (run, project, lane), df in preps.items():\n fp = os.path.join(output_dir, f'{run}.{project}.{lane}.tsv')\n\n if pipeline == 'fastp-and-minimap2':\n # stats are indexed by sample name and lane, lane is the first\n # level index. When merging, make sure to select the lane subset\n # that we care about, otherwise we'll end up with repeated rows\n df = df.merge(stats.xs(lane, level=1), how='left',\n on='sample_name')\n\n # strip qiita_id from project names in sample_project column\n df['sample_project'] = df['sample_project'].map(\n lambda x: re.sub(r'_\\d+$', r'', x))\n\n # center_project_name is a legacy column that should mirror\n # the values for sample_project.\n df['center_project_name'] = df['sample_project']\n\n df.to_csv(fp, sep='\\t', index=False)\n\n if verbose:\n project_name = remove_qiita_id(project)\n # assume qiita_id is extractable and is an integer, given that\n # we have already passed error-checking.\n qiita_id = project.replace(project_name + '_', '')\n print(\"%s\\t%s\" % (qiita_id, abspath(fp)))","def aggregate_bam_files(wildcards):\n\tsample_name, prep_type, seq_mode, abundance_control, sample_type, seq_type = get_sample_info(wildcards)\n\n\tif \"2x\" in seq_mode and seq_type == \"bisulfite\":\n\t\tbam = 'sample_output/pe_bisulfite_aligned/raw_aligned/{sample}.bam'\n\t\tmapped_all_chr = 'sample_output/pe_bisulfite_aligned/all_chr/{sample}_mapped_all_chr.bam'\n\t\tmapped_all_chr_bai = 'sample_output/pe_bisulfite_aligned/all_chr/{sample}_mapped_all_chr.bam.bai'\n\t\tunmapped_R1 = 'sample_output/pe_bisulfite_aligned/unmapped/{sample}_pe_unmapped_R1.fastq.gz'\n\t\tunmapped_R2 = 'sample_output/pe_bisulfite_aligned/unmapped/{sample}_pe_unmapped_R2.fastq.gz'\n\n\tif \"2x\" in seq_mode and seq_type == \"standard\":\n\t\tbam = 'sample_output/pe_stdseq_aligned/raw_aligned/{sample}.bam'\n\t\tmapped_all_chr = 'sample_output/pe_stdseq_aligned/all_chr/{sample}_mapped_all_chr.bam'\n\t\tmapped_all_chr_bai = 'sample_output/pe_stdseq_aligned/all_chr/{sample}_mapped_all_chr.bam.bai'\n\t\tunmapped_R1 = 'sample_output/pe_stdseq_aligned/unmapped/{sample}_pe_unmapped_R1.fastq.gz'\n\t\tunmapped_R2 = 'sample_output/pe_stdseq_aligned/unmapped/{sample}_pe_unmapped_R2.fastq.gz'\n\n\treturn[bam, mapped_all_chr, mapped_all_chr_bai, unmapped_R1, unmapped_R2]","def _minimal_export_traces(self, outdir=None, analytes=None,\n samples=None, subset=None):\n if analytes is None:\n analytes = self.analytes\n elif isinstance(analytes, str):\n analytes = [analytes]\n\n if samples is not None:\n subset = self.make_subset(samples)\n elif not hasattr(self, 'subsets'):\n self.make_subset()\n\n if subset is None:\n samples = self.subsets['All_Analyses']\n else:\n try:\n samples = self.subsets[subset]\n except:\n raise ValueError((\"Subset '{:s}' does not .\".format(subset) +\n \"exist.\\nRun 'make_subset' to create a\" +\n \"subset.\"))\n\n focus_stage = 'rawdata'\n ud = 'counts'\n\n if not os.path.isdir(outdir):\n os.mkdir(outdir)\n\n for s in samples:\n d = self.data_dict[s].data[focus_stage]\n out = {}\n\n for a in analytes:\n out[a] = d[a]\n\n out = pd.DataFrame(out, index=self.data_dict[s].Time)\n out.index.name = 'Time'\n\n header = ['# Minimal Reproduction Dataset Exported from LATOOLS on %s' %\n (time.strftime('%Y:%m:%d %H:%M:%S')),\n \"# Analysis described in '../analysis.log'\",\n '# Run latools.reproduce to import analysis.',\n '#',\n '# Sample: %s' % (s),\n '# Analysis Time: ' + self.data_dict[s].meta['date']]\n\n header = '\\n'.join(header) + '\\n'\n\n csv = out.to_csv()\n\n with open('%s/%s.csv' % (outdir, s), 'w') as f:\n f.write(header)\n f.write(csv)\n return","def sampling():\n # make directory for street images\n streetImageOutputFolder = CONFIG[\"sampling\"][\"streetImageOutputFolder\"]\n makeDirectory(streetImageOutputFolder)\n\n # Get preprocessed point data\n intersectionPointFile = CONFIG[\"shapefile\"][\"intersectoinPointFile\"]\n pointInfoFile = CONFIG[\"shapefile\"][\"pointInfoFilename\"]\n\n pointInfo = readPointFile(pointInfoFile)\n intersectionPointInfo = readIntersectionPointInfo(intersectionPointFile)\n\n # Filter point data that has street images taken within the specified period.\n maxYear = CONFIG[\"gmap\"][\"streetImageMaxYear\"]\n minYear = CONFIG[\"gmap\"][\"streetImageMinYear\"]\n filteredPoints = filterPointByYear(pointInfo, maxYear, minYear)\n\n IMG_NAME_COL_NUM = 5\n LAT_LNG_COL_NUM = 2\n\n # Sample street images, the return is list of sample info\n sampleNum = CONFIG[\"sampling\"][\"sampleNum\"]\n initImageNumber = CONFIG[\"sampling\"][\"initImageNumber\"]\n sampleData = sampleAndDownloadStreetImage(filteredPoints, sampleNum, initImageNumber, initImageNumber, streetImageOutputFolder, intersectionPointInfo)\n imageNames = [streetImageOutputFolder + \"/\" + data[IMG_NAME_COL_NUM] for data in sampleData]\n links = GDriveUpload(imageNames, \"Sampled_Image\")\n\n for i in xrange(len(sampleData)):\n imageName = streetImageOutputFolder + \"/\" + sampleData[i][IMG_NAME_COL_NUM]\n sampleData[i].append(links[imageName])\n\n columnTitle = [\"Sample Number\", \"Sampled Point Number\", \"Latitude + Longitude\", \"Heading\", \"Date\", \"Image Name\", \"Road Types\", \"Web Link\"]\n sampleData.insert(0, columnTitle)\n\n # output to csv file\n outputCSV(sampleData, CONFIG[\"sampling\"][\"csvFilename\"])\n\n # plot images map\n sampledPoints = set([divideGPS(d[LAT_LNG_COL_NUM]) for d in sampleData[1:]])\n plotSampledPointMap(list(sampledPoints), CONFIG[\"sampling\"][\"sampledPointsMapFilename\"])","def collect_samples(self):\n self.__running = True\n with open(self.__filename, 'a') as output:\n next_sample_time = time.time()\n while self.__running:\n sensor_name = self.__sensor.get_sensor_type_name()\n sensor_id = self.__sensor.get_sensor_id()\n data = self.__sensor.retrieve_data_string() \n if DEBUG:\n print('data: \"{}\"'.format(data),\n file = sys.stderr, flush=True)\n when = datetime.datetime.now(datetime.timezone.utc).isoformat()\n result = OUTPUT_FORMAT.format(when,\n sensor_name, \n sensor_id, \n data)\n output.write(result)\n output.flush()\n \n next_sample_time = next_sample_time + self.__interval\n delay_time = next_sample_time - time.time()\n if DEBUG:\n print('delay_time = {}'.format(delay_time),\n file=sys.stderr, flush=True)\n \n if 0 < delay_time: # don't sleep if already next sample time\n time.sleep(delay_time)","def addSamples(file, spec, data_card, channel_bins, samples, shapefile):\n data_card.processes = samples\n\n for idxc, channel in enumerate(spec[\"channels\"]):\n if data_card.hasShapes == False: # counting\n for sample in channel[\"samples\"]:\n data_card.exp[channel[\"name\"]].update(\n {sample[\"name\"]: sample[\"data\"][0]}\n )\n else:\n # shapes\n data_card.shapeMap.update({channel[\"name\"]: {}})\n data_card.shapeMap[channel[\"name\"]].update(\n {\"data_obs\": [shapefile, channel[\"name\"] + \"/\" + \"data_obs\"]}\n )\n for idxs, sample in enumerate(channel[\"samples\"]):\n data = sum(sample[\"data\"])\n data_card.exp[channel[\"name\"]].update({sample[\"name\"]: data})\n\n mods = [\n spec[\"channels\"][idxc][\"samples\"][idxs][\"modifiers\"][i][\"type\"]\n for i, mod in enumerate(\n spec[\"channels\"][idxc][\"samples\"][idxs][\"modifiers\"]\n )\n ]\n if \"histosys\" in mods:\n # if shape uncertainty histogram is available\n data_card.shapeMap[channel[\"name\"]].update(\n {\n spec[\"channels\"][idxc][\"samples\"][idxs][\"name\"]: [\n shapefile,\n channel[\"name\"]\n + \"/\"\n + spec[\"channels\"][idxc][\"samples\"][idxs][\"name\"],\n channel[\"name\"]\n + \"/\"\n + spec[\"channels\"][idxc][\"samples\"][idxs][\"name\"]\n + \"_$SYSTEMATIC\",\n ]\n }\n )\n else:\n data_card.shapeMap[channel[\"name\"]].update(\n {\n spec[\"channels\"][idxc][\"samples\"][idxs][\"name\"]: [\n shapefile,\n channel[\"name\"]\n + \"/\"\n + spec[\"channels\"][idxc][\"samples\"][idxs][\"name\"],\n ]\n }\n )\n if \"staterror\" in mods:\n # if staterror is present, provide variances for histograms\n h_data = hist.Hist.new.Regular(\n channel_bins[channel[\"name\"]], 0, channel_bins[channel[\"name\"]]\n ).Weight()\n\n h_data[...] = np.stack(\n [\n spec[\"channels\"][idxc][\"samples\"][idxs][\"data\"],\n [\n i**2\n for i in spec[\"channels\"][idxc][\"samples\"][idxs][\n \"modifiers\"\n ][mods.index(\"staterror\")][\"data\"]\n ],\n ],\n axis=-1,\n )\n data_card.binParFlags.update({channel[\"name\"]: True})\n elif \"shapesys\" in mods: # for shapesys, also provide variances\n h_data = hist.Hist.new.Regular(\n channel_bins[channel[\"name\"]], 0, channel_bins[channel[\"name\"]]\n ).Weight()\n h_data[...] = np.stack(\n [\n spec[\"channels\"][idxc][\"samples\"][idxs][\"data\"],\n [\n i**2\n for i in spec[\"channels\"][idxc][\"samples\"][idxs][\n \"modifiers\"\n ][mods.index(\"shapesys\")][\"data\"]\n ],\n ],\n axis=-1,\n )\n data_card.binParFlags.update({channel[\"name\"]: True})\n else: # no staterror or shapesys->0 variance\n h_data = hist.Hist.new.Regular(\n channel_bins[channel[\"name\"]], 0, channel_bins[channel[\"name\"]]\n ).Weight()\n h_data[...] = np.stack(\n [\n spec[\"channels\"][idxc][\"samples\"][idxs][\"data\"],\n [0 for _ in range(channel_bins[channel[\"name\"]])],\n ],\n axis=-1,\n )\n file[\n channel[\"name\"]\n + \"/\"\n + spec[\"channels\"][idxc][\"samples\"][idxs][\"name\"]\n ] = h_data","def create_samples(self):\n self._samples = self.load_samples()\n self.modify_samples()","def collect_outputs(self):\n # collect submex output hdf urls and add them to top mex outputs section\n top_mex = self.bqSession.fetchxml(self.options.mexURL, view='deep')\n outputTag = top_mex.xpath('/mex/tag[@name=\"outputs\"]')\n if not outputTag:\n # no \"outputs\" tag in mex => add it now\n etree.SubElement(top_mex, 'tag', name='outputs') \n top_mex = self.bqSession.postxml(url=top_mex.get('uri'), xml=top_mex, view='deep')\n outputTag = top_mex.xpath('/mex/tag[@name=\"outputs\"]')\n outputTag = outputTag[0]\n output_hdfs = top_mex.xpath('/mex/mex/tag[@name=\"outputs\"]/tag[@name=\"output_hdf\"]/@value')\n etree.SubElement(outputTag, 'tag', name='all_outputs', value=';'.join([ohdf.split('/')[-1] for ohdf in output_hdfs]))\n self.bqSession.postxml(url=outputTag.get('uri'), xml=outputTag)","def main():\n dir_path='.'\n meas_file='magic_measurements.txt'\n samp_file=\"er_samples.txt\"\n out_file='magic_measurements.txt'\n if '-h' in sys.argv:\n print(main.__doc__)\n sys.exit()\n if '-WD' in sys.argv:\n ind = sys.argv.index('-WD')\n dir_path=sys.argv[ind+1]\n if '-f' in sys.argv:\n ind = sys.argv.index('-f')\n meas_file=sys.argv[ind+1]\n if '-fsa' in sys.argv:\n ind = sys.argv.index('-fsa')\n samp_file=sys.argv[ind+1]\n if '-F' in sys.argv:\n ind = sys.argv.index('-F')\n out_file=sys.argv[ind+1]\n # read in measurements file\n meas_file=dir_path+'/'+meas_file\n out_file=dir_path+'/'+out_file\n samp_file=dir_path+'/'+samp_file\n data,file_type=pmag.magic_read(meas_file)\n samps,file_type=pmag.magic_read(samp_file)\n MeasRecs=[]\n sampnames,sflag=[],0\n for rec in data:\n for samp in samps:\n if samp['er_sample_name'].lower()==rec['er_sample_name'].lower():\n if samp['er_sample_name'] not in sampnames:sampnames.append(samp['er_sample_name'].lower())\n rec['er_site_name']=samp['er_site_name']\n rec['er_location_name']=samp['er_location_name']\n MeasRecs.append(rec)\n break\n if rec['er_sample_name'].lower() not in sampnames:\n sampnames.append(rec['er_sample_name'].lower())\n sflag=1\n SampRec={}\n for key in list(samps[0].keys()):SampRec[key]=\"\"\n SampRec['er_sample_name']=rec['er_sample_name']\n SampRec['er_citation_names']=\"This study\"\n SampRec['er_site_name']='MISSING'\n SampRec['er_location_name']='MISSING'\n SampRec['sample_desription']='recorded added by update_measurements - edit as needed'\n samps.append(SampRec)\n print(rec['er_sample_name'],' missing from er_samples.txt file - edit orient.txt file and re-import')\n rec['er_site_name']='MISSING'\n rec['er_location_name']='MISSING'\n MeasRecs.append(rec)\n pmag.magic_write(out_file,MeasRecs,'magic_measurements')\n print(\"updated measurements file stored in \", out_file)\n if sflag==1:\n pmag.magic_write(samp_file,samps,'er_samples')\n print(\"updated sample file stored in \", samp_file)","def finalize(param, input_files='count_files'):\n\n import csv\n HELPER.writeLog('Collecting featureCount raw counts ... \\n', param)\n\n #check which of these files are actually available\n working_files = [iFile for iFile in param[input_files] if iFile != '']\n\n if len(working_files) > 0:\n #get feature ID using the first column in the first file in the list of working files\n csv_file = open(working_files[0])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n\n #For featureCount output, we want to skip the first two lines as they\n #include the featureCount call and the headers which we don't want\n next(csv_reader, None)\n next(csv_reader, None)\n\n #Now start by taking the list of identifier,\n #which is the first column in the file\n counts = [row[0] for row in csv_reader]\n csv_file.close()\n\n #get all the expression values\n header = 'ID'\n for idx in range(param['num_samples']):\n if param[input_files] != '':\n header = header+'\\t'+param['stub'][idx]\n csv_file = open(param[input_files][idx])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n\n #Here too we want to skip the first two lines, before getting the counts\n next(csv_reader, None)\n next(csv_reader, None)\n #Now start getting the counts (row[6]) and add in the ID (counts[i]) before it\n idx = 0\n for row in csv_reader:\n counts[idx] = counts[idx]+'\\t'+row[6]\n idx += 1\n csv_file.close()\n\n #output the file\n out_file = param['working_dir']+'deliverables/featureCount_raw_counts.txt'\n out_handle = open(out_file, 'w')\n out_handle.write(header+'\\n')\n\n for i in range(len(counts)):\n out_handle.write(counts[i]+'\\n')\n out_handle.close()\n\n #output_phenotype_file\n HELPER.writeLog('Writing phenotype data ... \\n', param)\n MODULE_HELPER.output_sample_info(param)\n\n #write summary stats\n #featureCount does this on its own so we can just fetch each summary file\n #check which of these files are actually available\n working_files = [iFile+'.summary' for iFile in param[input_files] if iFile != '']\n\n if len(working_files) > 0:\n #get Status column from summary file using the first column in\n #the first file in the list of working files\n csv_file = open(working_files[0])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n #Here, we want to skip the first line, as it simply points to the\n #alignment file used when running featureCount\n next(csv_reader, None)\n\n #Now start by taking the list of identifier,\n #which is the first column in the file\n entry = [row[0] for row in csv_reader]\n csv_file.close()\n\n #get all the summary stats for each sample\n header = 'Status'\n\n for idx in range(param['num_samples']):\n if param[input_files] != '':\n header = header+'\\t'+param['stub'][idx]\n #Fetch the corresponding sample's summary file\n csv_file = open(param[input_files][idx]+'.summary')\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n #Again, we want to skip the first line, as it simply points\n #to the alignment file used when running featureCount\n next(csv_reader, None)\n\n #Now start getting the stats (row[1]) and add in the Status\n # (counts[i]) before it\n i = 0\n for row in csv_reader:\n entry[i] = entry[i]+'\\t'+row[1]\n i += 1\n csv_file.close()\n #output the file\n out_handle = open(param['working_dir']+\n 'results/featureCount/featureCount_stats.txt',\n 'w')\n out_handle.write(header+'\\n')\n\n for i in range(len(entry)):\n out_handle.write(entry[i]+'\\n')\n out_handle.close()\n else:\n print 'featureCount was not run successfully on any of the files..\\n'","def generate_data():\n for subdir, dirs, files in os.walk(legend_images_dir):\n for _file in files:\n getTables(_file)\n\n file_list = []\n for subdir, dirs, files in os.walk(pdf_output_dir):\n for _file in files:\n if _file.endswith('.pdf'):\n file_list.append(_file)\n\n print (\"Writing merged output in Output.pdf...\")\n current_dir = os.getcwd()\n mergeOutput(file_list, current_dir + \"/Output.pdf\")\n\n clean()","def get_sr_series(tables, out_name, max_sample=500):\n\n pt_ct = 0\n for year in YEARS:\n for state in TARGET_STATES:\n\n name_prefix = '{}_{}_{}'.format(out_name, state, year)\n local_file = os.path.join('/home/dgketchum/IrrigationGIS/EE_extracts/to_concatenate',\n '{}.csv'.format(name_prefix))\n if os.path.isfile(local_file):\n continue\n else:\n print(local_file)\n\n roi = ee.FeatureCollection(os.path.join(BOUNDARIES, state))\n\n start = '{}-01-01'.format(year)\n d = datetime.strptime(start, '%Y-%m-%d')\n epoch = datetime.utcfromtimestamp(0)\n start_millisec = str(int((d - epoch).total_seconds() * 1000))\n\n table = ee.FeatureCollection(tables)\n table = table.filter(ee.Filter.eq('YEAR', start_millisec))\n table = table.filterBounds(roi)\n table = table.randomColumn('rnd')\n points = table.size().getInfo()\n print('{} {} {} points'.format(state, year, points))\n\n n_splits = int(ceil(points / float(max_sample)))\n ranges = linspace(0, 1, n_splits + 1)\n diff = ranges[1] - ranges[0]\n\n for enum, slice in enumerate(ranges[:-1], start=1):\n slice_table = table.filter(ee.Filter.And(ee.Filter.gte('rnd', slice),\n ee.Filter.lt('rnd', slice + diff)))\n points = slice_table.size().getInfo()\n print('{} {} {} points'.format(state, year, points))\n\n name_prefix = '{}_{}_{}_{}'.format(out_name, state, enum, year)\n local_file = os.path.join('/home/dgketchum/IrrigationGIS/EE_extracts/to_concatenate',\n '{}.csv'.format(name_prefix))\n if os.path.isfile(local_file):\n continue\n else:\n print(local_file)\n\n pt_ct += points\n if points == 0:\n continue\n\n ls_sr_masked = daily_landsat(year, roi)\n stats = ls_sr_masked.sampleRegions(collection=table,\n properties=['POINT_TYPE', 'YEAR', 'LAT_GCS', 'Lon_GCS'],\n scale=30,\n tileScale=16)\n\n task = ee.batch.Export.table.toCloudStorage(\n stats,\n description=name_prefix,\n bucket='wudr',\n fileNamePrefix=name_prefix,\n fileFormat='CSV')\n\n task.start()\n print('{} total points'.format(pt_ct))","def preprocess(self):\n \n file_name_list = os.listdir(self.image_dir)\n random.seed(1234)\n random.shuffle(file_name_list)\n \n for i,d in enumerate(self.domains):\n self.attr2idx[d]=i \n\n for i, file_name in enumerate(file_name_list):\n if (file_name.startswith('X_')):\n continue\n \n parts = file_name.split(\"-\")\n label = int(parts[0])\n if label not in self.domains:\n continue\n img_name = file_name\n\n count=self.get_sample_count(label)\n if count<self.valid_set_size:\n # create holdout set on the fly\n utils.copy_file(self.image_dir,self.valid_set_dir,img_name)\n else:\n self.dataset.append([img_name, self.attr2idx[label]])\n \n self.increment_sample_count(label)\n\n print(\"Sample count per domain: \"+str(self.sample_count)+\" (including holdout set, holdout size per domain is: \"+str(self.valid_set_size)+\")\")\n print('Finished preprocessing the dataset...')","def get_additional_data_from_files(df, file_description): # file description one of [\"video\", \"eaf\", \"seg\", \"gentle\"]\n if file_description == \"gentle\":\n file_folder = FILE_BASE + \"/gentle/\"\n is_gentle_file = True\n else:\n file_folder = FILE_BASE + \"/original/\"\n is_gentle_file = False\n\n file_df = None\n\n if file_description not in list(FILE_DESCRIPTIONS_TO_EXT.keys()):\n print(\"Unknown file description! Don't know what to do with %s files...\" % file_description)\n return None\n\n else:\n print(\"Load and extract information from %s files...\" % file_description)\n #pbar = tqdm.tqdm(total = len(np.unique(df[\"source_file\"])),desc='Files', position=0,leave=True,file=sys.stdout)\n #file_log = tqdm.tqdm(total=0, position=1, bar_format='{desc}',leave=True,file=sys.stdout)\n print(\"Total files to laod and preprocess: \", len(np.unique(df[\"source_file\"])))\n \n for i,file in enumerate(np.unique(df[\"source_file\"])):\n if i%100 == 0:\n print(\"File: \",i)\n \n filepath = file_folder + get_file_path(file,is_gentle_file=is_gentle_file) + FILE_DESCRIPTIONS_TO_EXT[file_description]\n\n if file_description == \"video\":\n file_i_df = mp4_file_processing.get_word_video_snippet_size(df, filepath)\n elif file_description == \"eaf\":\n speech_annotation_eaf_data, gesture_eaf_data = eaf_file_processing.read_eaf(filepath)\n file_i_df = eaf_file_processing.map_gestures_to_annotation(speech_annotation_eaf_data, gesture_eaf_data, remove_pauses=False)\n file_i_df = eaf_file_processing.binary_encode_gestures(file_i_df, gesture_column=\"gesture\")\n\n elif file_description == \"seg\":\n file_i_df = seg_file_processing.get_seg_file_pos_info(filepath)\n\n elif file_description == \"gentle\":\n file_i_df = gentle_file_processing.get_gentle_file_transcripts(filepath)\n \n else:\n print(\"Unknown file format!!!\")\n return \n\n if file_df is None:\n file_df = file_i_df\n else:\n file_df = pd.concat([file_df, file_i_df], ignore_index=True)\n\n #file_log.set_description_str(f'Processed file: {file}')\n #pbar.update(1)\n #sleep(0.02)\n #file_log.close()\n #pbar.close()\n return file_df","def make_stats_files(sample_dc, otu_dc, degree_counts, num_con_cat, num_con,\r\n num_cat, cat_by_sample, dir_path):\r\n output = open(os.path.join(dir_path,\r\n \"stats/real_dc_sample_degree.txt\"), 'w')\r\n sample_dc_out = sorted(sample_dc.items())\r\n sample_dc_str = '\\n'.join(['\\t'.join(map(str, t)) for t in sample_dc_out])\r\n output.write(''.join([\"# Just Sample degree counts\\n\",\r\n \"Degree\tSample Count\\n\", sample_dc_str]))\r\n output.close()\r\n\r\n output = open(os.path.join(dir_path,\r\n \"stats/real_dc_otu_degree.txt\"), 'w')\r\n otu_dc_out = sorted(otu_dc.items())\r\n otu_dc_str = '\\n'.join(['\\t'.join(map(str, t)) for t in otu_dc_out])\r\n output.write(''.join([\"# Just OTU degree counts\\n\",\r\n \"Degree\tOTU Count\\n\", otu_dc_str]))\r\n output.close()\r\n\r\n output = open(os.path.join(dir_path,\r\n \"stats/real_dc_sample_otu_degree.txt\"), 'w')\r\n dc_out = sorted(degree_counts.items())\r\n dc_str = '\\n'.join(['\\t'.join(map(str, t)) for t in dc_out])\r\n output.write(''.join([\"# Sample and OTU degree counts\\n\",\r\n \"Degree\tBoth Count \\n\", dc_str]))\r\n output.close()\r\n\r\n num_pairs = len(cat_by_sample) * (len(cat_by_sample) - 1) / 2\r\n\r\n num_pairs_line = \"NUM PAIRS: %s\" % str(num_pairs)\r\n num_cat_pairs_line = \"NUM SAME CAT PAIRS: %s\"\r\n num_con_pairs_line = \"NUM CONNECTED PAIRS: %s\" % int(num_con)\r\n\r\n for cat, num in num_con_cat.items():\r\n filename = \"stats/real_cat_stats_%s.txt\" % cat\r\n output = open(os.path.join(dir_path, filename), 'w')\r\n num_neither = int((num_pairs - num_con) - (num_cat[cat] - num))\r\n stats_line = ''.join(['(', str(int(num)), ', ', str(int(num_con - num)),\r\n ', ', str(int(num_cat[cat] - num)), ', ',\r\n str(num_neither), ')'])\r\n G_stat = G_2_by_2(int(num), int(num_con - num),\r\n int(num_cat[cat] - num), num_neither)\r\n output.write(\r\n '\\n'.join([num_pairs_line,\r\n num_cat_pairs_line % num_cat[\r\n cat],\r\n num_con_pairs_line,\r\n stats_line,\r\n str(G_stat)]))\r\n output.close()","def test_parallel_dataflow():\n\n if os.path.exists('all.txt'):\n os.remove('all.txt')\n\n # create 5 files with random numbers\n output_files = []\n for i in range(5):\n if os.path.exists('random-%s.txt' % i):\n os.remove('random-%s.txt' % i)\n output_files.append(generate(outputs=[File('random-%s.txt' % i)]))\n\n # concatenate the files into a single file\n cc = concat(inputs=[i.outputs[0]\n for i in output_files], outputs=[File(\"all.txt\")])\n\n # calculate the average of the random numbers\n totals = total(inputs=[cc.outputs[0]])\n print(totals.result())","def _generate_samples(samples_data_table=None):\n samples_data_table = samples_data_table or dict()\n\n con_name = f\"Auto_Sample_Test_{datetime.now()}\"\n con_result_soup = BeautifulSoup(_post_con(con_name), \"xml\")\n con_uri = con_result_soup.find(\"con:container\")[\"uri\"]\n\n sample_list = list()\n for i in range(1, 97, 2):\n well = (\n 'ABCDEFGH'[(i - 1) % 8] + ':' + '%01d' % ((i - 1) // 8 + 1,))\n letter = 'ABCDEFGH'[i % 8]\n to_add = api_types.Sample(f\"test{i}{letter}\")\n to_add.location = well\n to_add.con = api_types.Container(\n con_name,\n \"96 well plate\",\n \"\",\n con_uri)\n\n for data_name, data_value in samples_data_table.items():\n if \"udf\" in data_name:\n udf_name = data_name.strip(\"udf_\")\n to_add.udf_to_value[udf_name] = data_value\n elif \"adapter\" in data_name:\n to_add.adapter = data_value\n sample_list.append(to_add)\n return sample_list","def aggregate_results(output_files, agg_filename):\n\n print(file_marker + \"STARTING AGGREGATION\")\n feather_files = output_files\n\n results = []\n for i in range(len(feather_files)):\n print(file_marker + str(i))\n x = pd.read_feather(feather_files[i])\n results.append(x)\n \n overall_results = pd.concat(results, ignore_index=True, sort=False)\n opt_diff_results = overall_results\n\n opt_diff_results.reset_index(inplace=True, drop=True) \n # drop=True: column 'index' gets removed\n\n opt_diff_results.to_feather(agg_filename)\n print(file_marker + \"Aggregated results saved to: \" + agg_filename)","def constructReadCountsStr(samplesList, resultsPath, outRoot, stype):\n files = []\n for i in range(len(samplesList)):\n files.append(os.path.join(resultsPath, outRoot) + \".\" + stype + \".\" + str(i) + \".pruned.te-counts.txt\")\n return \",\".join(files)","def get_result_files(self):\n name_pattern = \"{mapper}.{ngs_library.name}\"\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"out\", name_pattern + \"{ext}\"), ext=EXT_VALUES\n )\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"log\", \"{mapper}.{ngs_library.name}.{ext}\"),\n ext=(\n \"log\",\n \"conda_info.txt\",\n \"conda_list.txt\",\n \"log.md5\",\n \"conda_info.txt.md5\",\n \"conda_list.txt.md5\",\n ),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.{report}.txt\"\n ),\n report=(\"bamstats\", \"flagstats\", \"idxstats\"),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.{report}.txt.md5\"\n ),\n report=(\"bamstats\", \"flagstats\", \"idxstats\"),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.bamstats.html\"\n )\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.bamstats.html.md5\"\n )\n )\n\n for sheet in self.shortcut_sheets:\n for ngs_library in sheet.all_ngs_libraries:\n if ngs_library.name in self.ngs_library_to_kit:\n extraction_type = ngs_library.test_sample.extra_infos[\"extractionType\"]\n suffix = (\n \"_long\"\n if ngs_library.extra_infos[\"seqPlatform\"] in (\"PacBio\", \"ONP\")\n else \"\"\n )\n # Per-sample target coverage report.\n yield from expand(\n os.path.join(\n \"output\", name_pattern, \"report\", \"cov_qc\", name_pattern + \".{ext}\"\n ),\n mapper=self.config[\"tools\"][extraction_type.lower() + suffix],\n ngs_library=[ngs_library],\n ext=[\"txt\", \"txt.md5\"],\n )\n yield \"output/target_cov_report/out/target_cov_report.txt\"\n yield \"output/target_cov_report/out/target_cov_report.txt.md5\"\n if (\n self.config[\"picard_hs_metrics\"][\"path_targets_interval_list\"]\n and self.config[\"picard_hs_metrics\"][\"path_baits_interval_list\"]\n ):\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"picard_hs_metrics\", name_pattern + \".txt\"\n )\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"picard_hs_metrics\", name_pattern + \".txt.md5\"\n )\n )\n if self.config[\"compute_coverage_bed\"]:\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"report\", \"coverage\", name_pattern + \"{ext}\"),\n ext=(\".bed.gz\", \".bed.gz.tbi\"),\n )\n else:\n print(\n \"Genome-wide coverage BED generation disabled\", file=sys.stderr\n ) # pragma: no cover","def read_sample(sample_path):\n files = os.listdir(sample_path)\n json_path = [f for f in files if f.endswith('_read-info.json')]\n assert len(json_path) == 1, 'There should be exactly 1 *_read-info.json file in the sample (.smpl)'\n # get base for sample\n with open(os.path.join(sample_path, json_path[0]), 'r') as f:\n sample = json.load(f)\n # get sample info\n info_path = [f for f in files if f.endswith('info.csv')]\n assert len(info_path) == 1, 'There should be exactly 1 *_info.csv file in the sample (.smpl)'\n sample['info'] = pd.read_csv(os.path.join(sample_path, info_path[0]))\n info_path = os.path.join(sample_path, info_path[0])\n # get individual sample\n regex = re.compile(r'id-\\d*_t-\\d*')\n pairs = [f for f in files if len(regex.findall(f)) > 0]\n for str_pair in pairs:\n pair_dir = os.path.join(sample_path, str_pair)\n pair_dict = {}\n # get df\n df_path = os.path.join(pair_dir, str_pair + '_df.csv')\n pair_dict['df'] = pd.read_csv(df_path)\n pair_dict['df_path'] = df_path\n pair_dict['info_path'] = info_path\n # get tracks\n tracks_path = os.path.join(pair_dir, str_pair + '_tracks.zarr')\n pair_dict['tracks'] = zarr.open(tracks_path)\n # get b_box\n bbox_path = os.path.join(pair_dir, str_pair + '_bbox.json')\n with open(bbox_path, 'r') as f:\n bbox = json.load(f)\n new_bbox = {}\n for key in bbox.keys():\n args = list(bbox[key])\n new_bbox[key] = slice(*args)\n pair_dict['b_box'] = new_bbox\n # find out if corrs exist and read if so\n corr_path = [f for f in os.listdir(pair_dir) if f.endswith('_corr.json')]\n if len(corr_path) > 0:\n with open(os.path.join(pair_dir, corr_path[0]), 'r') as f:\n corr = json.load(f)\n pair_dict['corr'] = corr\n # if image exists, read\n img_path = os.path.join(pair_dir, str_pair + '_image.zarr')\n if os.path.exists(img_path):\n img = zarr.open(img_path)\n #print(img, img_path)\n pair_dict['image'] = img\n # if labels exist, read\n lab_path = os.path.join(pair_dir, str_pair + '_labels.zarr')\n if os.path.exists(lab_path):\n lab = zarr.open(lab_path)\n #print(lab.shape)\n pair_dict['labels'] = lab\n # add pair to sample\n id_re = re.compile(r'id-\\d*')\n ID = id_re.findall(str_pair)[0][3:]\n t_re = re.compile(r't-\\d*')\n t = t_re.findall(str_pair)[0][2:]\n pair = (int(ID), int(t))\n sample[pair] = pair_dict\n #print(str_pair, pair)\n #print(list(sample.keys()))\n #print(list(sample[eval(pairs[0])].keys()))\n return sample","def main(file_list):\n data_store = {}\n \n for file in file_list:\n sample_id = get_sample_id(file)\n data_store[sample_id] = {}\n data_store[sample_id][\"sample_type\"], data_store[sample_id][\"out_filename\"], data_store[sample_id][\"out_location\"] = check_name(file, sample_id)\n data_store[sample_id][\"df_parameters\"], data_store[sample_id][\"df_values\"], data_store[sample_id][\"df_parameters_for_values\"] = data_in(file, sample_id)\n if data_store[sample_id][\"sample_type\"] == \"EFC\":\n binder_mass = efc_calcs(data_store[sample_id][\"df_parameters\"])\n elif data_store[sample_id][\"sample_type\"] == \"OPC\":\n binder_mass = opc_calcs(data_store[sample_id][\"df_parameters\"])\n data_store[sample_id][\"df_values\"] = tidy_val_df(data_store[sample_id][\"df_values\"], binder_mass)\n data_store[sample_id][\"df_parameters\"] = tidy_param_df(sample_id, data_store[sample_id][\"df_parameters\"], data_store[sample_id][\"out_filename\"])\n for key, value in data_store.items():\n write_to_excel(key, value[\"df_parameters\"], value[\"df_values\"], value[\"df_parameters_for_values\"], value[\"out_location\"])","def file_summaries(self):\n summaries = {}\n if 'data' not in self.coverage_data:\n return summaries\n for info in self.coverage_data['data']:\n totals = info.get('totals', None)\n if totals:\n summaries['totals'] = totals\n for finfo in info['files']:\n filename = finfo.get('filename', None)\n if not filename:\n continue # :|\n if filename in summaries:\n raise ClangCoverageFilenameCollision(\n \"colliding file name: {}\".format(filename))\n summaries[filename] = finfo.get('summary', None)\n return summaries","def read_combined_testdata(path):\n # combine all subsets into one and shuffle\n test_inputs_sub, test_labels_sub, test_sad_sub = read_shared_testdata(path)\n\n test_inputs_all, test_labels_all, test_sad_all = (dict() for _ in range(3))\n for block in test_inputs_sub:\n test_inputs_all[block], test_labels_all[block], test_sad_all[block] = \\\n concatenate_dictionary_keys(test_inputs_sub[block], test_labels_sub[block], test_sad_sub[block])\n\n return test_inputs_all, test_labels_all, test_sad_all","def get_samples():\n r = req('GET', SUB_API + 'samples', params=handle_filters())\n samples = []\n for k in demisto.get(r.json(), 'data.items'):\n samples.append(sample_to_readable(k))\n md = tableToMarkdown('ThreatGrid - List of Samples', samples, [\n 'ID', 'Filename', 'State', 'Status', 'MD5', 'SHA1', 'SHA256', 'OS', 'SubmittedAt', 'StartedAt', 'CompletedAt'\n ])\n demisto.results({\n 'Type': entryTypes['note'],\n 'EntryContext': {'ThreatGrid.Sample(val.ID == obj.ID)': samples},\n 'HumanReadable': md,\n 'ContentsFormat': formats['json'],\n 'Contents': r.json()\n })","def main(raw_args=None):\r\n args = get_args(raw_args)\r\n dataset = DATASETS[args.dataset]\r\n dataset.set_work_dir(args.path)\r\n\r\n dataset_dir = os.path.join(dataset.get_data_path(), consts.RAW_DATA_DIR)\r\n input_file = os.path.join(dataset_dir, args.input)\r\n if args.output:\r\n out_name = args.output\r\n else:\r\n out_name = OUT_FILE % (args.chr, format_number(args.size))\r\n output_file = os.path.join(dataset_dir, out_name)\r\n\r\n print(\"Output will be in: %s\" % output_file)\r\n\r\n entries = set()\r\n out = open(output_file, 'w')\r\n input = open(input_file, 'r')\r\n\r\n is_first = True\r\n for line in input:\r\n if is_first:\r\n add_headers(line.strip(), out)\r\n is_first = False\r\n else:\r\n entries.add(line)\r\n input.close()\r\n print(\"Done reading\")\r\n\r\n # Keeps track of spacers that were already included.\r\n spacers = set()\r\n # Keeps track of the number of samples already obtained.\r\n sampled = 0\r\n # Keeps track of progress for printing.\r\n chunk = 1\r\n\r\n # While we have not sampled enough...\r\n while sampled < args.size:\r\n remaining = args.size - sampled\r\n # Sample more entries.\r\n new_entries = sample(entries, spacers, remaining, dataset)\r\n sampled += len(new_entries)\r\n for entry in new_entries:\r\n out.write(common.to_csv_line(*entry))\r\n\r\n # If we have passed a progress checkpoint - prints progress.\r\n if (args.size / PRINT_NUM) * chunk < sampled:\r\n print(\"Sampled: %d\" % sampled)\r\n chunk += 1\r\n\r\n print(\"Done\")\r\n print(STATS)","def _read_sample_data(self):\n def _read_tab(pth):\n \"\"\"\n Internal read table function\n\n :param str pth: absolute path to the file to read\n :return pandas.DataFrame: table object\n \"\"\"\n if not os.path.exists(pth):\n raise SampleTableFileException(\n \"File does not exist: {}\".format(pth))\n read_csv_kwargs = {\"engine\": \"python\", \"dtype\": str,\n \"index_col\": False, \"keep_default_na\": False,\n \"na_values\": [\"\"]}\n return pd.read_csv(pth, sep=infer_delimiter(pth), **read_csv_kwargs)\n\n no_metadata_msg = \"No {} specified\"\n if CONFIG_KEY not in self:\n _LOGGER.warning(\"No config key in Project\")\n return\n if CFG_SAMPLE_TABLE_KEY not in self[CONFIG_KEY]:\n _LOGGER.debug(\"no {} found\".format(CFG_SAMPLE_TABLE_KEY))\n return\n st = self[CONFIG_KEY][CFG_SAMPLE_TABLE_KEY]\n if st:\n self[SAMPLE_DF_KEY] = _read_tab(st)\n else:\n _LOGGER.warning(no_metadata_msg.format(CFG_SAMPLE_TABLE_KEY))\n self[SAMPLE_DF_KEY] = None\n if CFG_SUBSAMPLE_TABLE_KEY in self[CONFIG_KEY]:\n if self[CONFIG_KEY][CFG_SUBSAMPLE_TABLE_KEY] is not None:\n sst = make_list(self[CONFIG_KEY][CFG_SUBSAMPLE_TABLE_KEY], str)\n self[SUBSAMPLE_DF_KEY] = [_read_tab(x) for x in sst]\n else:\n _LOGGER.debug(no_metadata_msg.format(CFG_SUBSAMPLE_TABLE_KEY))\n self[SUBSAMPLE_DF_KEY] = None","def legacy_reporter(self):\n logging.info('Creating database-friendly summary report')\n header = '{}\\n'.format(','.join(self.legacy_headers))\n # Create a string to store all the results\n data = str()\n for sample in self.metadata:\n # Add the value of the appropriate attribute to the results string\n data += GenObject.returnattr(sample, 'name')\n # SampleName\n data += GenObject.returnattr(sample.run, 'SamplePlate')\n # Genus\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\n # SequencingDate\n data += GenObject.returnattr(sample.run, 'Date')\n # Analyst\n data += GenObject.returnattr(sample.run, 'InvestigatorName')\n # Legacy ConFindr clean/contaminated call\n data += 'ND,'\n # N50\n n50 = GenObject.returnattr(sample.quast, 'N50',\n number=True)\n if n50 != '-,':\n data += n50\n else:\n data += '0,'\n # NumContigs\n data += GenObject.returnattr(sample.quast, 'num_contigs',\n number=True)\n # TotalLength\n data += GenObject.returnattr(sample.quast, 'Total_length',\n number=True)\n # MeanInsertSize\n data += GenObject.returnattr(sample.quast, 'mean_insert',\n number=True)\n # InsertSizeSTD\n data += GenObject.returnattr(sample.quast, 'std_insert',\n number=True)\n # AverageCoverageDepth\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\n number=True)\n # CoverageDepthSTD\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\n number=True)\n # PercentGC\n data += GenObject.returnattr(sample.quast, 'GC',\n number=True)\n # MASH_ReferenceGenome\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\n # MASH_NumMatchingHashes\n data += GenObject.returnattr(sample.mash, 'nummatches')\n # 16S_result\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\n # 16S PercentID\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\n # rMLST_Result\n try:\n # If the number of matches to the closest reference profile is 53, return the profile number\n if sample.rmlst.matches == 53:\n if type(sample.rmlst.sequencetype) is list:\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\n else:\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\n data += rmlst_seq_type\n else:\n # Otherwise the profile is set to new\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_Result\n try:\n if sample.mlst.matches == 7:\n if type(sample.mlst.sequencetype) is list:\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\n else:\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\n data += mlst_seq_type\n else:\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_gene_X_alleles\n try:\n # Create a set of all the genes present in the results (gene name split from allele)\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\n for gene in sorted(gene_set):\n allele_list = list()\n # Determine all the alleles that are present for each gene\n for allele in sample.mlst.combined_metadata_results:\n if gene in allele:\n allele_list.append(allele.replace(' ', '_'))\n # If there is more than one allele in the sample, add both to the string separated by a ';'\n if len(allele_list) > 1:\n data += '{},'.format(';'.join(allele_list))\n # Otherwise add the only allele\n else:\n data += allele_list[0] + ','\n # If there are fewer than seven matching alleles, add a ND for each missing result\n if len(gene_set) < 7:\n data += (7 - len(gene_set)) * 'ND,'\n except AttributeError:\n # data += '-,-,-,-,-,-,-,'\n data += 'ND,ND,ND,ND,ND,ND,ND,'\n # CoreGenesPresent\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\n # E_coli_Serotype\n try:\n # If no O-type was found, set the output to be O-untypeable\n if ';'.join(sample.ectyper.o_type) == '-':\n otype = 'O-untypeable'\n else:\n otype = sample.ectyper.o_type\n # Same as above for the H-type\n if ';'.join(sample.ectyper.h_type) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = sample.ectyper.h_type\n serotype = '{otype}:{htype},'.format(otype=otype,\n htype=htype)\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\n except AttributeError:\n data += 'ND,'\n # SISTR_serovar_antigen\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\n # SISTR_serovar_cgMLST\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\n # SISTR_serogroup\n data += GenObject.returnattr(sample.sistr, 'serogroup')\n # SISTR_h1\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\n # SISTR_h2\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\n # SISTR_serovar\n data += GenObject.returnattr(sample.sistr, 'serovar')\n # GeneSeekr_Profile\n try:\n if sample.genesippr.report_output:\n data += ';'.join(sample.genesippr.report_output) + ','\n else:\n data += 'ND,'\n except AttributeError:\n data += 'ND,'\n # Vtyper_Profile\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\n # AMR_Profile and resistant/sensitive status\n if sample.resfinder_assembled.pipelineresults:\n # Profile\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\n r_set=';'.join(sorted(list(resistance_set))))\n data += ','\n # Resistant/Sensitive\n data += 'Resistant,'\n else:\n # Profile\n data += 'ND,'\n # Resistant/Sensitive\n data += 'Sensitive,'\n # Plasmid Result'\n if sample.mobrecon.pipelineresults:\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\n data += '{plasmid}({details});'.format(plasmid=plasmid,\n details=details)\n data += ','\n else:\n data += 'ND,'\n # TotalPredictedGenes\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\n number=True)\n # PredictedGenesOver3000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\n number=True)\n # PredictedGenesOver1000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\n number=True)\n # PredictedGenesOver500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\n number=True)\n # PredictedGenesUnder500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\n number=True)\n # NumClustersPF\n data += GenObject.returnattr(sample.run, 'NumberofClustersPF')\n # Percentage of reads mapping to PhiX control\n data += GenObject.returnattr(sample.run, 'phix_aligned')\n # Error rate calculated from PhiX control\n data += GenObject.returnattr(sample.run, 'error_rate')\n # LengthForwardRead\n data += GenObject.returnattr(sample.run, 'forwardlength',\n number=True)\n # LengthReverseRead\n data += GenObject.returnattr(sample.run, 'reverselength',\n number=True)\n # Real time strain\n data += GenObject.returnattr(sample.run, 'Description')\n # Flowcell\n data += GenObject.returnattr(sample.run, 'flowcell')\n # MachineName\n data += GenObject.returnattr(sample.run, 'instrument')\n # PipelineVersion\n data += self.commit + ','\n # AssemblyDate\n data += datetime.now().strftime('%Y-%m-%d') + ','\n # SamplePurity\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\n # cgMLST\n try:\n if type(sample.cgmlst.sequencetype) is list:\n if sample.cgmlst.sequencetype:\n cgmlst_seq_type = ';'.join(sorted(sample.cgmlst.sequencetype)).rstrip(';') + ','\n else:\n cgmlst_seq_type = 'ND,'\n else:\n cgmlst_seq_type = GenObject.returnattr(sample.cgmlst, 'sequencetype')\n # cgmlst_seq_type = cgmlst_seq_type if cgmlst_seq_type != 'ND,' else 'new,'\n data += cgmlst_seq_type\n except AttributeError:\n data += 'ND,'\n # Name of the database used in the analyses\n data += os.path.split(self.reffilepath)[-1] + ','\n # Database download date\n data += self.download_date\n # Append a new line to the end of the results for this sample\n data += '\\n'\n # Replace any NA values with ND\n cleandata = data.replace('NA', 'ND')\n with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:\n metadatareport.write(header)\n metadatareport.write(cleandata)","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def tgt_samples(self, params):\r\n def save_json(save_path, file_id, samples):\r\n init_logger()\r\n for i, sample in enumerate(samples):\r\n save_ = os.path.join(save_path, \"{:s}_{:d}.json\".format(file_id, i))\r\n with open(save_, 'w') as file:\r\n json.dump(sample, file)\r\n logger.info(\"{:s} saved at {:s}\".format(save_, save_path))\r\n\r\n\r\n json_file, save_path = params\r\n init_logger()\r\n _, tgt = self.load_json(json_file)\r\n\r\n file_id = json_file.split(\"/\")[-1].split(\".\")[0]\r\n if len(tgt) >= self.args.min_sents_num and len(tgt) <= self.args.max_sents_num:\r\n tgt_ = list(tgt)\r\n random.seed(66)\r\n random.shuffle(tgt_)\r\n\r\n # make sentence pair and write in a single file\r\n positive_sents = tgt\r\n positive_pairs = [(positive_sents[i], positive_sents[i+1]) for i in range(len(positive_sents)-1)]\r\n\r\n negative_sents = tgt_\r\n negative_pairs = [(negative_sents[i], negative_sents[i+1]) for i in range(len(negative_sents)-1)]\r\n\r\n positive_samples = [{\"tgt\": pair, \"coherence\": 0} for pair in positive_pairs] # 0 represents coherent\r\n negative_samples = [{\"tgt\": pair, \"coherence\": 1} for pair in negative_pairs] # 1 represents incoherent\r\n\r\n save_json(save_path, file_id, positive_samples)\r\n save_json(save_path, file_id+\"_r\", negative_samples)","def read_combine_elia_freq_R1(path):\r\n i=0\r\n dfs = []\r\n data_files = glob.glob(path + 'FrequencyAndDemand_*')\r\n print(str(datetime.datetime.utcnow()) + \" amount of files to combine: \" + str(len(data_files)))\r\n for file in data_files:\r\n i=i+1\r\n print(str(datetime.datetime.utcnow()) + \" processing file number: \"+ str(i))\r\n df = read_elia_freq_R1(file)\r\n dfs.append(df)\r\n combined_data = pd.concat(dfs, axis = 0)\r\n return combined_data","def get_all_files(samples, directory, suffix=\"\", nested=False):\n files = []\n if type(directory) == list:\n directory = directory[0]\n for sample in samples:\n for unit in samples[sample].keys():\n if nested:\n d = \"{}/{}_{}\".format(directory, sample, unit)\n else:\n d = \"{}\".format(directory)\n if is_pe(samples[sample][unit]):\n files.append(d+\"/{}_{}_pe{}\".format(sample, unit, suffix))\n else:\n files.append(d+\"/{}_{}_se{}\".format(sample, unit, suffix))\n return files","def main():\n file_one_path, file_two_path, output_path =\\\n get_command_line_arguments(\n ['/home/ehler002/project/groups/go/Data/Cluster_Data/Dataset.txt',\n '/home/ehler002/project/groups/go/Data/Cluster_Data/translated_genes.fpkm_table',\n '/home/ehler002/project/groups/go/Data/Cluster_Data/Full_fpkm_Table.txt'])\n pattern = 'CRO_T'\n for file_path in [file_one_path, file_two_path]:\n assert os.path.exists(file_path), 'File %s does not exist.' % file_path\n start_time = datetime.datetime.now()\n print('Started concatenation at %s' % start_time)\n file_contents, headers = get_file_contents(file_two_path)\n file_contents = sort_file_contents(file_contents)\n file_contents = remove_pattern(file_contents, pattern)\n concatenate_files(file_one_path, file_contents, headers, output_path)\n print('Finished concatenation in %s' % (datetime.datetime.now() - start_time))","def collect_stats_samples(\n peaktable, \n groups, \n bioactivity_samples,\n orig_bioactiv_df,\n ):\n rt_min = min(peaktable.loc[:,\"retention_time\"])\n rt_max = max(peaktable.loc[:,\"retention_time\"])\n rt_range = (rt_max - rt_min)\n \n #Get names of samples by mining peaktable columns\n samples_set = set()\n for label in peaktable.filter(regex=\":intensity_range:max\").columns:\n samples_set.add(label.split(\":\")[1])\n \n #AD BIOACTIVITY\n #If bioactivity was provided, test if 'active' samples exist in samples_set\n #If no bioactivity provided, all samples are set as 'inactive'\n try:\n active_samples_set = set(bioactivity_samples.loc[:,'sample_name'])\n inactive_samples_set = copy.deepcopy(samples_set)\n for active_sample in active_samples_set.copy():\n if not active_sample in samples_set:\n active_samples_set.remove(active_sample)\n \n for active_sample in active_samples_set:\n inactive_samples_set.remove(active_sample)\n \n orig_bioactiv = dict()\n for id, row in orig_bioactiv_df.iterrows():\n orig_bioactiv[row['sample_name']] = row['quant_data']\n \n except:\n active_samples_set = set()\n inactive_samples_set = copy.deepcopy(samples_set)\n orig_bioactiv = dict()\n \n #AD GROUPS (METADATA)\n #Test if all samples have been assigned their groups;\n #If any unassigned samples, assign to reserved group \"GENERAL\n grouped_samples_set = set()\n for group in groups:\n for sample in groups[group].copy():\n if sample in samples_set:\n grouped_samples_set.add(sample)\n else:\n groups[group].remove(sample)\n for i in samples_set.difference(grouped_samples_set):\n groups['GENERAL'].add(i)\n \n #create a dict of samples with group info attached\n samples_list = list(samples_set)\n samples_dict = {}\n for sample in samples_list:\n for group in groups:\n if sample in groups[group]:\n samples_dict[sample] = group\n \n sample_stats = {\n \"rt_min\" : rt_min, \n \"rt_max\" : rt_max,\n \"rt_range\" : rt_range,\n \"samples_list\" : samples_list,\n \"samples_dict\" : samples_dict,\n \"groups_dict\" : groups,\n \"features_per_sample\" : {},\n \"cliques_per_sample\" : {},\n \"cliques\" : {},\n \"nr_all_cliques\" : \"\",\n \"set_all_cliques\" : set(),\n \"set_blank_cliques\" : set(),\n \"active_samples_set\" : active_samples_set,\n \"inactive_samples_set\" : inactive_samples_set,\n 'original_bioactivity' : orig_bioactiv,\n 'relative_intensity_removed_features' : [],\n 'ms2query_annotation_excluded_based_on_range_filter' : [],\n }\n return sample_stats","def produce_test_all(root_dir):\n test = 'testImnamesSe.csv'\n all_bboxes = 'bboxesDF.csv'\n test_lab = 'testLabeledDF.csv'\n\n if test in os.listdir(root_dir) and all_bboxes in os.listdir(root_dir) \\\n and test_lab in os.listdir(root_dir):\n test_imnames = pd.read_csv(osp.join(root_dir, test),\n header=None, squeeze=True)\n all_bboxes = pd.read_csv(osp.join(root_dir, all_bboxes))\n test_lab = pd.read_csv(osp.join(root_dir, test_lab))\n else:\n _, test_imnames = process_pool_mat(root_dir)\n all_bboxes, _ = process_images_mat(root_dir)\n test_lab, _ = process_test_mat(root_dir)\n\n test_all = all_bboxes[all_bboxes['imname'].isin(test_imnames)]\n test_all.index = range(test_all.shape[0])\n test_all = pd.merge(test_all, test_lab, how='outer')\n test_all['pid'] = test_all['pid'].fillna(-1)\n test_all['is_query'] = test_all['is_query'].fillna(0)\n test_all['pid'] = test_all['pid'].values.astype(np.int32)\n test_all['is_query'] = test_all['is_query'].values.astype(np.int32)\n\n test_all.to_csv(osp.join(root_dir, 'testAllDF.csv'), index=False)\n\n return test_all","def read_files(dataroot, is_test=False, preprocess=False, aug_dataset_path=None):\n\tall_filenames = sorted(glob.glob(dataroot), key=alphanum_key)\n\tsamples_raw = []\n\tpreprocessed = 0\n\tskipped = 0\n\n\tfor fname in all_filenames:\n\t\twith open(fname, 'r') as fp:\n\t\t\ttry:\n\t\t\t\tproblem_data = json.load(fp)\n\t\t\t\t#print(fname)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(f\"Error loading JSON from {fname}\", e)\n\t\t\t\traise e\n\n\t\tproblem = MATHProblem(\n\t\t\tfname,\n\t\t\tproblem_data['problem'],\n\t\t\tproblem_data['type'],\n\t\t\tproblem_data['level'],\n\t\t\texplanation=problem_data['explanation'] if not is_test else None,\n\t\t\tsolution=problem_data['solution'] if not is_test else None,\n\t\t\tequation=problem_data['equation'] if not is_test else None,\n\t\t\tjson_data=problem_data,\n\t\t\tpreprocess=preprocess,\n\t\t)\n\n\t\tif problem.no_expression:\n\t\t\tskipped += 1\n\t\tsamples_raw.append(problem)\n\n\tif not is_test and aug_dataset_path:\n\t\tprint('Adding another dataset to the samples...')\n\t\twith open(aug_dataset_path) as fp:\n\t\t\tdata = json.load(fp)\n\t\t\tfor data_item in data:\n\t\t\t\tproblem = MATHProblem(\n\t\t\t\t\t'augmented',\n\t\t\t\t\tdata_item['sQuestion'],\n\t\t\t\t\t'MWP',\n\t\t\t\t\t'NA',\n\t\t\t\t\tsolution=str(data_item['lSolutions'][0]),\n\t\t\t\t\tequation=data_item['new_equation'],\n\t\t\t\t)\n\n\t\t\t\tif problem.no_expression:\n\t\t\t\t\tskipped += 1\n\t\t\t\tsamples_raw.append(problem)\t\n\n\tprint('There are no expressions for {} in {} items'.format(skipped, len(samples_raw)))\n\treturn samples_raw","def prepare_subset_vcf_files_by_population():\n if not os.path.exists(VCF_BY_POPULATION_PATH):\n print(\"preparing subset vcf by population\")\n os.makedirs(VCF_BY_POPULATION_PATH)\n sleep(10)\n vcf_tools_runner = VCFToolsDockerRunner()\n samples = glob(f\"{SAMPLES_FOLDER}/*.csv\")\n\n with tqdm(total=len(samples)) as pbar:\n for sample in samples:\n sample = sample.replace('\\\\', '/')\n sample_name = get_filename_from_path(sample)\n sample_path = \"/\".join([IMAGE_SHARE_FOLDER_PATH] + sample.split('/')[1:])\n pbar.set_description(f\"Processing {sample_name}\")\n vcf_tools_runner(\n f\"vcf-subset -c {sample_path} \"\n f\"{IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_VCF_FILE_NAME} | fill-an-ac > \"\n f\"{IMAGE_SHARE_FOLDER_PATH}/{VCF_BY_POPULATION_FOLDER}/{sample_name}.vcf\")\n pbar.update(1)\n else:\n print(f\"Subset VCF files by population already exist in: {VCF_BY_POPULATION_PATH}\")","def gather_sample(self):\n\n for _pid in self._select_processes():\n if not self.__trackers.get(_pid):\n self.__trackers[_pid] = ProcessTracker(_pid, self._logger, self.__id)\n\n self._reset_absolute_metrics()\n\n for _tracker in self.__trackers.values():\n _metrics = _tracker.collect()\n self.record_metrics(_tracker.pid, _metrics)\n\n self._calculate_aggregated_metrics()\n self._remove_dead_processes()\n\n self.print_metrics()","def create_sample_listing():\n entries = []\n for task_name, dataset in chain(MCBackgroundsSampleDictionaryUnordered, SignalMCSampleDictionaryUnordered, DataDictionaryMuonChannelUnordered):\n matching_output = [x for x in TaskDictionaryNameUnordered if x[0] == task_name]\n if len(matching_output) > 1:\n raise RuntimeError(\"More than 1 match for %s\" % task_name)\n if len(matching_output) == 0:\n print \"No match for task %s\" % task_name\n continue\n output_name = matching_output[0][1]\n entries.append(Sample(task_name, dataset, output_name))\n\n return entries","def samples(self):\n if self._samples:\n return self._samples\n if SAMPLE_DF_KEY not in self or self[SAMPLE_DF_KEY] is None:\n _LOGGER.debug(\"No samples are defined\")\n return []","def pool_test_dataset_dir(obs_dir_fp,exp_dir_fp,file_name_delimiter=\"--\",\\\n file_name_field_order=\\\n {'file_type':0,\"prediction_method\":1,\"weighting_method\":2,\"holdout_method\":3,\\\n \"distance\":4,\"organism\":5},strict=False, verbose=True,pool_by=['distance']):\n trials = defaultdict(list)\n #We'll want a quick unzip fn for converting points to trials\n #TODO: separate out into a 'get_paired_data_from_dirs' function\n\n pooled_observations = {}\n pooled_expectations = {}\n pairs = iter_prediction_expectation_pairs(obs_dir_fp,exp_dir_fp,file_name_field_order,file_name_delimiter,verbose=verbose)\n file_number = 0\n for obs_table,exp_table,filename in pairs:\n #print \"analyzing filename:\",filename \n filename_metadata= get_metadata_from_filename(filename,file_name_field_order,\\\n file_name_delimiter,verbose=verbose)\n \n #base_tag = '%s\\t%s\\t' %(filename_metadata['holdout_method'],filename_metadata['prediction_method'])\n #tags = [base_tag+'all_results']\n if 'file_type' in pool_by:\n pool_by.remove('file_type') #we do this manually at the end\n combined_tag = ['all']*len(file_name_field_order.keys())\n for field in file_name_field_order.keys():\n #print combined_tag\n #print file_name_field_order\n idx = file_name_field_order[field]\n #print idx\n if field in pool_by:\n combined_tag[idx] = filename_metadata[field]\n tags=[file_name_delimiter.join(combined_tag)]\n \n if verbose:\n print \"Pooling by:\", pool_by\n print \"Combined tags:\",tags\n \n pooled_observations,pooled_expectations =\\\n update_pooled_data(obs_table,exp_table,tags,pooled_observations,\\\n pooled_expectations,str(file_number),verbose=verbose)\n file_number += 1\n\n return pooled_observations,pooled_expectations","def create_sample_files(bam_file_name, fractions):\n\n print(\"running sample retreival: \\n\\n\")\n\n sample_folder = \"./%s_sample_stats/\" % bam_file_name.replace(\n \"_mRNA.bam\", \"\")\n\n if not os.path.exists(sample_folder):\n os.mkdir(sample_folder)\n os.chdir(sample_folder)\n else:\n os.chdir(sample_folder)\n print(\"fractions: \", fractions)\n for decimal in fractions:\n print(\"%s: started\" % decimal)\n file_var = \"%s_sample.bam\" % str(decimal).replace(\"0.\", \"\")\n\n # extract sample\n print(bam_file_name, file_var)\n sample_cmd = \"samtools view ../%s -s %s -b -@12 > %s\" % (\n bam_file_name, '{:f}'.format(decimal), file_var)\n print(sample_cmd)\n os.system(sample_cmd)","def data_sample(complexe_list, taille):\n\n indices = random.sample(range(len(complexe_list)), taille)\n\n complex_file_names = [complexe_list[i] for i in indices]\n\n return(complex_file_names)","def generate_figures_and_xls_all_strains(outdir, cols_starts, region2data, ext, xls, group2pos, feature_names, samples):\n all_freqs = []\n # concatenate all pos and samples into one dataframe\n dframes = []\n for ri, (ref, pos) in enumerate(region2data.keys()): #regions): #[3]#; print(ref, pos, mt)\n mer, calls = region2data[(ref, pos)]\n for c, s in zip(calls, samples): \n df = pd.DataFrame(c, columns=feature_names)\n df[\"Strain\"] = s\n df[\"chr_pos\"] = \"%s:%s\"%(ref, pos)\n dframes.append(df)\n # read all tsv files\n df = pd.concat(dframes).dropna().reset_index()\n chr_pos, strains = df[\"chr_pos\"].unique(), df[\"Strain\"].unique() \n # compare individual methods\n for clf, method in (\n (KMeans(n_clusters=2), \"KMeans\"), \n (KNeighborsClassifier(), \"KNN\"), \n #(iso_new.iForest(ntrees=100, random_state=0), \"GMM+eIF\"), \n (GaussianMixture(random_state=0, n_components=2), \"GMM\"), \n (AgglomerativeClustering(n_clusters=2), \"AggClust\"), \n #(OneClassSVM(), \"OCSVM\"), \n (IsolationForest(random_state=0), \"IF\"), \n #(iso_new.iForest(ntrees=100, random_state=0), \"eIF\"), \n (RandomForestClassifier(), \"RF\"), \n ):\n fname = method\n for i, cols_start in enumerate(cols_starts, 1):\n results = []\n feat_name = \"_\".join(cols_start)\n fname = \"%s.%s\"%(method, feat_name); print(fname)\n outfn = os.path.join(outdir, \"%s.%s\"%(fname, ext))\n # narrow down the features to only signal intensity & trace\n cols = list(filter(lambda n: n.startswith(cols_start), feature_names))#; print(cols) #, \"DT\"\n # compare all samples to 0%\n s0 = samples[0]\n for s in samples[3:]: \n with np.errstate(under='ignore'):\n if \"+\" in method:\n clf2_name = method.split(\"+\")[-1]\n results += get_mod_freq_two_step(df, cols, chr_pos, [s0, s], feat_name, \n OFFSET=0.5, clf2_name=clf2_name, clf2=clf)\n elif method in (\"KNN\", \"RF\"):\n results += get_mod_freq_clf_train_test(df, cols, chr_pos, [s0, s], samples[1:3], clf, feat_name)\n else:\n results += get_mod_freq_clf(df, cols, chr_pos, [s0, s], clf, feat_name)\n \n # and store mod_freq predicted by various methods\n freqs = pd.DataFrame(results, columns=[\"chr_pos\", \"features\", \"mod_freq wt\", \"mod_freq strain\", \"strain\"])\n freqs[\"diff\"] = freqs.max(axis=1)-freqs.min(axis=1); freqs\n for name, pos in group2pos.items(): #((\"negative\", negatives), (\"pU\", pU_pos), (\"Nm\", Nm_pos)):\n freqs.loc[freqs[\"chr_pos\"].isin(pos), \"group\"] = name\n #freqs.to_csv(outfn, sep=\"\\t\"); freqs.head()\n freqs.to_excel(xls, fname, index=False)\n # plot differences between methods\n for group, pos in group2pos.items():\n freqs.loc[freqs[\"chr_pos\"].isin(pos), \"modification\"] = group\n #return freqs\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))#, sharey=\"all\")\n sns.barplot(x=\"chr_pos\", y=\"mod_freq strain\", hue=\"strain\", edgecolor=\"white\", palette=[\"#f8786fff\", \"#7aae02ff\", \"#00bfc2ff\", \"#c67afeff\"], \n data=freqs[(freqs[\"features\"]==feat_name)&(freqs[\"group\"]==\"pU\")], ax=ax1)\n sns.barplot(x=\"chr_pos\", y=\"mod_freq strain\", hue=\"strain\", edgecolor=\"white\", palette=[\"#ed823aff\", \"#1c6ca9ff\", \"#35d1bbff\", \"#c978fdff\"], \n data=freqs[(freqs[\"features\"]==feat_name)&(freqs[\"group\"]==\"Nm\")], ax=ax2)\n ax1.set_ylabel(\"Per-site stoichiometry\"); ax2.set_ylabel(\"\")\n ax1.get_legend().remove(); ax2.get_legend().remove()#ax1.legend([]); ax2.legend([])\n ax1.set_ylim(0, 1); ax2.set_ylim(0, 1); #ax2.set(aspect=1.7)\n ax1.set_title(\"pU modifications\"); ax2.set_title(\"Nm modifications\")\n fig.suptitle(fname)\n fig.savefig(outfn)\n plt.close() # clear axis\n freqs[\"name\"] = fname\n all_freqs.append(freqs)\n return all_freqs","def output_files(self):\n # Output file for Moller generation\n if 'moller' in self.name:\n return ['moller.stdhep']\n # Output file for beam generation\n return ['beam.stdhep']","def __init__(self):\n self.nlp = spacy.load('en_core_web_sm')\n self.summary_file = open(\"results/question_driven_answer_summ_collection_stats.txt\", \"w\", encoding=\"utf-8\")","def getEMPSampleList(self, study_id, web_app_user_id):\n samples = []\n con = self.getMetadataDatabaseConnection()\n results = con.cursor()\n con.cursor().callproc('qiime_assets.get_emp_sample_list', [study_id, web_app_user_id, results])\n for row in results:\n # sample_id, avg_emp_score, user_emp_score, sample_name, emp_status, sample_location, \n # sample_progress, description, altitude, samp_size,\n # temp, samp_store_temp, country, depth, elevation, env_biome, env_feature, \n # env_matter, ph, latitude, longitude, chem_administration, samp_store_loc\n samples.append((row[0], row[1], row[2], row[3], row[4], row[5], row[6], \n row[7], row[8], row[9], row[10], row[11], row[12], row[13], row[14], \n row[15], row[16], row[17], row[18], row[19], row[20], row[21], row[22]))\n \n \n return samples","def test_multiple_output_files(self):\r\n convert_fastq(self.fasta_file_path, self.qual_file_path,\r\n multiple_output_files=True,\r\n output_directory=self.output_dir,\r\n per_file_buffer_size=23)\r\n\r\n sample_ids = [('PC.634', expected_fastq_634_default),\r\n ('PC.354', expected_fastq_354_default),\r\n ('PC.481', expected_fastq_481_default)]\r\n for sample_id, expected_output in sample_ids:\r\n actual_output_file_path = get_filename_with_new_ext(\r\n self.fasta_file_path,\r\n '_' + sample_id + '.fastq',\r\n self.output_dir)\r\n\r\n actual_output_file = open(actual_output_file_path)\r\n actual_output = actual_output_file.read()\r\n actual_output_file.close()\r\n self._files_to_remove.append(actual_output_file_path)\r\n\r\n self.assertEquals(actual_output, expected_output)","def adjust_body_records_to_samples(self):\r\n if len(self.list_of_samples_to_be_combined) == 0:\r\n self.determinate_samples_to_be_combined()\r\n Body_header_line.list_of_samples_to_be_combined = self.list_of_samples_to_be_combined\r\n Body_record.list_of_samples_to_be_combined = self.list_of_samples_to_be_combined\r\n for body_object in self.list_of_body_objects:\r\n body_object.update_line()","def get_samples(paths_to_batches_info, google_bucket_id, sublist=None):\n paths_to_samples = pd.read_excel(paths_to_batches_info, index_col=0)\n df_list = []\n\n for tsca_id, paths in paths_to_samples.iterrows():\n if sublist is not None and tsca_id not in sublist:\n continue\n # Make data Firecloud-compatible\n batch_data = prepare_batch_samples_for_metadata_export(paths.path_to_samples_info, tsca_id, google_bucket_id)\n df_list.append(batch_data)\n\n all_samples = pd.concat(df_list, axis=0)\n \n # Add cohort codes to data\n cohort_formatted_names = pd.read_table('cohort_files/cohort_names_dictionary.txt', header=None, names=['Collection', 'cohort_code'])\n all_samples = pd.merge(all_samples, cohort_formatted_names, on='Collection', how='left')\n return all_samples","def _fetch_data(self, samples):\n pass","def test_multiple_output_files(self):\r\n convert_fastaqual(self.fasta_file_path,\r\n multiple_output_files=True,\r\n output_directory=self.output_dir,\r\n per_file_buffer_size=23)\r\n\r\n sample_id_s = [('PC.634', expected_fasta_634_default,\r\n expected_qual_634_default),\r\n ('PC.354', expected_fasta_354_default,\r\n expected_qual_354_default),\r\n ('PC.481', expected_fasta_481_default,\r\n expected_qual_481_default)]\r\n for sample_id, expected_fasta, expected_qual in sample_id_s:\r\n actual_output_fasta_path = get_filename_with_new_ext(\r\n self.fasta_file_path,\r\n '_' + sample_id + '.fna',\r\n self.output_dir)\r\n\r\n actual_output_qual_path = get_filename_with_new_ext(\r\n self.fasta_file_path,\r\n '_' + sample_id + '.qual',\r\n self.output_dir)\r\n\r\n actual_output_fasta = open(actual_output_fasta_path)\r\n actual_output_qual = open(actual_output_qual_path)\r\n actual_fasta = actual_output_fasta.read()\r\n actual_output_fasta.close()\r\n actual_qual = actual_output_qual.read()\r\n actual_output_qual.close()\r\n self._files_to_remove.append(actual_output_fasta_path)\r\n self._files_to_remove.append(actual_output_qual_path)\r\n\r\n self.assertEquals(actual_fasta, expected_fasta)\r\n self.assertEquals(actual_qual, expected_qual)","def test_pipeline_data(self, doc_list):\n summaries = []\n for item in doc_list:\n summaries.append(self.preprocess(item))\n return summaries","def test01Summarize(self):\n \n summaryFile = re.sub(\"\\.csv\",\"\",self.parsedFile)+\"_summary.csv\"\n if os.path.exists(summaryFile):\n os.remove(summaryFile)\n\n self.bm.create_summarized(self.parsedFile,uniprot=True)\n self.assertTrue(os.path.exists(summaryFile))","def run_sample(smp: sample.Sample,\n run_dir: Text,\n summary_file: Optional[Text] = None,\n generate_sample_ns: Optional[int] = None):\n start = time.time()\n # Create a script named 'run.sh' for rerunning the sample.\n args = [\n SAMPLE_RUNNER_MAIN_PATH,\n '--logtostderr',\n '--input_file=sample.x',\n '--options_file=options.pbtxt',\n ]\n\n _write_to_file(run_dir, 'sample.x', smp.input_text)\n _write_to_file(run_dir, 'options.pbtxt', smp.options.to_pbtxt())\n args_filename = 'args.txt'\n _write_to_file(\n run_dir, args_filename, sample.args_batch_to_text(smp.args_batch)\n )\n args.append('--args_file=args.txt')\n ir_channel_names_filename = None\n if smp.ir_channel_names is not None:\n ir_channel_names_filename = 'ir_channel_names.txt'\n _write_to_file(run_dir, ir_channel_names_filename,\n sample.ir_channel_names_to_text(smp.ir_channel_names))\n args.append('--ir_channel_names_file=ir_channel_names.txt')\n args.append(run_dir)\n _write_to_file(\n run_dir,\n 'run.sh',\n f'#!/bin/sh\\n\\n{subprocess.list2cmdline(args)}\\n',\n executable=True)\n logging.vlog(1, 'Starting to run sample')\n logging.vlog(2, smp.input_text)\n runner = sample_runner.SampleRunner(run_dir)\n runner.run_from_files(\n 'sample.x', 'options.pbtxt', args_filename, ir_channel_names_filename\n )\n timing = runner.timing\n\n timing.total_ns = int((time.time() - start) * 1e9)\n if generate_sample_ns:\n # The sample generation time, if given, is not part of the measured total\n # time, so add it in.\n timing.total_ns += generate_sample_ns\n timing.generate_sample_ns = generate_sample_ns\n\n logging.vlog(1, 'Completed running sample, elapsed: %0.2fs',\n time.time() - start)\n\n if summary_file:\n _write_ir_summaries(run_dir, timing, summary_file)","def generate_sample_sheet(self):\n pool = self.pool\n bcl2fastq_sample_ids = []\n i7_names = []\n i7_sequences = []\n i5_names = []\n i5_sequences = []\n wells = []\n plate = pool.container.external_id\n sample_ids = []\n sequencer_type = self.sequencer.equipment_type\n\n for component in pool.components:\n lp_composition = component['composition']\n # Get the well information\n wells.append(lp_composition.container.well_id)\n # Get the i7 index information\n i7_comp = lp_composition.i7_composition.primer_set_composition\n i7_names.append(i7_comp.external_id)\n i7_sequences.append(i7_comp.barcode)\n # Get the i5 index information\n i5_comp = lp_composition.i5_composition.primer_set_composition\n i5_names.append(i5_comp.external_id)\n i5_sequences.append(i5_comp.barcode)\n # Get the sample id\n sample_id = lp_composition.normalized_gdna_composition.\\\n gdna_composition.sample_composition.content\n sample_ids.append(sample_id)\n\n # Transform te sample ids to be bcl2fastq-compatible\n bcl2fastq_sample_ids = [\n SequencingProcess._bcl_scrub_name(sid) for sid in sample_ids]\n # Reverse the i5 sequences if needed based on the sequencer\n i5_sequences = SequencingProcess._sequencer_i5_index(\n sequencer_type, i5_sequences)\n\n data = SequencingProcess._format_sample_sheet_data(\n bcl2fastq_sample_ids, i7_names, i7_sequences, i5_names,\n i5_sequences, wells=wells, sample_plate=plate,\n description=sample_ids, sample_proj=self.run_name,\n lanes=self.lanes, sep=',')\n\n contacts = {c.name: c.email for c in self.contacts}\n pi = self.principal_investigator\n principal_investigator = {pi.name: pi.email}\n sample_sheet_dict = {\n 'comments': SequencingProcess._format_sample_sheet_comments(\n principal_investigator=principal_investigator,\n contacts=contacts),\n 'IEMFileVersion': '4',\n 'Investigator Name': pi.name,\n 'Experiment Name': self.experiment,\n 'Date': str(self.date),\n 'Workflow': 'GenerateFASTQ',\n 'Application': 'FASTQ Only',\n 'Assay': self.assay,\n 'Description': '',\n 'Chemistry': 'Default',\n 'read1': self.fwd_cycles,\n 'read2': self.rev_cycles,\n 'ReverseComplement': '0',\n 'data': data}\n return SequencingProcess._format_sample_sheet(sample_sheet_dict)","def merge_summaries(root_dir: str,output_file: str=None) -> pd.DataFrame:\n #\n print (f'Collecting the available summary files in {root_dir}, can take time... please wait.')\n sumfiles = glob.glob(f\"{root_dir}/**/*smry.txt\",recursive=True)\n nsums = len(sumfiles)\n print (f\"Found {nsums} summary files in {root_dir}\")\n #\n # will concatenate all smry.txt files into one temporary file and then will put it in pandas DataFrame and \n # save as CSV\n #\n with tempfile.NamedTemporaryFile(mode='w') as fp:\n for sumfile in tqdm(sumfiles,desc='Collecting the summaries'):\n with open(sumfile,'r') as sfile:\n fp.write(sfile.read())\n #\n # now read as pandas dataframe\n #\n colnames = [\"rev\",\"obsid\",\"expid\",\"mode\",\"filt\",\"tstart\",\"tend\",\"texpo\",\"mvcratio\", # (a rough measure of the ratio of counts in the MnKa versus continuum)\n \"qboxt0\",\"qboxt1\",\"qboxt2\",\"qboxt3\", # x 4 (electronics quadrant box temperatures)\n \"ndisclin_mean0\",\"ndisclin_mean1\",\"ndisclin_mean2\",\"ndisclin_mean3\", #x 4\n \"mipsel0\",\"mipsel1\",\"mipsel2\",\"mipsel3\", #x 4 (parameter for on-board MIP rejection algorithm)\n \"maxmip0\",\"maxmip1\",\"maxmip2\",\"maxmip3\", #x 4 (parameter for on-board MIP rejection algorithm)\n \"ndisclin_med0\",\"ndisclin_med1\",\"ndisclin_med2\",\"ndisclin_med3\", #median x 4\n \"ndisclin_std0\",\"ndisclin_std1\",\"ndisclin_std2\",\"ndisclin_std3\"] #, stddev x 4\n\n df = pd.read_csv(fp.name,delimiter='\\s+',header=None,skip_blank_lines=True,names=colnames)\n #\n # now calculate the time_delta, the difference in years from observation start and 2000-01-01\n #\n stime = [(datetime.strptime(x,\"%Y-%m-%dT%H:%M:%S\")-time0).total_seconds()/(365.0*24.0*3600.0) for x in df.tstart]\n df.insert(6,\"delta_time\",pd.Series(stime,index=df.index))\n #\n print (f'Last observation t={df.delta_time.max():.2f} years')\n if (output_file is not None):\n df.to_csv(output_file)\n fp.close()\n return df","def samples(\n self,\n pcollection_ids: Optional[Iterable[str]] = None\n ) -> beam_fn_api_pb2.SampleDataResponse:\n ret = beam_fn_api_pb2.SampleDataResponse()\n\n with self._samplers_lock:\n samplers = self._samplers.copy()\n\n for pcoll_id in samplers:\n if pcollection_ids and pcoll_id not in pcollection_ids:\n continue\n\n samples = samplers[pcoll_id].flush()\n if samples:\n ret.element_samples[pcoll_id].elements.extend(samples)\n\n return ret","def pull_sample_data(self, sample_id: str, sample_size: int or None = None, include_controls: bool = True,\n output_format: str = 'dataframe', columns_default: str = 'marker') -> None or list:\n db = connection.get_db(alias='core')\n db_name = db.name\n file_grp = self.pull_sample(sample_id)\n if not file_grp:\n return None\n files = file_grp.files\n # Fetch data\n if not include_controls: # Fetch data for primary file only\n file_id = [f for f in files if f.file_type == 'complete'][0].file_id\n connection.disconnect('core')\n connection._connections = {}\n connection._connection_settings = {}\n connection._dbs = {}\n FileGroup._collection = None\n\n complete = data_from_file(file_id=file_id,\n filegrp_id=file_grp.id,\n db_name=db_name,\n sample_size=sample_size,\n output_format=output_format,\n columns_default=columns_default)\n connection.connect(db=db_name, alias='core')\n return [complete]\n # Fetch data for primary file & controls\n files = [f.file_id for f in file_grp.files]\n connection.disconnect('core')\n connection._connections = {}\n connection._connection_settings = {}\n connection._dbs = {}\n FileGroup._collection = None\n\n pool = Pool(cpu_count())\n f = partial(data_from_file,\n filegrp_id=file_grp.id,\n db_name=db_name,\n sample_size=sample_size,\n output_format=output_format,\n columns_default=columns_default)\n data = pool.map(f, files)\n pool.close()\n pool.join()\n connection.connect(db=db_name, alias='core')\n return data","def features_combine():\n\n\n\t# PROCESSING AUDIO","def app_SN_plot_all_active_sample(self, sample_list: List, phases):\n\n for sample in sample_list:\n sample_name = sample['sample_name']\n print(f'processing: {sample_name}')\n dfs, phase_dirs, sizer_val, styler_val = self.preprocessing_before_plotting(sample, phases=phases)\n\n self.app_SN_combined_master(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_combined_temperature_series(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_combined_temperature_gradient(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_combined_moisture_series(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_combined_moisture_gradient(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_combined_series_moist_vs_temp(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_separate_temperature_series(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_separate_temperature_gradient(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_separate_moisture_series(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_separate_moisture_gradient(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_separate_series_moist_vs_temp(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_last_24h_plots(dfs, phase_dirs, sizer_val, styler_val)\n self.app_SN_hot_end_temperature(dfs, phase_dirs, sizer_val, styler_val)","def samples_path():\n dir = os.path.dirname(os.path.abspath(__file__))\n samples = os.path.join(dir, 'samples')\n return samples","def store_overall_means(src_file: H5File) -> None:\n perp_sum = 0\n par_sum = 0\n ref_sum = 0\n counts = 0\n for path in rawnav.pump_group_paths(src_file):\n perp_path = path + '/perp'\n par_path = path + '/par'\n ref_path = path + '/ref'\n perp_sum += src_file[perp_path].attrs['mean']\n par_sum += src_file[par_path].attrs['mean']\n ref_sum += src_file[ref_path].attrs['mean']\n counts += 1\n src_file.attrs['perp_mean'] = perp_sum / counts\n src_file.attrs['par_mean'] = par_sum / counts\n src_file.attrs['ref_mean'] = ref_sum / counts\n return","def final_output_analysis(samples_dict, dir_results_path):\n with open(path.join(dir_results_path, 'corrupted_processes.txt'), 'w', encoding='utf-8', errors='replace') as c_out:\n with open(path.join(dir_results_path, 'analysis.txt'), 'w', encoding='utf-8', errors='replace') as i_out:\n with open(path.join(dir_results_path, 'syscalls.txt'), 'w', encoding='utf-8', errors='replace') as s_out:\n for uuid in sorted(samples_dict.keys()):\n reduced_sample = samples_dict[uuid]\n\n i_out.write('{} {}\\n'.format(string_utils.filename, uuid))\n s_out.write('{} {}\\n'.format(string_utils.filename, uuid))\n c_out.write('{} {}\\n'.format(string_utils.filename, uuid))\n\n # corrupted processes section\n process_repr = '\\t\\t{:15s}\\t{:10d}\\t{:15s}\\tby:\\t{:15s}\\t{:10d}\\n'\n for process in reduced_sample.corrupted_processes:\n c_out.write(process_repr.format(process[0],\n process[1],\n process[2],\n process[3],\n process[4]))\n\n # instruction count section\n i_out.write(string_utils.out_final + '\\t' + str(reduced_sample.total_instruction) + '\\n')\n i_out.write(string_utils.out_terminating + '\\t' + str(reduced_sample.terminate_all) + '\\t')\n i_out.write(string_utils.out_sleeping + '\\t' + str(reduced_sample.sleep_all) + '\\t')\n i_out.write(string_utils.out_crashing + '\\t' + str(reduced_sample.crash_all) + '\\t')\n i_out.write(string_utils.out_raising_error + '\\t' + str(reduced_sample.error_all) + '\\t')\n i_out.write(string_utils.out_writes_file + '\\t' + str(reduced_sample.write_file) + '\\n')\n\n # system calls count section\n s_out.write(string_utils.syscall_final + '\\t' + str(reduced_sample.total_syscalls) + '\\n')\n\n i_out.write('\\n')\n s_out.write('\\n')\n c_out.write('\\n')","def extract_info_from_arguments(self):\r\n\r\n for sample_name in self.arguments['--sample_name']:\r\n self.list_of_samples_to_be_combined.append(sample_name)\r\n\r\n for file_path in self.arguments['--input_file']:\r\n file_object = Input_file(file_path, self.list_of_samples_to_be_combined)\r\n self.indices.update(file_object.indices)\r\n self.list_of_input_files.append(file_object)\r\n self.list_of_input_files_paths.append(file_path)\r\n\r\n if self.arguments['--out']:\r\n if self.arguments['--output_format'] == 'COMPRESSED':\r\n self.compressed = True\r\n elif self.arguments['--output_format'] == 'UNCOMPRESSED':\r\n self.compressed = False\r\n else:\r\n if self.list_of_input_files[0].compressed:\r\n self.compressed = True\r\n else:\r\n self.compressed = False","def _generate_examples(self, files):\n idx = 0\n for filename in files:\n with open(filename) as file:\n for line in file:\n yield idx, {\"text\": line}\n idx += 1","def samples(self, groups=None):\n groups = check_parameters_for_validity(\n groups, \"groups\", self.groups(), self.groups()\n )\n all_samples = []\n\n if \"train\" in groups:\n all_samples.extend(self.background_model_samples())\n groups.remove(\"train\")\n\n for grp in groups:\n all_samples.extend(_sample_sets_to_samples(self.references(grp)))\n all_samples.extend(_sample_sets_to_samples(self.probes(grp)))\n\n # Add znorm samples. Returning znorm samples for one group of dev or\n # eval is enough because they are duplicated.\n for grp in groups:\n all_samples.extend(_sample_sets_to_samples(self.zprobes(grp)))\n break\n\n # Add tnorm samples.\n all_samples.extend(_sample_sets_to_samples(self.treferences()))\n\n return all_samples","def main(output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n\n baseurl = 'http://codeandbeer.org/virtual/BigData/Labs/'\n files = ['Booking-20151012-1322.csv', 'Booking-20181025-1232.csv']\n for filename in files:\n r = requests.get(baseurl+filename, stream=True)\n if r.status == 200:\n with open(output_filepath+\"/\"+filename, \"wb\") as f:\n f.raw.decode_content = True\n shutil.copyfileobj(r.raw, f)","def get_nested_samples(self, filename='nested_samples.dat'):\n import numpy.lib.recfunctions as rfn\n self.nested_samples = rfn.stack_arrays(\n [s.asnparray()\n for s in self.NS.nested_samples]\n ,usemask=False)\n if filename:\n np.savetxt(os.path.join(\n self.NS.output_folder, filename),\n self.nested_samples.ravel(),\n header=' '.join(self.nested_samples.dtype.names),\n newline='\\n',delimiter=' ')\n return self.nested_samples","def main(input_folder, output_folder, bounding_boxes_file, cores, resampling,\n order):\n logger = logging.getLogger(__name__)\n logger.info('Resampling')\n\n if not os.path.exists(output_folder):\n os.mkdir(output_folder)\n print('resampling is {}'.format(str(resampling)))\n bb_df = pd.read_csv(bounding_boxes_file)\n bb_df = bb_df.set_index('PatientID')\n files_list = [\n f for f in glob.glob(input_folder + '/**/*.nii.gz', recursive=True)\n ]\n resampler = Resampler(bb_df, output_folder, order, resampling=resampling)\n with Pool(cores) as p:\n p.map(resampler, files_list)","def _collect_all(self):","def collect_data(base='.'):\n files = glob(join(base, 'accuracy_*.json'))\n data = {}\n for file in files:\n fields = basename(file).split('_')\n fields = fields[1:-2]\n accuracy = load(open(file))\n weights = load(open(file.replace('accuracy', 'weights')))\n for model in accuracy.keys():\n # add both accuracy and weights\n key = tuple(fields + [model])\n value = accuracy[model] + weights[model]\n data[key] = value\n return data","def test_get_file_copy_list(self):\n \n so = sys.stdout\n dn = open(os.devnull,\"w\")\n \n # Create a file hierarchy to search for files\n root = tempfile.mkdtemp(prefix=\"test_casava_data_delivery_\")\n date = \"111111\"\n fcs = [\"{}_{}\".format(date,fcid) for fcid in [\"FCA\",\"FCB\"]]\n \n # Create some sample files\n exp_files = []\n samples = []\n for n in xrange(2):\n sample = tempfile.mkdtemp(dir=root)\n samples.append(os.path.basename(sample))\n for fcid in fcs:\n fcdir = os.path.join(sample,fcid)\n nophixdir = os.path.join(fcdir,\"nophix\")\n for d in [fcdir,nophixdir]:\n os.makedirs(d)\n test_names = [\"{:d}_{:s}_1_1_fastq.txt.gz\".format(random.randint(1,8),\n fcid),\n \"{}_CGATGT_L001_R1_001.fastq.gz\".format(samples[-1]),\n \"{}_CGATGT_L001_R1_001.fastq..gz\".format(samples[-1]),]\n for test_name in test_names:\n test_file = os.path.join(d,test_name)\n open(test_file,\"w\").close()\n exp_files.append([samples[-1],\n fcid,\n os.path.basename(d) == \"nophix\",\n test_file,\n os.path.join(samples[-1],fcid),\n create_final_name(os.path.basename(test_name),date,fcid.split(\"_\")[-1],samples[-1])])\n \n # Get the list of files to copy under various conditions\n \n for deliver_all_fcs in [False, True]:\n for fcid in fcs:\n for deliver_nophix in [False, True]:\n for skip_sample_list in [[],[samples[0]],[samples[1]],samples]:\n sys.stdout = dn\n obs_to_copy = sorted(get_file_copy_list(root,\"\",fcid,deliver_all_fcs,deliver_nophix,skip_sample_list))\n sys.stdout = so\n exp_to_copy = sorted([ef[3:6] for ef in exp_files if (deliver_all_fcs or ef[1] == fcid) and \\\n deliver_nophix == ef[2] and \\\n ef[0] not in skip_sample_list])\n #import pdb; pdb.set_trace()\n self.assertListEqual(obs_to_copy,\n exp_to_copy,\n \"The files to copy result did not match the expected for \" \\\n \"{:s}\".format(\", \".join([\"{:s}: {:s}\".format(k,v) for k, v in \\\n dict(zip([\"deliver_all_fcs\",\n \"fcid\",\n \"deliver_nophix\",\n \"skip_samples\"],\n [str(deliver_all_fcs),\n fcid,\n str(deliver_nophix),\n \" \".join(skip_sample_list)])).items()])))","def test_split_fasta_on_sample_ids_to_files(self):\r\n temp_output_dir = mkdtemp()\r\n self.dirs_to_remove.append(temp_output_dir)\r\n\r\n split_fasta_on_sample_ids_to_files(\r\n parse_fasta(self.fasta2),\r\n output_dir=temp_output_dir,\r\n per_sample_buffer_size=2)\r\n self.files_to_remove.extend(glob('%s/*fasta' % temp_output_dir))\r\n\r\n # confirm that all files are as expected\r\n self.assertEqual(open('%s/Samp1.fasta' % temp_output_dir).read(),\r\n \">Samp1_42\\nACCGGTT\\n>Samp1_43 some comme_nt\\nAACCG\\n>Samp1_44\\nA\\n\")\r\n self.assertEqual(open('%s/s2_a.fasta' % temp_output_dir).read(),\r\n \">s2_a_50\\nGGGCCC\\n\")\r\n self.assertEqual(open('%s/s3.fasta' % temp_output_dir).read(),\r\n \">s3_25\\nAAACCC\\n\")\r\n # confirm number of files is as expected\r\n self.assertEqual(len(glob('%s/*' % temp_output_dir)), 3)","def fixture_samples(sample_single) -> Iterator[dict]:\n _samples = []\n sample_id = sample_single[\"sample_id\"]\n for number in range(3):\n sample = copy.deepcopy(sample_single)\n sample[\"sample_id\"] = \"_\".join([sample_id, str(number)])\n _samples.append(sample)\n return _samples"],"string":"[\n \"def gather_sample(self, my_file, collector=None):\\n\\n pass\",\n \"def per_sample_dot_files(self):\\n # The output directory #\\n directory = DirectoryPath(self.a.out_dir+'per_sample_ontology/')\\n directory.create_if_not_exists()\\n # Main loop #\\n for i, sample in self.df_sample_concepts.iteritems():\\n # File path #\\n sanitized_name = \\\"\\\".join([c for c in sample.name if re.match(r'\\\\w', c)])\\n dot_path = directory + sanitized_name +'.dot'\\n pdf_path = directory + sanitized_name +'.pdf'\\n # Counts #\\n counts = sample / sample.sum()\\n counts = dict(counts)\\n envos = counts.keys()\\n # Skip sample if it has no counts #\\n if sample.sum() == 0: continue\\n # Make graph #\\n graph = self.a.ontology.get_subgraph(envos)\\n graph = self.a.ontology.add_weights(graph, counts)\\n graph = self.a.ontology.add_style(graph)\\n # Write output #\\n self.a.ontology.write_to_dot(graph, dot_path)\\n self.a.ontology.add_legend(dot_path)\\n self.a.ontology.draw_to_pdf(dot_path, pdf_path)\",\n \"def load_sample(self):\\n\\n self.load_images(self.folder + \\\"/sampleSet.txt\\\")\\n self.load_traces(self.folder + \\\"/sampleLabel.txt\\\")\",\n \"def samples(self):\\n pass\",\n \"def export_sample_related_mip(args):\\n clarity_epp.export.sample.sample_related_mip(lims, args.process_id, args.output_file)\",\n \"def export_removed_samples(args):\\n clarity_epp.export.sample.removed_samples(lims, args.output_file)\",\n \"def summarize(self):\\n self.smalltalk += \\\"\\\\n Data IDs in this bundle: \\\\n\\\"\\n self._files = {}\\n inv_dict = {}\\n # sort IDs to make sure pdfs are printed in same oder as they were\\n # taken\\n for k, v in self.stage_summaries.items():\\n for qc_id in flatten_list(v):\\n inv_dict[qc_id] = k\\n sorted_ids = list(flatten_list(self.stage_summaries.values()))\\n sorted_ids.sort(key=int)\\n # for stage, value in self.stage_summaries.items():\\n for qc_run_id in sorted_ids:\\n # stage = inv_dict[qc_run_id]\\n # if stage[0:7] == 'failed_':\\n # stage = stage[7:]\\n # try:\\n # s = self.comments[qc_run_id]\\n # except KeyError:\\n # s = ''\\n # self.comments[qc_run_id] = 'Classified as poor result.\\\\n' + s\\n ds = Dataset(qc_run_id, self.db_name)\\n device_name = ds.device_name\\n f_folder = os.path.join(self.db_folder, \\\"tuning_results\\\", device_name)\\n # for qc_run_id in flatten_list(value):\\n self.smalltalk += str(qc_run_id) + \\\", \\\"\\n\\n # filename = stage + '_fit_ds'\\n # filename += str(qc_run_id) + '.png'\\n filename = os.path.join(f_folder, str(ds.ds.guid) + \\\".png\\\")\\n\\n self._files[str(qc_run_id)] = filename\",\n \"def sampleDocuments(sample_size = 250):\\n\\n\\tdocuments_csv_filepath = getScriptDirectory() + \\\"/result/documents.csv\\\"\\n\\tsampled_documents_filepath = getScriptDirectory() + \\\"/result/sampled_documents.csv\\\"\\n\\tsample_statistics_filepath = getScriptDirectory() + \\\"/result/statistics.csv\\\"\\n\\n\\t# Check if the sample already exists\\n\\tif(os.path.isfile(sampled_documents_filepath)):\\n\\t\\tprint \\\"Sample already exists. Moving on to getting metadata.\\\"\\n\\n\\t\\treturn\\n\\n\\t# Check if result folder has been made\\n\\tresult_folder = getScriptDirectory() + \\\"/result\\\"\\n\\n\\tif not os.path.exists(result_folder):\\n\\t\\tos.makedirs(result_folder)\\n\\n\\n\\t# Read in the existing documents\\n\\tdocuments = pandas.read_csv(documents_csv_filepath)\\n\\n\\tunique_mns = pandas.unique(documents['authoritativeMN'])\\n\\tsampled_documents = pandas.DataFrame({'identifier' : [], 'authoritativeMN' : []})\\n\\n\\tfor mn in unique_mns:\\n\\t\\tdf_subset = documents[documents.authoritativeMN == mn]\\n\\t\\tnrows = df_subset.shape[0]\\n\\n\\t\\tprint(\\\" Member node \\\" + mn + \\\" has \\\" + str(nrows) + \\\" rows\\\")\\n\\n\\t\\tif nrows is 0:\\n\\t\\t\\tcontinue\\n\\t\\telif nrows is 1:\\n\\t\\t\\tsampled_rows = [0]\\n\\t\\telse:\\n\\t\\t\\tif nrows > sample_size:\\n\\t\\t\\t\\trows_to_sample = range(0, nrows)\\n\\t\\t\\t\\tsampled_rows = numpy.random.choice(rows_to_sample, sample_size)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsampled_rows = range(0, nrows)\\n\\n\\t\\tdf_subset_filtered = df_subset.iloc[sampled_rows,:]\\n\\n\\t\\tsampled_documents = pandas.concat([sampled_documents, df_subset_filtered])\\n\\n\\tsampled_documents.groupby([\\\"authoritativeMN\\\"]).aggregate(['count']).to_csv(sample_statistics_filepath, encoding = \\\"utf-8\\\")\\n\\tsampled_documents.to_csv(sampled_documents_filepath, index = False, encoding = \\\"utf-8\\\")\\n\\n\\treturn\",\n \"def existing_data(self):\\n # Set the directory and file name\\n data_summary_dir = op.join('../logs', self.name, 'data_summary')\\n file_name = 'Train_Test_Summary_generative.csv'\\n\\n # Read the csv and obtain the train data list\\n df = pd.read_csv(op.join(data_summary_dir, file_name))\\n train_data = df['Train Data'].dropna().values.tolist()\\n test_data = df['Test Data'].dropna().values.tolist()\\n\\n train_data_list, test_data_list = [], []\\n for single_train in train_data:\\n data_name = single_train.split('_')[0]\\n if data_name == 'LTRC':\\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4]\\n else:\\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4] + '_' + single_train.split('_')[5]\\n full_data_name = single_train.split('_')[0] + '_' + single_train.split('_')[1] + '_' + single_train.split('_')[2] + '_' + series\\n train_data_list.append(full_data_name)\\n\\n for single_test in test_data:\\n data_name = single_test.split('_')[0]\\n if data_name == 'LTRC':\\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4]\\n else:\\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4] + '_' + single_test.split('_')[5]\\n full_data_name = single_test.split('_')[0] + '_' + single_test.split('_')[1] + '_' + single_test.split('_')[2] + '_' + series\\n test_data_list.append(full_data_name)\\n\\n # Obtain the label map and CT list and file names\\n label_map_list = glob(op.join(self.save_root_dir, 'source_data_2', '*'))\\n ct_list = glob(op.join(self.save_root_dir, 'target_data_2', '*'))\\n\\n label_map_files = [single_file.split('/')[-1] for single_file in label_map_list]\\n ct_files = [single_file.split('/')[-1] for single_file in ct_list]\\n label_map_files.sort(), ct_files.sort()\\n\\n # Initialize empty list\\n existing_train_lm, existing_train_ct = [], []\\n existing_test_lm, existing_test_ct = [], []\\n\\n for single_lm, single_ct in zip(label_map_files, ct_files):\\n\\n ct_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\\n lm_data_name = single_lm.split('_')[0] + '_' + single_lm.split('_')[1] + '_' + single_lm.split('_')[2]\\n\\n assert ct_data_name == lm_data_name, 'Data is not the same.'\\n\\n data_name = single_ct.split('_')[0]\\n if data_name == 'LTRC':\\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4]\\n else:\\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4] + '_' + single_ct.split('_')[5]\\n full_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\\\\\\n + '_' + series\\n\\n if full_data_name in train_data_list:\\n existing_train_lm.append(single_lm)\\n existing_train_ct.append(single_ct)\\n if full_data_name in test_data_list:\\n existing_test_lm.append(single_lm)\\n existing_test_ct.append(single_ct)\\n existing_train_data = [existing_train_lm, existing_train_ct]\\n existing_test_data = [existing_test_lm, existing_test_ct]\\n return existing_train_data, existing_test_data\",\n \"def metadata_reporter(self):\\n logging.info('Creating summary report')\\n header = '{}\\\\n'.format(','.join(self.headers))\\n # Create a string to store all the results\\n data = str()\\n for sample in self.metadata:\\n # Add the value of the appropriate attribute to the results string\\n data += GenObject.returnattr(sample, 'name')\\n # SampleName\\n data += GenObject.returnattr(sample.run, 'SamplePlate')\\n # Genus\\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\\n # SamplePurity\\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\\n # N50\\n n50 = GenObject.returnattr(sample.quast, 'N50',\\n number=True)\\n if n50 != '-,':\\n data += n50\\n else:\\n data += '0,'\\n # NumContigs\\n data += GenObject.returnattr(sample.quast, 'num_contigs',\\n number=True)\\n # TotalLength\\n data += GenObject.returnattr(sample.quast, 'Total_length',\\n number=True)\\n # MeanInsertSize\\n data += GenObject.returnattr(sample.quast, 'mean_insert',\\n number=True)\\n # InsertSizeSTD\\n data += GenObject.returnattr(sample.quast, 'std_insert',\\n number=True)\\n # AverageCoverageDepth\\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\\n number=True)\\n # CoverageDepthSTD\\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\\n number=True)\\n # PercentGC\\n data += GenObject.returnattr(sample.quast, 'GC',\\n number=True)\\n # MASH_ReferenceGenome\\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\\n # MASH_NumMatchingHashes\\n data += GenObject.returnattr(sample.mash, 'nummatches')\\n # 16S_result\\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\\n # 16S PercentID\\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\\n # CoreGenesPresent\\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\\n # rMLST_Result\\n try:\\n # If the number of matches to the closest reference profile is 53, return the profile number\\n if sample.rmlst.matches == 53:\\n if type(sample.rmlst.sequencetype) is list:\\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\\n else:\\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\\n data += rmlst_seq_type\\n else:\\n # Otherwise the profile is set to new\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_Result\\n try:\\n if sample.mlst.matches == 7:\\n if type(sample.mlst.sequencetype) is list:\\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\\n else:\\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\\n data += mlst_seq_type\\n else:\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_gene_X_alleles\\n try:\\n # Create a set of all the genes present in the results (gene name split from allele)\\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\\n for gene in sorted(gene_set):\\n allele_list = list()\\n # Determine all the alleles that are present for each gene\\n for allele in sample.mlst.combined_metadata_results:\\n if gene in allele:\\n allele_list.append(allele.replace(' ', '_'))\\n # If there is more than one allele in the sample, add both to the string separated by a ';'\\n if len(allele_list) > 1:\\n data += '{},'.format(';'.join(allele_list))\\n # Otherwise add the only allele\\n else:\\n data += allele_list[0] + ','\\n # If there are fewer than seven matching alleles, add a ND for each missing result\\n if len(gene_set) < 7:\\n data += (7 - len(gene_set)) * 'ND,'\\n except AttributeError:\\n # data += '-,-,-,-,-,-,-,'\\n data += 'ND,ND,ND,ND,ND,ND,ND,'\\n # E_coli_Serotype\\n try:\\n # If no O-type was found, set the output to be O-untypeable\\n if ';'.join(sample.ectyper.o_type) == '-':\\n otype = 'O-untypeable'\\n else:\\n otype = sample.ectyper.o_type\\n # Same as above for the H-type\\n if ';'.join(sample.ectyper.h_type) == '-':\\n htype = 'H-untypeable'\\n\\n else:\\n htype = sample.ectyper.h_type\\n serotype = '{otype}:{htype},'.format(otype=otype,\\n htype=htype)\\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # SISTR_serovar_antigen\\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\\n # SISTR_serovar_cgMLST\\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\\n # SISTR_serogroup\\n data += GenObject.returnattr(sample.sistr, 'serogroup')\\n # SISTR_h1\\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\\n # SISTR_h2\\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\\n # SISTR_serovar\\n data += GenObject.returnattr(sample.sistr, 'serovar')\\n # GeneSeekr_Profile\\n try:\\n if sample.genesippr.report_output:\\n data += ';'.join(sample.genesippr.report_output) + ','\\n else:\\n data += 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # Vtyper_Profile\\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\\n # AMR_Profile and resistant/sensitive status\\n if sample.resfinder_assembled.pipelineresults:\\n # Profile\\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\\n r_set=';'.join(sorted(list(resistance_set))))\\n data += ','\\n # Resistant/Sensitive\\n data += 'Resistant,'\\n else:\\n # Profile\\n data += 'ND,'\\n # Resistant/Sensitive\\n data += 'Sensitive,'\\n # Plasmid Result'\\n if sample.mobrecon.pipelineresults:\\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\\n data += '{plasmid}({details});'.format(plasmid=plasmid,\\n details=details)\\n data += ','\\n else:\\n data += 'ND,'\\n # TotalPredictedGenes\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\\n number=True)\\n # PredictedGenesOver3000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\\n number=True)\\n # PredictedGenesOver1000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\\n number=True)\\n # PredictedGenesOver500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\\n number=True)\\n # PredictedGenesUnder500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\\n number=True)\\n # AssemblyDate\\n data += datetime.now().strftime('%Y-%m-%d') + ','\\n # PipelineVersion\\n data += self.commit + ','\\n # Name of the database used in the analyses\\n data += os.path.split(self.reffilepath)[-1] + ','\\n # Database download date\\n data += self.download_date\\n # Append a new line to the end of the results for this sample\\n data += '\\\\n'\\n # Replace any NA values with ND\\n cleandata = data.replace('NA', 'ND')\\n with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:\\n metadatareport.write(header)\\n metadatareport.write(cleandata)\",\n \"def combine_summary_and_sets(sequence_read_set_file, summary_file):\\n summaries = {}\\n with open(summary_file) as f:\\n for line in f:\\n summary = json.loads(line)\\n sample = summary.pop(\\\"sample\\\")\\n reference_organism = summary.pop(\\\"reference\\\").lower()\\n if summaries.get(sample):\\n if summaries[sample].get(reference_organism):\\n summaries[sample][reference_organism][\\\"summary_stats\\\"] = summary\\n else:\\n summaries[sample].update({reference_organism: {\\n \\\"summary_stats\\\": summary\\n }})\\n else:\\n summaries[sample]={reference_organism: {\\\"summary_stats\\\": summary}}\\n f.close()\\n with open(sequence_read_set_file) as f:\\n for line in f:\\n read_set = json.loads(line)\\n sample = read_set.pop(\\\"sample\\\")\\n read_set[\\\"urls\\\"] = read_set.pop(\\\"urls\\\")\\n if summaries.get(sample):\\n summaries[sample].update({'metadata': read_set})\\n else:\\n continue\\n return summaries\",\n \"def sample_names(self):\\n with open(self.sample_sheet) as sample_sheet:\\n for line in sample_sheet:\\n if 'Sample_ID' in line:\\n for subline in sample_sheet:\\n data = subline.split(',')\\n self.samples.append(data[0])\",\n \"def create_samples(self):\\n for s_id in range(len(self.data[\\\"sample\\\"])):\\n self.samples.add(Sample(s_id, [self.data[key][s_id] for key in self.data.keys() if key not in WRONG_KEYS],\\n self.data[\\\"label\\\"][s_id]))\",\n \"def _prepare_samples(args):\\n if args.galaxy:\\n system_config = args.galaxy\\n else:\\n system_config = os.path.join(_get_data_dir(), \\\"galaxy\\\", \\\"bcbio_system.yaml\\\")\\n config = yaml.load(open(system_config))\\n config['algorithm'] = {}\\n data = []\\n vcf_files = [fn for fn in args.files if fn.endswith('vcf')]\\n bam_files = [fn for fn in args.files if fn.endswith('bam')]\\n fastq_files = [fn for fn in args.files if is_fastq(fn)]\\n if not fastq_files:\\n fastq_files = vcf_files\\n for sample in fastq_files:\\n dt = {}\\n dt['name'] = splitext_plus(op.basename(sample))[0]\\n dt['config'] = config\\n dt['fastq'] = op.abspath(sample)\\n if bam_files:\\n dt['bam'] = _find_bam(bam_files, sample)\\n data.append([dt])\\n return data\",\n \"def collect_metrics(grouped_samples, projroot, tgtdir, ext, grouping=\\\"sample\\\"):\\n metrics = []\\n for item_id, itemlist in grouped_samples.items():\\n item = itemlist[0]\\n # FIXME: tgtdir should be docroot!\\n pfx = os.path.relpath(itemlist[0].prefix(grouping), os.path.dirname(tgtdir))\\n mfile = glob.glob(pfx + \\\".*\\\" + ext)\\n if mfile:\\n metrics.append((item_id, mfile[0]))\\n return PicardMetricsCollection(metrics)\",\n \"def samples(self):\\n if not self._samples:\\n self._samples = defaultdict(list)\\n blobs = self.blobs()\\n sequencing = self._get_entities('sequencing')\\n for s in self._get_entities('sample'):\\n s = sample_factory(s, workspace=self, blobs=blobs, sequencing=sequencing, avro_path=self.avro_path)\\n self._samples[s.subject_id].append(s)\\n if s.missing_sequence:\\n self.missing_sequence = s.missing_sequence\\n return self._samples\",\n \"def samples():\\n f = open(config['samples'], \\\"r\\\")\\n samp=[]\\n for line in f:\\n samp.append(line.strip().split()[0])\\n return samp\",\n \"def _compute_samples(self, samples):\\n return samples\",\n \"def gatherSamplesPerLane(self):\\n if self.verbose > 0:\\n msg = \\\"gather samples per lane...\\\"\\n sys.stdout.write(\\\"%s\\\\n\\\" % msg)\\n sys.stdout.flush()\\n \\n groupJobId = self.makeGroupJobId(\\\"%s_gbs-step4-gather\\\" % self.project2Id)\\n if self.verbose > 0:\\n msg = \\\"groupJobId=%s\\\" % groupJobId\\n sys.stdout.write(\\\"%s\\\\n\\\" % msg)\\n sys.stdout.flush()\\n iJobGroup = JobGroup(groupJobId, self.queue2, self.lResources)\\n self.jobManager.insert(iJobGroup)\\n \\n for laneId,iLane in self.dLanes.items():\\n outDir = \\\"%s/%s\\\" % (self.allSamplesDir, iLane.id)\\n iLane.gather(self.jvmXms, self.jvmXmx, self.tmpDir,\\n outDir, iJobGroup)\\n \\n self.jobManager.submit(iJobGroup.id)\\n self.jobManager.wait(iJobGroup.id, self.rmvBash, self.verbose)\",\n \"def get_samples_file(foldername, filter=None):\\n samples = []\\n for file in os.listdir(foldername):\\n if filter and file.find(filter) == -1:\\n continue\\n for sample in sfile(foldername + '/' + file, None).get_samples():\\n samples.append(sample)\\n return samples\",\n \"def gatherfiles(self):\\n\\t\\tfrom subprocess import Popen,PIPE\\n\\t\\timport os\\n\\t\\timport tarfile\\n\\t\\timport glob\\n\\t\\t\\n\\t\\tprint \\\"=== \\\",self.nameID,\\\": Joining all the files in one\\\"\\n\\t\\t# FIXME: Only there are 1 file, not needed the hadd\\n\\t\\tfinalfile = os.path.join(\\\"Results\\\",self.outputfile)\\n\\t\\t# FIXED BUG: just cp when there is only one file, otherwise\\n\\t\\t# there are problems with the TTree\\n\\t\\tif len(self.outputfiles) == 1:\\n\\t\\t\\t# Note that when there is only 1 file, always its #task=1\\n\\t\\t\\tcommand = [ 'cp', self.outputfiles[1], finalfile ]\\n\\t\\telse:\\n\\t\\t\\tcommand = [ 'haddPlus', finalfile ]\\n\\t\\t\\tfor f in self.outputfiles.itervalues():\\n\\t\\t\\t\\tcommand.append( f )\\n\\t\\tp = Popen( command ,stdout=PIPE,stderr=PIPE ).communicate()\\n\\t\\t# Checking if everything was allright\\n\\t\\ttotalevts = self.getevents(finalfile,True)\\n\\t\\tif totalevts != self.nevents:\\n\\t\\t\\tmessage = \\\"\\\\033[33;1mclustermanager.gatherfiles: WARNING\\\\033[0m the total file\\\"\\n\\t\\t\\tmessage += \\\"'\\\"+finalfile+\\\"' do not contain all the events:\\\\n\\\"\\n\\t\\t\\tmessage += \\\"Total events to be processed:\\\"+str(self.nevents)+\\\"\\\\n\\\"\\n\\t\\t\\tmessage += \\\"Total events in '\\\"+finalfile+\\\"':\\\"+str(totalevts)+\\\"\\\\n\\\"\\n\\t\\t\\tprint message\\n\\t\\t\\treturn \\n\\t\\t# If everything was fine, deleting the files \\n\\t\\t# and cleaning the directory\\n\\t\\tfor f in self.outputfiles.itervalues():\\n\\t\\t\\tos.remove( f )\\n\\t\\t# Taring and compressing\\n\\t\\tfilestotar = glob.glob(\\\"./*.*\\\")\\n\\t\\tfilestotar.append( \\\".storedmanager\\\")\\n\\t\\ttar = tarfile.open(os.path.basename(self.cwd)+\\\".tar.gz\\\",\\\"w:gz\\\")\\n\\t\\tfor f in filestotar:\\n\\t\\t\\ttar.add(f)\\n\\t\\ttar.close()\\n\\t\\t# if everything was fine, deleting the files\\n\\t\\tif os.path.exists(os.path.basename(self.cwd)+\\\".tar.gz\\\"):\\n\\t\\t\\tfor f in filestotar:\\n\\t\\t\\t\\tos.remove(f)\\n\\t\\telse:\\n\\t\\t\\tmessage = \\\"\\\\033[33;1mclustermanager.gatherfiles: WARNING\\\\033[0m I can't manage\\\\n\\\"\\n\\t\\t\\tmessage += \\\"to create the backup .tar.gz file\\\\n\\\"\\n\\t\\t\\tprint message\\n\\n\\t\\tprint \\\"Created \\\"+finalfile\\n\\t\\tprint \\\"========= Process Completed =========\\\"\",\n \"def read_input_files(self):\\r\\n\\r\\n for input_file in self.list_of_input_files:\\r\\n input_file.read_header_of_file()\\r\\n self.list_of_header_objects.extend(input_file.list_of_header_objects)\\r\\n self.list_of_header_objects_without_ID.extend(input_file.list_of_header_objects_without_ID)\\r\\n self.list_of_contigs.extend(input_file.list_of_contigs)\\r\\n\\r\\n self.list_of_header_objects = list(toolz.unique(self.list_of_header_objects, key=lambda x: x.tag_and_ID))\\r\\n self.list_of_header_objects_without_ID = list(\\r\\n toolz.unique(self.list_of_header_objects_without_ID, key=lambda x: x.line))\\r\\n self.list_of_contigs = list(toolz.unique(self.list_of_contigs, key=lambda x: x.line))\\r\\n self.list_of_header_objects.extend(self.list_of_header_objects_without_ID)\\r\\n self.list_of_header_objects.sort(key=lambda x: x.line)\\r\\n self.list_of_header_objects.extend(self.list_of_contigs)\\r\\n self.list_of_header_objects.sort(key=lambda x: x.tag, reverse=False)\\r\\n self.create_body_header_line_for_output()\\r\\n self.write_header_in_output_file()\\r\\n\\r\\n list_of_chrom = list(self.indices.keys())\\r\\n list_of_chrom.sort(key=lambda x: self.alphanum_key(x))\\r\\n for chrom in list_of_chrom:\\r\\n self.list_of_body_objects.clear()\\r\\n for input_file in self.list_of_input_files:\\r\\n input_file.read_specific_chrom_body_of_file(chrom)\\r\\n self.list_of_body_objects.extend(input_file.list_of_body_objects)\\r\\n\\r\\n self.adjust_body_records_to_samples()\\r\\n self.list_of_body_objects = list(toolz.unique(self.list_of_body_objects, key=lambda x: x.line))\\r\\n self.list_of_body_objects.sort(key=lambda x: self.alphanum_key(x.line))\\r\\n self.verify_and_merge_body_records()\\r\\n self.write_specific_chrom_in_output_file()\",\n \"def parse_picardCollect_summary(sample, file):\\n with open(file, 'r') as fh:\\n for l in fh:\\n if l.startswith('#'):\\n continue\\n if l.startswith('PF_BASES'):\\n parsed = l\\n parsed += next(fh)\\n break\\n\\n if len(parsed) == 0:\\n return None\\n else:\\n df = pd.read_csv(StringIO(parsed), sep='\\\\t')\\n df.index = [sample]\\n return df\",\n \"def concatenate(self, use_trim=False, top_db=None):\\n spkr_num = len(self.raw_data_dir)\\n print(\\\"No.speakers : %d\\\" % spkr_num)\\n f_info = open(os.path.join(self.out_data_dir, 'info.pkl'), 'wb') # f to write down information of processed data\\n info = []\\n f_setting = open(os.path.join(self.out_data_dir, 'setting.txt'), 'wt') # f to write down settings of processing pipe\\n\\n # Concatenating......\\n print(\\\"Concatenation begins...\\\")\\n if use_trim:\\n print(\\\"Removing nonspeech samples is activated.\\\")\\n for i, folder in enumerate(self.raw_data_dir):\\n spkr_id = self.get_spkr_name(folder_name=folder)\\n print(\\\"%d: %sth speaker processing...\\\" % (i, spkr_id))\\n utter_all = []\\n for root, _, utter_names in os.walk(folder):\\n if len(utter_names) > 0:\\n utter_names = list(filter(lambda x: x.endswith(self.file_suffix), utter_names))\\n for utter_name in utter_names:\\n utter_path = os.path.join(root, utter_name) # absolute path of each utterance\\n utter = self.load_audio(utter_path)\\n utter_all.append(utter)\\n utter_all = np.concatenate(utter_all, axis=0)\\n\\n if use_trim:\\n utter_trim = []\\n intervals = librosa.effects.split(utter_all, top_db=top_db)\\n for interval in intervals:\\n utter_trim.append(utter_all[interval[0]: interval[1]])\\n utter_trim = np.concatenate(utter_trim, axis=0)\\n sf.write(os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_trim, self.sr)\\n info.append((os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_trim.shape[0], i))\\n else:\\n sf.write(os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_all, self.sr)\\n info.append((os.path.join(self.out_data_dir, self.name_ptn(spkr_id=spkr_id)), utter_all.shape[0], i))\\n # Concatenating succeed\\n\\n print(\\\"Writing necessary files...\\\")\\n pickle.dump(info, f_info)\\n f_info.close()\\n\\n info = sorted(info, key=lambda x: x[1])\\n print(\\\"Speaker number: {}\\\".format(spkr_num), file=f_setting)\\n print(\\\"Minimum length: {}\\\\t{}\\\".format(info[0][0], info[0][1]), file=f_setting)\\n print(\\\"Maximum length: {}\\\\t{}\\\".format(info[-1][0], info[-1][1]), file=f_setting)\\n print(\\\"sr: {}\\\".format(self.sr), file=f_setting)\\n print(\\\"Use trim: {}\\\".format(use_trim), file=f_setting)\\n print(\\\"top_db: {}\\\".format(top_db), file=f_setting)\\n f_setting.close()\",\n \"def format_preparation_files(run_dir, sample_sheet, output_dir, pipeline,\\n verbose):\\n sample_sheet = KLSampleSheet(sample_sheet)\\n df_sheet = sample_sheet_to_dataframe(sample_sheet)\\n\\n if pipeline == 'atropos-and-bowtie2':\\n click.echo('Stats collection is not supported for pipeline '\\n 'atropos-and-bowtie2')\\n else:\\n stats = run_counts(run_dir, sample_sheet)\\n\\n stats['sample_name'] = \\\\\\n df_sheet.set_index('lane', append=True)['sample_name']\\n\\n # returns a map of (run, project_name, lane) -> preparation frame\\n preps = preparations_for_run(run_dir, df_sheet, pipeline=pipeline)\\n\\n os.makedirs(output_dir, exist_ok=True)\\n\\n for (run, project, lane), df in preps.items():\\n fp = os.path.join(output_dir, f'{run}.{project}.{lane}.tsv')\\n\\n if pipeline == 'fastp-and-minimap2':\\n # stats are indexed by sample name and lane, lane is the first\\n # level index. When merging, make sure to select the lane subset\\n # that we care about, otherwise we'll end up with repeated rows\\n df = df.merge(stats.xs(lane, level=1), how='left',\\n on='sample_name')\\n\\n # strip qiita_id from project names in sample_project column\\n df['sample_project'] = df['sample_project'].map(\\n lambda x: re.sub(r'_\\\\d+$', r'', x))\\n\\n # center_project_name is a legacy column that should mirror\\n # the values for sample_project.\\n df['center_project_name'] = df['sample_project']\\n\\n df.to_csv(fp, sep='\\\\t', index=False)\\n\\n if verbose:\\n project_name = remove_qiita_id(project)\\n # assume qiita_id is extractable and is an integer, given that\\n # we have already passed error-checking.\\n qiita_id = project.replace(project_name + '_', '')\\n print(\\\"%s\\\\t%s\\\" % (qiita_id, abspath(fp)))\",\n \"def aggregate_bam_files(wildcards):\\n\\tsample_name, prep_type, seq_mode, abundance_control, sample_type, seq_type = get_sample_info(wildcards)\\n\\n\\tif \\\"2x\\\" in seq_mode and seq_type == \\\"bisulfite\\\":\\n\\t\\tbam = 'sample_output/pe_bisulfite_aligned/raw_aligned/{sample}.bam'\\n\\t\\tmapped_all_chr = 'sample_output/pe_bisulfite_aligned/all_chr/{sample}_mapped_all_chr.bam'\\n\\t\\tmapped_all_chr_bai = 'sample_output/pe_bisulfite_aligned/all_chr/{sample}_mapped_all_chr.bam.bai'\\n\\t\\tunmapped_R1 = 'sample_output/pe_bisulfite_aligned/unmapped/{sample}_pe_unmapped_R1.fastq.gz'\\n\\t\\tunmapped_R2 = 'sample_output/pe_bisulfite_aligned/unmapped/{sample}_pe_unmapped_R2.fastq.gz'\\n\\n\\tif \\\"2x\\\" in seq_mode and seq_type == \\\"standard\\\":\\n\\t\\tbam = 'sample_output/pe_stdseq_aligned/raw_aligned/{sample}.bam'\\n\\t\\tmapped_all_chr = 'sample_output/pe_stdseq_aligned/all_chr/{sample}_mapped_all_chr.bam'\\n\\t\\tmapped_all_chr_bai = 'sample_output/pe_stdseq_aligned/all_chr/{sample}_mapped_all_chr.bam.bai'\\n\\t\\tunmapped_R1 = 'sample_output/pe_stdseq_aligned/unmapped/{sample}_pe_unmapped_R1.fastq.gz'\\n\\t\\tunmapped_R2 = 'sample_output/pe_stdseq_aligned/unmapped/{sample}_pe_unmapped_R2.fastq.gz'\\n\\n\\treturn[bam, mapped_all_chr, mapped_all_chr_bai, unmapped_R1, unmapped_R2]\",\n \"def _minimal_export_traces(self, outdir=None, analytes=None,\\n samples=None, subset=None):\\n if analytes is None:\\n analytes = self.analytes\\n elif isinstance(analytes, str):\\n analytes = [analytes]\\n\\n if samples is not None:\\n subset = self.make_subset(samples)\\n elif not hasattr(self, 'subsets'):\\n self.make_subset()\\n\\n if subset is None:\\n samples = self.subsets['All_Analyses']\\n else:\\n try:\\n samples = self.subsets[subset]\\n except:\\n raise ValueError((\\\"Subset '{:s}' does not .\\\".format(subset) +\\n \\\"exist.\\\\nRun 'make_subset' to create a\\\" +\\n \\\"subset.\\\"))\\n\\n focus_stage = 'rawdata'\\n ud = 'counts'\\n\\n if not os.path.isdir(outdir):\\n os.mkdir(outdir)\\n\\n for s in samples:\\n d = self.data_dict[s].data[focus_stage]\\n out = {}\\n\\n for a in analytes:\\n out[a] = d[a]\\n\\n out = pd.DataFrame(out, index=self.data_dict[s].Time)\\n out.index.name = 'Time'\\n\\n header = ['# Minimal Reproduction Dataset Exported from LATOOLS on %s' %\\n (time.strftime('%Y:%m:%d %H:%M:%S')),\\n \\\"# Analysis described in '../analysis.log'\\\",\\n '# Run latools.reproduce to import analysis.',\\n '#',\\n '# Sample: %s' % (s),\\n '# Analysis Time: ' + self.data_dict[s].meta['date']]\\n\\n header = '\\\\n'.join(header) + '\\\\n'\\n\\n csv = out.to_csv()\\n\\n with open('%s/%s.csv' % (outdir, s), 'w') as f:\\n f.write(header)\\n f.write(csv)\\n return\",\n \"def sampling():\\n # make directory for street images\\n streetImageOutputFolder = CONFIG[\\\"sampling\\\"][\\\"streetImageOutputFolder\\\"]\\n makeDirectory(streetImageOutputFolder)\\n\\n # Get preprocessed point data\\n intersectionPointFile = CONFIG[\\\"shapefile\\\"][\\\"intersectoinPointFile\\\"]\\n pointInfoFile = CONFIG[\\\"shapefile\\\"][\\\"pointInfoFilename\\\"]\\n\\n pointInfo = readPointFile(pointInfoFile)\\n intersectionPointInfo = readIntersectionPointInfo(intersectionPointFile)\\n\\n # Filter point data that has street images taken within the specified period.\\n maxYear = CONFIG[\\\"gmap\\\"][\\\"streetImageMaxYear\\\"]\\n minYear = CONFIG[\\\"gmap\\\"][\\\"streetImageMinYear\\\"]\\n filteredPoints = filterPointByYear(pointInfo, maxYear, minYear)\\n\\n IMG_NAME_COL_NUM = 5\\n LAT_LNG_COL_NUM = 2\\n\\n # Sample street images, the return is list of sample info\\n sampleNum = CONFIG[\\\"sampling\\\"][\\\"sampleNum\\\"]\\n initImageNumber = CONFIG[\\\"sampling\\\"][\\\"initImageNumber\\\"]\\n sampleData = sampleAndDownloadStreetImage(filteredPoints, sampleNum, initImageNumber, initImageNumber, streetImageOutputFolder, intersectionPointInfo)\\n imageNames = [streetImageOutputFolder + \\\"/\\\" + data[IMG_NAME_COL_NUM] for data in sampleData]\\n links = GDriveUpload(imageNames, \\\"Sampled_Image\\\")\\n\\n for i in xrange(len(sampleData)):\\n imageName = streetImageOutputFolder + \\\"/\\\" + sampleData[i][IMG_NAME_COL_NUM]\\n sampleData[i].append(links[imageName])\\n\\n columnTitle = [\\\"Sample Number\\\", \\\"Sampled Point Number\\\", \\\"Latitude + Longitude\\\", \\\"Heading\\\", \\\"Date\\\", \\\"Image Name\\\", \\\"Road Types\\\", \\\"Web Link\\\"]\\n sampleData.insert(0, columnTitle)\\n\\n # output to csv file\\n outputCSV(sampleData, CONFIG[\\\"sampling\\\"][\\\"csvFilename\\\"])\\n\\n # plot images map\\n sampledPoints = set([divideGPS(d[LAT_LNG_COL_NUM]) for d in sampleData[1:]])\\n plotSampledPointMap(list(sampledPoints), CONFIG[\\\"sampling\\\"][\\\"sampledPointsMapFilename\\\"])\",\n \"def collect_samples(self):\\n self.__running = True\\n with open(self.__filename, 'a') as output:\\n next_sample_time = time.time()\\n while self.__running:\\n sensor_name = self.__sensor.get_sensor_type_name()\\n sensor_id = self.__sensor.get_sensor_id()\\n data = self.__sensor.retrieve_data_string() \\n if DEBUG:\\n print('data: \\\"{}\\\"'.format(data),\\n file = sys.stderr, flush=True)\\n when = datetime.datetime.now(datetime.timezone.utc).isoformat()\\n result = OUTPUT_FORMAT.format(when,\\n sensor_name, \\n sensor_id, \\n data)\\n output.write(result)\\n output.flush()\\n \\n next_sample_time = next_sample_time + self.__interval\\n delay_time = next_sample_time - time.time()\\n if DEBUG:\\n print('delay_time = {}'.format(delay_time),\\n file=sys.stderr, flush=True)\\n \\n if 0 < delay_time: # don't sleep if already next sample time\\n time.sleep(delay_time)\",\n \"def addSamples(file, spec, data_card, channel_bins, samples, shapefile):\\n data_card.processes = samples\\n\\n for idxc, channel in enumerate(spec[\\\"channels\\\"]):\\n if data_card.hasShapes == False: # counting\\n for sample in channel[\\\"samples\\\"]:\\n data_card.exp[channel[\\\"name\\\"]].update(\\n {sample[\\\"name\\\"]: sample[\\\"data\\\"][0]}\\n )\\n else:\\n # shapes\\n data_card.shapeMap.update({channel[\\\"name\\\"]: {}})\\n data_card.shapeMap[channel[\\\"name\\\"]].update(\\n {\\\"data_obs\\\": [shapefile, channel[\\\"name\\\"] + \\\"/\\\" + \\\"data_obs\\\"]}\\n )\\n for idxs, sample in enumerate(channel[\\\"samples\\\"]):\\n data = sum(sample[\\\"data\\\"])\\n data_card.exp[channel[\\\"name\\\"]].update({sample[\\\"name\\\"]: data})\\n\\n mods = [\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"modifiers\\\"][i][\\\"type\\\"]\\n for i, mod in enumerate(\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"modifiers\\\"]\\n )\\n ]\\n if \\\"histosys\\\" in mods:\\n # if shape uncertainty histogram is available\\n data_card.shapeMap[channel[\\\"name\\\"]].update(\\n {\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"name\\\"]: [\\n shapefile,\\n channel[\\\"name\\\"]\\n + \\\"/\\\"\\n + spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"name\\\"],\\n channel[\\\"name\\\"]\\n + \\\"/\\\"\\n + spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"name\\\"]\\n + \\\"_$SYSTEMATIC\\\",\\n ]\\n }\\n )\\n else:\\n data_card.shapeMap[channel[\\\"name\\\"]].update(\\n {\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"name\\\"]: [\\n shapefile,\\n channel[\\\"name\\\"]\\n + \\\"/\\\"\\n + spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"name\\\"],\\n ]\\n }\\n )\\n if \\\"staterror\\\" in mods:\\n # if staterror is present, provide variances for histograms\\n h_data = hist.Hist.new.Regular(\\n channel_bins[channel[\\\"name\\\"]], 0, channel_bins[channel[\\\"name\\\"]]\\n ).Weight()\\n\\n h_data[...] = np.stack(\\n [\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"data\\\"],\\n [\\n i**2\\n for i in spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\n \\\"modifiers\\\"\\n ][mods.index(\\\"staterror\\\")][\\\"data\\\"]\\n ],\\n ],\\n axis=-1,\\n )\\n data_card.binParFlags.update({channel[\\\"name\\\"]: True})\\n elif \\\"shapesys\\\" in mods: # for shapesys, also provide variances\\n h_data = hist.Hist.new.Regular(\\n channel_bins[channel[\\\"name\\\"]], 0, channel_bins[channel[\\\"name\\\"]]\\n ).Weight()\\n h_data[...] = np.stack(\\n [\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"data\\\"],\\n [\\n i**2\\n for i in spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\n \\\"modifiers\\\"\\n ][mods.index(\\\"shapesys\\\")][\\\"data\\\"]\\n ],\\n ],\\n axis=-1,\\n )\\n data_card.binParFlags.update({channel[\\\"name\\\"]: True})\\n else: # no staterror or shapesys->0 variance\\n h_data = hist.Hist.new.Regular(\\n channel_bins[channel[\\\"name\\\"]], 0, channel_bins[channel[\\\"name\\\"]]\\n ).Weight()\\n h_data[...] = np.stack(\\n [\\n spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"data\\\"],\\n [0 for _ in range(channel_bins[channel[\\\"name\\\"]])],\\n ],\\n axis=-1,\\n )\\n file[\\n channel[\\\"name\\\"]\\n + \\\"/\\\"\\n + spec[\\\"channels\\\"][idxc][\\\"samples\\\"][idxs][\\\"name\\\"]\\n ] = h_data\",\n \"def create_samples(self):\\n self._samples = self.load_samples()\\n self.modify_samples()\",\n \"def collect_outputs(self):\\n # collect submex output hdf urls and add them to top mex outputs section\\n top_mex = self.bqSession.fetchxml(self.options.mexURL, view='deep')\\n outputTag = top_mex.xpath('/mex/tag[@name=\\\"outputs\\\"]')\\n if not outputTag:\\n # no \\\"outputs\\\" tag in mex => add it now\\n etree.SubElement(top_mex, 'tag', name='outputs') \\n top_mex = self.bqSession.postxml(url=top_mex.get('uri'), xml=top_mex, view='deep')\\n outputTag = top_mex.xpath('/mex/tag[@name=\\\"outputs\\\"]')\\n outputTag = outputTag[0]\\n output_hdfs = top_mex.xpath('/mex/mex/tag[@name=\\\"outputs\\\"]/tag[@name=\\\"output_hdf\\\"]/@value')\\n etree.SubElement(outputTag, 'tag', name='all_outputs', value=';'.join([ohdf.split('/')[-1] for ohdf in output_hdfs]))\\n self.bqSession.postxml(url=outputTag.get('uri'), xml=outputTag)\",\n \"def main():\\n dir_path='.'\\n meas_file='magic_measurements.txt'\\n samp_file=\\\"er_samples.txt\\\"\\n out_file='magic_measurements.txt'\\n if '-h' in sys.argv:\\n print(main.__doc__)\\n sys.exit()\\n if '-WD' in sys.argv:\\n ind = sys.argv.index('-WD')\\n dir_path=sys.argv[ind+1]\\n if '-f' in sys.argv:\\n ind = sys.argv.index('-f')\\n meas_file=sys.argv[ind+1]\\n if '-fsa' in sys.argv:\\n ind = sys.argv.index('-fsa')\\n samp_file=sys.argv[ind+1]\\n if '-F' in sys.argv:\\n ind = sys.argv.index('-F')\\n out_file=sys.argv[ind+1]\\n # read in measurements file\\n meas_file=dir_path+'/'+meas_file\\n out_file=dir_path+'/'+out_file\\n samp_file=dir_path+'/'+samp_file\\n data,file_type=pmag.magic_read(meas_file)\\n samps,file_type=pmag.magic_read(samp_file)\\n MeasRecs=[]\\n sampnames,sflag=[],0\\n for rec in data:\\n for samp in samps:\\n if samp['er_sample_name'].lower()==rec['er_sample_name'].lower():\\n if samp['er_sample_name'] not in sampnames:sampnames.append(samp['er_sample_name'].lower())\\n rec['er_site_name']=samp['er_site_name']\\n rec['er_location_name']=samp['er_location_name']\\n MeasRecs.append(rec)\\n break\\n if rec['er_sample_name'].lower() not in sampnames:\\n sampnames.append(rec['er_sample_name'].lower())\\n sflag=1\\n SampRec={}\\n for key in list(samps[0].keys()):SampRec[key]=\\\"\\\"\\n SampRec['er_sample_name']=rec['er_sample_name']\\n SampRec['er_citation_names']=\\\"This study\\\"\\n SampRec['er_site_name']='MISSING'\\n SampRec['er_location_name']='MISSING'\\n SampRec['sample_desription']='recorded added by update_measurements - edit as needed'\\n samps.append(SampRec)\\n print(rec['er_sample_name'],' missing from er_samples.txt file - edit orient.txt file and re-import')\\n rec['er_site_name']='MISSING'\\n rec['er_location_name']='MISSING'\\n MeasRecs.append(rec)\\n pmag.magic_write(out_file,MeasRecs,'magic_measurements')\\n print(\\\"updated measurements file stored in \\\", out_file)\\n if sflag==1:\\n pmag.magic_write(samp_file,samps,'er_samples')\\n print(\\\"updated sample file stored in \\\", samp_file)\",\n \"def finalize(param, input_files='count_files'):\\n\\n import csv\\n HELPER.writeLog('Collecting featureCount raw counts ... \\\\n', param)\\n\\n #check which of these files are actually available\\n working_files = [iFile for iFile in param[input_files] if iFile != '']\\n\\n if len(working_files) > 0:\\n #get feature ID using the first column in the first file in the list of working files\\n csv_file = open(working_files[0])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n\\n #For featureCount output, we want to skip the first two lines as they\\n #include the featureCount call and the headers which we don't want\\n next(csv_reader, None)\\n next(csv_reader, None)\\n\\n #Now start by taking the list of identifier,\\n #which is the first column in the file\\n counts = [row[0] for row in csv_reader]\\n csv_file.close()\\n\\n #get all the expression values\\n header = 'ID'\\n for idx in range(param['num_samples']):\\n if param[input_files] != '':\\n header = header+'\\\\t'+param['stub'][idx]\\n csv_file = open(param[input_files][idx])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n\\n #Here too we want to skip the first two lines, before getting the counts\\n next(csv_reader, None)\\n next(csv_reader, None)\\n #Now start getting the counts (row[6]) and add in the ID (counts[i]) before it\\n idx = 0\\n for row in csv_reader:\\n counts[idx] = counts[idx]+'\\\\t'+row[6]\\n idx += 1\\n csv_file.close()\\n\\n #output the file\\n out_file = param['working_dir']+'deliverables/featureCount_raw_counts.txt'\\n out_handle = open(out_file, 'w')\\n out_handle.write(header+'\\\\n')\\n\\n for i in range(len(counts)):\\n out_handle.write(counts[i]+'\\\\n')\\n out_handle.close()\\n\\n #output_phenotype_file\\n HELPER.writeLog('Writing phenotype data ... \\\\n', param)\\n MODULE_HELPER.output_sample_info(param)\\n\\n #write summary stats\\n #featureCount does this on its own so we can just fetch each summary file\\n #check which of these files are actually available\\n working_files = [iFile+'.summary' for iFile in param[input_files] if iFile != '']\\n\\n if len(working_files) > 0:\\n #get Status column from summary file using the first column in\\n #the first file in the list of working files\\n csv_file = open(working_files[0])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n #Here, we want to skip the first line, as it simply points to the\\n #alignment file used when running featureCount\\n next(csv_reader, None)\\n\\n #Now start by taking the list of identifier,\\n #which is the first column in the file\\n entry = [row[0] for row in csv_reader]\\n csv_file.close()\\n\\n #get all the summary stats for each sample\\n header = 'Status'\\n\\n for idx in range(param['num_samples']):\\n if param[input_files] != '':\\n header = header+'\\\\t'+param['stub'][idx]\\n #Fetch the corresponding sample's summary file\\n csv_file = open(param[input_files][idx]+'.summary')\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n #Again, we want to skip the first line, as it simply points\\n #to the alignment file used when running featureCount\\n next(csv_reader, None)\\n\\n #Now start getting the stats (row[1]) and add in the Status\\n # (counts[i]) before it\\n i = 0\\n for row in csv_reader:\\n entry[i] = entry[i]+'\\\\t'+row[1]\\n i += 1\\n csv_file.close()\\n #output the file\\n out_handle = open(param['working_dir']+\\n 'results/featureCount/featureCount_stats.txt',\\n 'w')\\n out_handle.write(header+'\\\\n')\\n\\n for i in range(len(entry)):\\n out_handle.write(entry[i]+'\\\\n')\\n out_handle.close()\\n else:\\n print 'featureCount was not run successfully on any of the files..\\\\n'\",\n \"def generate_data():\\n for subdir, dirs, files in os.walk(legend_images_dir):\\n for _file in files:\\n getTables(_file)\\n\\n file_list = []\\n for subdir, dirs, files in os.walk(pdf_output_dir):\\n for _file in files:\\n if _file.endswith('.pdf'):\\n file_list.append(_file)\\n\\n print (\\\"Writing merged output in Output.pdf...\\\")\\n current_dir = os.getcwd()\\n mergeOutput(file_list, current_dir + \\\"/Output.pdf\\\")\\n\\n clean()\",\n \"def get_sr_series(tables, out_name, max_sample=500):\\n\\n pt_ct = 0\\n for year in YEARS:\\n for state in TARGET_STATES:\\n\\n name_prefix = '{}_{}_{}'.format(out_name, state, year)\\n local_file = os.path.join('/home/dgketchum/IrrigationGIS/EE_extracts/to_concatenate',\\n '{}.csv'.format(name_prefix))\\n if os.path.isfile(local_file):\\n continue\\n else:\\n print(local_file)\\n\\n roi = ee.FeatureCollection(os.path.join(BOUNDARIES, state))\\n\\n start = '{}-01-01'.format(year)\\n d = datetime.strptime(start, '%Y-%m-%d')\\n epoch = datetime.utcfromtimestamp(0)\\n start_millisec = str(int((d - epoch).total_seconds() * 1000))\\n\\n table = ee.FeatureCollection(tables)\\n table = table.filter(ee.Filter.eq('YEAR', start_millisec))\\n table = table.filterBounds(roi)\\n table = table.randomColumn('rnd')\\n points = table.size().getInfo()\\n print('{} {} {} points'.format(state, year, points))\\n\\n n_splits = int(ceil(points / float(max_sample)))\\n ranges = linspace(0, 1, n_splits + 1)\\n diff = ranges[1] - ranges[0]\\n\\n for enum, slice in enumerate(ranges[:-1], start=1):\\n slice_table = table.filter(ee.Filter.And(ee.Filter.gte('rnd', slice),\\n ee.Filter.lt('rnd', slice + diff)))\\n points = slice_table.size().getInfo()\\n print('{} {} {} points'.format(state, year, points))\\n\\n name_prefix = '{}_{}_{}_{}'.format(out_name, state, enum, year)\\n local_file = os.path.join('/home/dgketchum/IrrigationGIS/EE_extracts/to_concatenate',\\n '{}.csv'.format(name_prefix))\\n if os.path.isfile(local_file):\\n continue\\n else:\\n print(local_file)\\n\\n pt_ct += points\\n if points == 0:\\n continue\\n\\n ls_sr_masked = daily_landsat(year, roi)\\n stats = ls_sr_masked.sampleRegions(collection=table,\\n properties=['POINT_TYPE', 'YEAR', 'LAT_GCS', 'Lon_GCS'],\\n scale=30,\\n tileScale=16)\\n\\n task = ee.batch.Export.table.toCloudStorage(\\n stats,\\n description=name_prefix,\\n bucket='wudr',\\n fileNamePrefix=name_prefix,\\n fileFormat='CSV')\\n\\n task.start()\\n print('{} total points'.format(pt_ct))\",\n \"def preprocess(self):\\n \\n file_name_list = os.listdir(self.image_dir)\\n random.seed(1234)\\n random.shuffle(file_name_list)\\n \\n for i,d in enumerate(self.domains):\\n self.attr2idx[d]=i \\n\\n for i, file_name in enumerate(file_name_list):\\n if (file_name.startswith('X_')):\\n continue\\n \\n parts = file_name.split(\\\"-\\\")\\n label = int(parts[0])\\n if label not in self.domains:\\n continue\\n img_name = file_name\\n\\n count=self.get_sample_count(label)\\n if count<self.valid_set_size:\\n # create holdout set on the fly\\n utils.copy_file(self.image_dir,self.valid_set_dir,img_name)\\n else:\\n self.dataset.append([img_name, self.attr2idx[label]])\\n \\n self.increment_sample_count(label)\\n\\n print(\\\"Sample count per domain: \\\"+str(self.sample_count)+\\\" (including holdout set, holdout size per domain is: \\\"+str(self.valid_set_size)+\\\")\\\")\\n print('Finished preprocessing the dataset...')\",\n \"def get_additional_data_from_files(df, file_description): # file description one of [\\\"video\\\", \\\"eaf\\\", \\\"seg\\\", \\\"gentle\\\"]\\n if file_description == \\\"gentle\\\":\\n file_folder = FILE_BASE + \\\"/gentle/\\\"\\n is_gentle_file = True\\n else:\\n file_folder = FILE_BASE + \\\"/original/\\\"\\n is_gentle_file = False\\n\\n file_df = None\\n\\n if file_description not in list(FILE_DESCRIPTIONS_TO_EXT.keys()):\\n print(\\\"Unknown file description! Don't know what to do with %s files...\\\" % file_description)\\n return None\\n\\n else:\\n print(\\\"Load and extract information from %s files...\\\" % file_description)\\n #pbar = tqdm.tqdm(total = len(np.unique(df[\\\"source_file\\\"])),desc='Files', position=0,leave=True,file=sys.stdout)\\n #file_log = tqdm.tqdm(total=0, position=1, bar_format='{desc}',leave=True,file=sys.stdout)\\n print(\\\"Total files to laod and preprocess: \\\", len(np.unique(df[\\\"source_file\\\"])))\\n \\n for i,file in enumerate(np.unique(df[\\\"source_file\\\"])):\\n if i%100 == 0:\\n print(\\\"File: \\\",i)\\n \\n filepath = file_folder + get_file_path(file,is_gentle_file=is_gentle_file) + FILE_DESCRIPTIONS_TO_EXT[file_description]\\n\\n if file_description == \\\"video\\\":\\n file_i_df = mp4_file_processing.get_word_video_snippet_size(df, filepath)\\n elif file_description == \\\"eaf\\\":\\n speech_annotation_eaf_data, gesture_eaf_data = eaf_file_processing.read_eaf(filepath)\\n file_i_df = eaf_file_processing.map_gestures_to_annotation(speech_annotation_eaf_data, gesture_eaf_data, remove_pauses=False)\\n file_i_df = eaf_file_processing.binary_encode_gestures(file_i_df, gesture_column=\\\"gesture\\\")\\n\\n elif file_description == \\\"seg\\\":\\n file_i_df = seg_file_processing.get_seg_file_pos_info(filepath)\\n\\n elif file_description == \\\"gentle\\\":\\n file_i_df = gentle_file_processing.get_gentle_file_transcripts(filepath)\\n \\n else:\\n print(\\\"Unknown file format!!!\\\")\\n return \\n\\n if file_df is None:\\n file_df = file_i_df\\n else:\\n file_df = pd.concat([file_df, file_i_df], ignore_index=True)\\n\\n #file_log.set_description_str(f'Processed file: {file}')\\n #pbar.update(1)\\n #sleep(0.02)\\n #file_log.close()\\n #pbar.close()\\n return file_df\",\n \"def make_stats_files(sample_dc, otu_dc, degree_counts, num_con_cat, num_con,\\r\\n num_cat, cat_by_sample, dir_path):\\r\\n output = open(os.path.join(dir_path,\\r\\n \\\"stats/real_dc_sample_degree.txt\\\"), 'w')\\r\\n sample_dc_out = sorted(sample_dc.items())\\r\\n sample_dc_str = '\\\\n'.join(['\\\\t'.join(map(str, t)) for t in sample_dc_out])\\r\\n output.write(''.join([\\\"# Just Sample degree counts\\\\n\\\",\\r\\n \\\"Degree\\tSample Count\\\\n\\\", sample_dc_str]))\\r\\n output.close()\\r\\n\\r\\n output = open(os.path.join(dir_path,\\r\\n \\\"stats/real_dc_otu_degree.txt\\\"), 'w')\\r\\n otu_dc_out = sorted(otu_dc.items())\\r\\n otu_dc_str = '\\\\n'.join(['\\\\t'.join(map(str, t)) for t in otu_dc_out])\\r\\n output.write(''.join([\\\"# Just OTU degree counts\\\\n\\\",\\r\\n \\\"Degree\\tOTU Count\\\\n\\\", otu_dc_str]))\\r\\n output.close()\\r\\n\\r\\n output = open(os.path.join(dir_path,\\r\\n \\\"stats/real_dc_sample_otu_degree.txt\\\"), 'w')\\r\\n dc_out = sorted(degree_counts.items())\\r\\n dc_str = '\\\\n'.join(['\\\\t'.join(map(str, t)) for t in dc_out])\\r\\n output.write(''.join([\\\"# Sample and OTU degree counts\\\\n\\\",\\r\\n \\\"Degree\\tBoth Count \\\\n\\\", dc_str]))\\r\\n output.close()\\r\\n\\r\\n num_pairs = len(cat_by_sample) * (len(cat_by_sample) - 1) / 2\\r\\n\\r\\n num_pairs_line = \\\"NUM PAIRS: %s\\\" % str(num_pairs)\\r\\n num_cat_pairs_line = \\\"NUM SAME CAT PAIRS: %s\\\"\\r\\n num_con_pairs_line = \\\"NUM CONNECTED PAIRS: %s\\\" % int(num_con)\\r\\n\\r\\n for cat, num in num_con_cat.items():\\r\\n filename = \\\"stats/real_cat_stats_%s.txt\\\" % cat\\r\\n output = open(os.path.join(dir_path, filename), 'w')\\r\\n num_neither = int((num_pairs - num_con) - (num_cat[cat] - num))\\r\\n stats_line = ''.join(['(', str(int(num)), ', ', str(int(num_con - num)),\\r\\n ', ', str(int(num_cat[cat] - num)), ', ',\\r\\n str(num_neither), ')'])\\r\\n G_stat = G_2_by_2(int(num), int(num_con - num),\\r\\n int(num_cat[cat] - num), num_neither)\\r\\n output.write(\\r\\n '\\\\n'.join([num_pairs_line,\\r\\n num_cat_pairs_line % num_cat[\\r\\n cat],\\r\\n num_con_pairs_line,\\r\\n stats_line,\\r\\n str(G_stat)]))\\r\\n output.close()\",\n \"def test_parallel_dataflow():\\n\\n if os.path.exists('all.txt'):\\n os.remove('all.txt')\\n\\n # create 5 files with random numbers\\n output_files = []\\n for i in range(5):\\n if os.path.exists('random-%s.txt' % i):\\n os.remove('random-%s.txt' % i)\\n output_files.append(generate(outputs=[File('random-%s.txt' % i)]))\\n\\n # concatenate the files into a single file\\n cc = concat(inputs=[i.outputs[0]\\n for i in output_files], outputs=[File(\\\"all.txt\\\")])\\n\\n # calculate the average of the random numbers\\n totals = total(inputs=[cc.outputs[0]])\\n print(totals.result())\",\n \"def _generate_samples(samples_data_table=None):\\n samples_data_table = samples_data_table or dict()\\n\\n con_name = f\\\"Auto_Sample_Test_{datetime.now()}\\\"\\n con_result_soup = BeautifulSoup(_post_con(con_name), \\\"xml\\\")\\n con_uri = con_result_soup.find(\\\"con:container\\\")[\\\"uri\\\"]\\n\\n sample_list = list()\\n for i in range(1, 97, 2):\\n well = (\\n 'ABCDEFGH'[(i - 1) % 8] + ':' + '%01d' % ((i - 1) // 8 + 1,))\\n letter = 'ABCDEFGH'[i % 8]\\n to_add = api_types.Sample(f\\\"test{i}{letter}\\\")\\n to_add.location = well\\n to_add.con = api_types.Container(\\n con_name,\\n \\\"96 well plate\\\",\\n \\\"\\\",\\n con_uri)\\n\\n for data_name, data_value in samples_data_table.items():\\n if \\\"udf\\\" in data_name:\\n udf_name = data_name.strip(\\\"udf_\\\")\\n to_add.udf_to_value[udf_name] = data_value\\n elif \\\"adapter\\\" in data_name:\\n to_add.adapter = data_value\\n sample_list.append(to_add)\\n return sample_list\",\n \"def aggregate_results(output_files, agg_filename):\\n\\n print(file_marker + \\\"STARTING AGGREGATION\\\")\\n feather_files = output_files\\n\\n results = []\\n for i in range(len(feather_files)):\\n print(file_marker + str(i))\\n x = pd.read_feather(feather_files[i])\\n results.append(x)\\n \\n overall_results = pd.concat(results, ignore_index=True, sort=False)\\n opt_diff_results = overall_results\\n\\n opt_diff_results.reset_index(inplace=True, drop=True) \\n # drop=True: column 'index' gets removed\\n\\n opt_diff_results.to_feather(agg_filename)\\n print(file_marker + \\\"Aggregated results saved to: \\\" + agg_filename)\",\n \"def constructReadCountsStr(samplesList, resultsPath, outRoot, stype):\\n files = []\\n for i in range(len(samplesList)):\\n files.append(os.path.join(resultsPath, outRoot) + \\\".\\\" + stype + \\\".\\\" + str(i) + \\\".pruned.te-counts.txt\\\")\\n return \\\",\\\".join(files)\",\n \"def get_result_files(self):\\n name_pattern = \\\"{mapper}.{ngs_library.name}\\\"\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"out\\\", name_pattern + \\\"{ext}\\\"), ext=EXT_VALUES\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"log\\\", \\\"{mapper}.{ngs_library.name}.{ext}\\\"),\\n ext=(\\n \\\"log\\\",\\n \\\"conda_info.txt\\\",\\n \\\"conda_list.txt\\\",\\n \\\"log.md5\\\",\\n \\\"conda_info.txt.md5\\\",\\n \\\"conda_list.txt.md5\\\",\\n ),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.{report}.txt\\\"\\n ),\\n report=(\\\"bamstats\\\", \\\"flagstats\\\", \\\"idxstats\\\"),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.{report}.txt.md5\\\"\\n ),\\n report=(\\\"bamstats\\\", \\\"flagstats\\\", \\\"idxstats\\\"),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.bamstats.html\\\"\\n )\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.bamstats.html.md5\\\"\\n )\\n )\\n\\n for sheet in self.shortcut_sheets:\\n for ngs_library in sheet.all_ngs_libraries:\\n if ngs_library.name in self.ngs_library_to_kit:\\n extraction_type = ngs_library.test_sample.extra_infos[\\\"extractionType\\\"]\\n suffix = (\\n \\\"_long\\\"\\n if ngs_library.extra_infos[\\\"seqPlatform\\\"] in (\\\"PacBio\\\", \\\"ONP\\\")\\n else \\\"\\\"\\n )\\n # Per-sample target coverage report.\\n yield from expand(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"cov_qc\\\", name_pattern + \\\".{ext}\\\"\\n ),\\n mapper=self.config[\\\"tools\\\"][extraction_type.lower() + suffix],\\n ngs_library=[ngs_library],\\n ext=[\\\"txt\\\", \\\"txt.md5\\\"],\\n )\\n yield \\\"output/target_cov_report/out/target_cov_report.txt\\\"\\n yield \\\"output/target_cov_report/out/target_cov_report.txt.md5\\\"\\n if (\\n self.config[\\\"picard_hs_metrics\\\"][\\\"path_targets_interval_list\\\"]\\n and self.config[\\\"picard_hs_metrics\\\"][\\\"path_baits_interval_list\\\"]\\n ):\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"picard_hs_metrics\\\", name_pattern + \\\".txt\\\"\\n )\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"picard_hs_metrics\\\", name_pattern + \\\".txt.md5\\\"\\n )\\n )\\n if self.config[\\\"compute_coverage_bed\\\"]:\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"report\\\", \\\"coverage\\\", name_pattern + \\\"{ext}\\\"),\\n ext=(\\\".bed.gz\\\", \\\".bed.gz.tbi\\\"),\\n )\\n else:\\n print(\\n \\\"Genome-wide coverage BED generation disabled\\\", file=sys.stderr\\n ) # pragma: no cover\",\n \"def read_sample(sample_path):\\n files = os.listdir(sample_path)\\n json_path = [f for f in files if f.endswith('_read-info.json')]\\n assert len(json_path) == 1, 'There should be exactly 1 *_read-info.json file in the sample (.smpl)'\\n # get base for sample\\n with open(os.path.join(sample_path, json_path[0]), 'r') as f:\\n sample = json.load(f)\\n # get sample info\\n info_path = [f for f in files if f.endswith('info.csv')]\\n assert len(info_path) == 1, 'There should be exactly 1 *_info.csv file in the sample (.smpl)'\\n sample['info'] = pd.read_csv(os.path.join(sample_path, info_path[0]))\\n info_path = os.path.join(sample_path, info_path[0])\\n # get individual sample\\n regex = re.compile(r'id-\\\\d*_t-\\\\d*')\\n pairs = [f for f in files if len(regex.findall(f)) > 0]\\n for str_pair in pairs:\\n pair_dir = os.path.join(sample_path, str_pair)\\n pair_dict = {}\\n # get df\\n df_path = os.path.join(pair_dir, str_pair + '_df.csv')\\n pair_dict['df'] = pd.read_csv(df_path)\\n pair_dict['df_path'] = df_path\\n pair_dict['info_path'] = info_path\\n # get tracks\\n tracks_path = os.path.join(pair_dir, str_pair + '_tracks.zarr')\\n pair_dict['tracks'] = zarr.open(tracks_path)\\n # get b_box\\n bbox_path = os.path.join(pair_dir, str_pair + '_bbox.json')\\n with open(bbox_path, 'r') as f:\\n bbox = json.load(f)\\n new_bbox = {}\\n for key in bbox.keys():\\n args = list(bbox[key])\\n new_bbox[key] = slice(*args)\\n pair_dict['b_box'] = new_bbox\\n # find out if corrs exist and read if so\\n corr_path = [f for f in os.listdir(pair_dir) if f.endswith('_corr.json')]\\n if len(corr_path) > 0:\\n with open(os.path.join(pair_dir, corr_path[0]), 'r') as f:\\n corr = json.load(f)\\n pair_dict['corr'] = corr\\n # if image exists, read\\n img_path = os.path.join(pair_dir, str_pair + '_image.zarr')\\n if os.path.exists(img_path):\\n img = zarr.open(img_path)\\n #print(img, img_path)\\n pair_dict['image'] = img\\n # if labels exist, read\\n lab_path = os.path.join(pair_dir, str_pair + '_labels.zarr')\\n if os.path.exists(lab_path):\\n lab = zarr.open(lab_path)\\n #print(lab.shape)\\n pair_dict['labels'] = lab\\n # add pair to sample\\n id_re = re.compile(r'id-\\\\d*')\\n ID = id_re.findall(str_pair)[0][3:]\\n t_re = re.compile(r't-\\\\d*')\\n t = t_re.findall(str_pair)[0][2:]\\n pair = (int(ID), int(t))\\n sample[pair] = pair_dict\\n #print(str_pair, pair)\\n #print(list(sample.keys()))\\n #print(list(sample[eval(pairs[0])].keys()))\\n return sample\",\n \"def main(file_list):\\n data_store = {}\\n \\n for file in file_list:\\n sample_id = get_sample_id(file)\\n data_store[sample_id] = {}\\n data_store[sample_id][\\\"sample_type\\\"], data_store[sample_id][\\\"out_filename\\\"], data_store[sample_id][\\\"out_location\\\"] = check_name(file, sample_id)\\n data_store[sample_id][\\\"df_parameters\\\"], data_store[sample_id][\\\"df_values\\\"], data_store[sample_id][\\\"df_parameters_for_values\\\"] = data_in(file, sample_id)\\n if data_store[sample_id][\\\"sample_type\\\"] == \\\"EFC\\\":\\n binder_mass = efc_calcs(data_store[sample_id][\\\"df_parameters\\\"])\\n elif data_store[sample_id][\\\"sample_type\\\"] == \\\"OPC\\\":\\n binder_mass = opc_calcs(data_store[sample_id][\\\"df_parameters\\\"])\\n data_store[sample_id][\\\"df_values\\\"] = tidy_val_df(data_store[sample_id][\\\"df_values\\\"], binder_mass)\\n data_store[sample_id][\\\"df_parameters\\\"] = tidy_param_df(sample_id, data_store[sample_id][\\\"df_parameters\\\"], data_store[sample_id][\\\"out_filename\\\"])\\n for key, value in data_store.items():\\n write_to_excel(key, value[\\\"df_parameters\\\"], value[\\\"df_values\\\"], value[\\\"df_parameters_for_values\\\"], value[\\\"out_location\\\"])\",\n \"def file_summaries(self):\\n summaries = {}\\n if 'data' not in self.coverage_data:\\n return summaries\\n for info in self.coverage_data['data']:\\n totals = info.get('totals', None)\\n if totals:\\n summaries['totals'] = totals\\n for finfo in info['files']:\\n filename = finfo.get('filename', None)\\n if not filename:\\n continue # :|\\n if filename in summaries:\\n raise ClangCoverageFilenameCollision(\\n \\\"colliding file name: {}\\\".format(filename))\\n summaries[filename] = finfo.get('summary', None)\\n return summaries\",\n \"def read_combined_testdata(path):\\n # combine all subsets into one and shuffle\\n test_inputs_sub, test_labels_sub, test_sad_sub = read_shared_testdata(path)\\n\\n test_inputs_all, test_labels_all, test_sad_all = (dict() for _ in range(3))\\n for block in test_inputs_sub:\\n test_inputs_all[block], test_labels_all[block], test_sad_all[block] = \\\\\\n concatenate_dictionary_keys(test_inputs_sub[block], test_labels_sub[block], test_sad_sub[block])\\n\\n return test_inputs_all, test_labels_all, test_sad_all\",\n \"def get_samples():\\n r = req('GET', SUB_API + 'samples', params=handle_filters())\\n samples = []\\n for k in demisto.get(r.json(), 'data.items'):\\n samples.append(sample_to_readable(k))\\n md = tableToMarkdown('ThreatGrid - List of Samples', samples, [\\n 'ID', 'Filename', 'State', 'Status', 'MD5', 'SHA1', 'SHA256', 'OS', 'SubmittedAt', 'StartedAt', 'CompletedAt'\\n ])\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'EntryContext': {'ThreatGrid.Sample(val.ID == obj.ID)': samples},\\n 'HumanReadable': md,\\n 'ContentsFormat': formats['json'],\\n 'Contents': r.json()\\n })\",\n \"def main(raw_args=None):\\r\\n args = get_args(raw_args)\\r\\n dataset = DATASETS[args.dataset]\\r\\n dataset.set_work_dir(args.path)\\r\\n\\r\\n dataset_dir = os.path.join(dataset.get_data_path(), consts.RAW_DATA_DIR)\\r\\n input_file = os.path.join(dataset_dir, args.input)\\r\\n if args.output:\\r\\n out_name = args.output\\r\\n else:\\r\\n out_name = OUT_FILE % (args.chr, format_number(args.size))\\r\\n output_file = os.path.join(dataset_dir, out_name)\\r\\n\\r\\n print(\\\"Output will be in: %s\\\" % output_file)\\r\\n\\r\\n entries = set()\\r\\n out = open(output_file, 'w')\\r\\n input = open(input_file, 'r')\\r\\n\\r\\n is_first = True\\r\\n for line in input:\\r\\n if is_first:\\r\\n add_headers(line.strip(), out)\\r\\n is_first = False\\r\\n else:\\r\\n entries.add(line)\\r\\n input.close()\\r\\n print(\\\"Done reading\\\")\\r\\n\\r\\n # Keeps track of spacers that were already included.\\r\\n spacers = set()\\r\\n # Keeps track of the number of samples already obtained.\\r\\n sampled = 0\\r\\n # Keeps track of progress for printing.\\r\\n chunk = 1\\r\\n\\r\\n # While we have not sampled enough...\\r\\n while sampled < args.size:\\r\\n remaining = args.size - sampled\\r\\n # Sample more entries.\\r\\n new_entries = sample(entries, spacers, remaining, dataset)\\r\\n sampled += len(new_entries)\\r\\n for entry in new_entries:\\r\\n out.write(common.to_csv_line(*entry))\\r\\n\\r\\n # If we have passed a progress checkpoint - prints progress.\\r\\n if (args.size / PRINT_NUM) * chunk < sampled:\\r\\n print(\\\"Sampled: %d\\\" % sampled)\\r\\n chunk += 1\\r\\n\\r\\n print(\\\"Done\\\")\\r\\n print(STATS)\",\n \"def _read_sample_data(self):\\n def _read_tab(pth):\\n \\\"\\\"\\\"\\n Internal read table function\\n\\n :param str pth: absolute path to the file to read\\n :return pandas.DataFrame: table object\\n \\\"\\\"\\\"\\n if not os.path.exists(pth):\\n raise SampleTableFileException(\\n \\\"File does not exist: {}\\\".format(pth))\\n read_csv_kwargs = {\\\"engine\\\": \\\"python\\\", \\\"dtype\\\": str,\\n \\\"index_col\\\": False, \\\"keep_default_na\\\": False,\\n \\\"na_values\\\": [\\\"\\\"]}\\n return pd.read_csv(pth, sep=infer_delimiter(pth), **read_csv_kwargs)\\n\\n no_metadata_msg = \\\"No {} specified\\\"\\n if CONFIG_KEY not in self:\\n _LOGGER.warning(\\\"No config key in Project\\\")\\n return\\n if CFG_SAMPLE_TABLE_KEY not in self[CONFIG_KEY]:\\n _LOGGER.debug(\\\"no {} found\\\".format(CFG_SAMPLE_TABLE_KEY))\\n return\\n st = self[CONFIG_KEY][CFG_SAMPLE_TABLE_KEY]\\n if st:\\n self[SAMPLE_DF_KEY] = _read_tab(st)\\n else:\\n _LOGGER.warning(no_metadata_msg.format(CFG_SAMPLE_TABLE_KEY))\\n self[SAMPLE_DF_KEY] = None\\n if CFG_SUBSAMPLE_TABLE_KEY in self[CONFIG_KEY]:\\n if self[CONFIG_KEY][CFG_SUBSAMPLE_TABLE_KEY] is not None:\\n sst = make_list(self[CONFIG_KEY][CFG_SUBSAMPLE_TABLE_KEY], str)\\n self[SUBSAMPLE_DF_KEY] = [_read_tab(x) for x in sst]\\n else:\\n _LOGGER.debug(no_metadata_msg.format(CFG_SUBSAMPLE_TABLE_KEY))\\n self[SUBSAMPLE_DF_KEY] = None\",\n \"def legacy_reporter(self):\\n logging.info('Creating database-friendly summary report')\\n header = '{}\\\\n'.format(','.join(self.legacy_headers))\\n # Create a string to store all the results\\n data = str()\\n for sample in self.metadata:\\n # Add the value of the appropriate attribute to the results string\\n data += GenObject.returnattr(sample, 'name')\\n # SampleName\\n data += GenObject.returnattr(sample.run, 'SamplePlate')\\n # Genus\\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\\n # SequencingDate\\n data += GenObject.returnattr(sample.run, 'Date')\\n # Analyst\\n data += GenObject.returnattr(sample.run, 'InvestigatorName')\\n # Legacy ConFindr clean/contaminated call\\n data += 'ND,'\\n # N50\\n n50 = GenObject.returnattr(sample.quast, 'N50',\\n number=True)\\n if n50 != '-,':\\n data += n50\\n else:\\n data += '0,'\\n # NumContigs\\n data += GenObject.returnattr(sample.quast, 'num_contigs',\\n number=True)\\n # TotalLength\\n data += GenObject.returnattr(sample.quast, 'Total_length',\\n number=True)\\n # MeanInsertSize\\n data += GenObject.returnattr(sample.quast, 'mean_insert',\\n number=True)\\n # InsertSizeSTD\\n data += GenObject.returnattr(sample.quast, 'std_insert',\\n number=True)\\n # AverageCoverageDepth\\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\\n number=True)\\n # CoverageDepthSTD\\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\\n number=True)\\n # PercentGC\\n data += GenObject.returnattr(sample.quast, 'GC',\\n number=True)\\n # MASH_ReferenceGenome\\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\\n # MASH_NumMatchingHashes\\n data += GenObject.returnattr(sample.mash, 'nummatches')\\n # 16S_result\\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\\n # 16S PercentID\\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\\n # rMLST_Result\\n try:\\n # If the number of matches to the closest reference profile is 53, return the profile number\\n if sample.rmlst.matches == 53:\\n if type(sample.rmlst.sequencetype) is list:\\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\\n else:\\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\\n data += rmlst_seq_type\\n else:\\n # Otherwise the profile is set to new\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_Result\\n try:\\n if sample.mlst.matches == 7:\\n if type(sample.mlst.sequencetype) is list:\\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\\n else:\\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\\n data += mlst_seq_type\\n else:\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_gene_X_alleles\\n try:\\n # Create a set of all the genes present in the results (gene name split from allele)\\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\\n for gene in sorted(gene_set):\\n allele_list = list()\\n # Determine all the alleles that are present for each gene\\n for allele in sample.mlst.combined_metadata_results:\\n if gene in allele:\\n allele_list.append(allele.replace(' ', '_'))\\n # If there is more than one allele in the sample, add both to the string separated by a ';'\\n if len(allele_list) > 1:\\n data += '{},'.format(';'.join(allele_list))\\n # Otherwise add the only allele\\n else:\\n data += allele_list[0] + ','\\n # If there are fewer than seven matching alleles, add a ND for each missing result\\n if len(gene_set) < 7:\\n data += (7 - len(gene_set)) * 'ND,'\\n except AttributeError:\\n # data += '-,-,-,-,-,-,-,'\\n data += 'ND,ND,ND,ND,ND,ND,ND,'\\n # CoreGenesPresent\\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\\n # E_coli_Serotype\\n try:\\n # If no O-type was found, set the output to be O-untypeable\\n if ';'.join(sample.ectyper.o_type) == '-':\\n otype = 'O-untypeable'\\n else:\\n otype = sample.ectyper.o_type\\n # Same as above for the H-type\\n if ';'.join(sample.ectyper.h_type) == '-':\\n htype = 'H-untypeable'\\n\\n else:\\n htype = sample.ectyper.h_type\\n serotype = '{otype}:{htype},'.format(otype=otype,\\n htype=htype)\\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # SISTR_serovar_antigen\\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\\n # SISTR_serovar_cgMLST\\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\\n # SISTR_serogroup\\n data += GenObject.returnattr(sample.sistr, 'serogroup')\\n # SISTR_h1\\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\\n # SISTR_h2\\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\\n # SISTR_serovar\\n data += GenObject.returnattr(sample.sistr, 'serovar')\\n # GeneSeekr_Profile\\n try:\\n if sample.genesippr.report_output:\\n data += ';'.join(sample.genesippr.report_output) + ','\\n else:\\n data += 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # Vtyper_Profile\\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\\n # AMR_Profile and resistant/sensitive status\\n if sample.resfinder_assembled.pipelineresults:\\n # Profile\\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\\n r_set=';'.join(sorted(list(resistance_set))))\\n data += ','\\n # Resistant/Sensitive\\n data += 'Resistant,'\\n else:\\n # Profile\\n data += 'ND,'\\n # Resistant/Sensitive\\n data += 'Sensitive,'\\n # Plasmid Result'\\n if sample.mobrecon.pipelineresults:\\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\\n data += '{plasmid}({details});'.format(plasmid=plasmid,\\n details=details)\\n data += ','\\n else:\\n data += 'ND,'\\n # TotalPredictedGenes\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\\n number=True)\\n # PredictedGenesOver3000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\\n number=True)\\n # PredictedGenesOver1000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\\n number=True)\\n # PredictedGenesOver500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\\n number=True)\\n # PredictedGenesUnder500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\\n number=True)\\n # NumClustersPF\\n data += GenObject.returnattr(sample.run, 'NumberofClustersPF')\\n # Percentage of reads mapping to PhiX control\\n data += GenObject.returnattr(sample.run, 'phix_aligned')\\n # Error rate calculated from PhiX control\\n data += GenObject.returnattr(sample.run, 'error_rate')\\n # LengthForwardRead\\n data += GenObject.returnattr(sample.run, 'forwardlength',\\n number=True)\\n # LengthReverseRead\\n data += GenObject.returnattr(sample.run, 'reverselength',\\n number=True)\\n # Real time strain\\n data += GenObject.returnattr(sample.run, 'Description')\\n # Flowcell\\n data += GenObject.returnattr(sample.run, 'flowcell')\\n # MachineName\\n data += GenObject.returnattr(sample.run, 'instrument')\\n # PipelineVersion\\n data += self.commit + ','\\n # AssemblyDate\\n data += datetime.now().strftime('%Y-%m-%d') + ','\\n # SamplePurity\\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\\n # cgMLST\\n try:\\n if type(sample.cgmlst.sequencetype) is list:\\n if sample.cgmlst.sequencetype:\\n cgmlst_seq_type = ';'.join(sorted(sample.cgmlst.sequencetype)).rstrip(';') + ','\\n else:\\n cgmlst_seq_type = 'ND,'\\n else:\\n cgmlst_seq_type = GenObject.returnattr(sample.cgmlst, 'sequencetype')\\n # cgmlst_seq_type = cgmlst_seq_type if cgmlst_seq_type != 'ND,' else 'new,'\\n data += cgmlst_seq_type\\n except AttributeError:\\n data += 'ND,'\\n # Name of the database used in the analyses\\n data += os.path.split(self.reffilepath)[-1] + ','\\n # Database download date\\n data += self.download_date\\n # Append a new line to the end of the results for this sample\\n data += '\\\\n'\\n # Replace any NA values with ND\\n cleandata = data.replace('NA', 'ND')\\n with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:\\n metadatareport.write(header)\\n metadatareport.write(cleandata)\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def tgt_samples(self, params):\\r\\n def save_json(save_path, file_id, samples):\\r\\n init_logger()\\r\\n for i, sample in enumerate(samples):\\r\\n save_ = os.path.join(save_path, \\\"{:s}_{:d}.json\\\".format(file_id, i))\\r\\n with open(save_, 'w') as file:\\r\\n json.dump(sample, file)\\r\\n logger.info(\\\"{:s} saved at {:s}\\\".format(save_, save_path))\\r\\n\\r\\n\\r\\n json_file, save_path = params\\r\\n init_logger()\\r\\n _, tgt = self.load_json(json_file)\\r\\n\\r\\n file_id = json_file.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\r\\n if len(tgt) >= self.args.min_sents_num and len(tgt) <= self.args.max_sents_num:\\r\\n tgt_ = list(tgt)\\r\\n random.seed(66)\\r\\n random.shuffle(tgt_)\\r\\n\\r\\n # make sentence pair and write in a single file\\r\\n positive_sents = tgt\\r\\n positive_pairs = [(positive_sents[i], positive_sents[i+1]) for i in range(len(positive_sents)-1)]\\r\\n\\r\\n negative_sents = tgt_\\r\\n negative_pairs = [(negative_sents[i], negative_sents[i+1]) for i in range(len(negative_sents)-1)]\\r\\n\\r\\n positive_samples = [{\\\"tgt\\\": pair, \\\"coherence\\\": 0} for pair in positive_pairs] # 0 represents coherent\\r\\n negative_samples = [{\\\"tgt\\\": pair, \\\"coherence\\\": 1} for pair in negative_pairs] # 1 represents incoherent\\r\\n\\r\\n save_json(save_path, file_id, positive_samples)\\r\\n save_json(save_path, file_id+\\\"_r\\\", negative_samples)\",\n \"def read_combine_elia_freq_R1(path):\\r\\n i=0\\r\\n dfs = []\\r\\n data_files = glob.glob(path + 'FrequencyAndDemand_*')\\r\\n print(str(datetime.datetime.utcnow()) + \\\" amount of files to combine: \\\" + str(len(data_files)))\\r\\n for file in data_files:\\r\\n i=i+1\\r\\n print(str(datetime.datetime.utcnow()) + \\\" processing file number: \\\"+ str(i))\\r\\n df = read_elia_freq_R1(file)\\r\\n dfs.append(df)\\r\\n combined_data = pd.concat(dfs, axis = 0)\\r\\n return combined_data\",\n \"def get_all_files(samples, directory, suffix=\\\"\\\", nested=False):\\n files = []\\n if type(directory) == list:\\n directory = directory[0]\\n for sample in samples:\\n for unit in samples[sample].keys():\\n if nested:\\n d = \\\"{}/{}_{}\\\".format(directory, sample, unit)\\n else:\\n d = \\\"{}\\\".format(directory)\\n if is_pe(samples[sample][unit]):\\n files.append(d+\\\"/{}_{}_pe{}\\\".format(sample, unit, suffix))\\n else:\\n files.append(d+\\\"/{}_{}_se{}\\\".format(sample, unit, suffix))\\n return files\",\n \"def main():\\n file_one_path, file_two_path, output_path =\\\\\\n get_command_line_arguments(\\n ['/home/ehler002/project/groups/go/Data/Cluster_Data/Dataset.txt',\\n '/home/ehler002/project/groups/go/Data/Cluster_Data/translated_genes.fpkm_table',\\n '/home/ehler002/project/groups/go/Data/Cluster_Data/Full_fpkm_Table.txt'])\\n pattern = 'CRO_T'\\n for file_path in [file_one_path, file_two_path]:\\n assert os.path.exists(file_path), 'File %s does not exist.' % file_path\\n start_time = datetime.datetime.now()\\n print('Started concatenation at %s' % start_time)\\n file_contents, headers = get_file_contents(file_two_path)\\n file_contents = sort_file_contents(file_contents)\\n file_contents = remove_pattern(file_contents, pattern)\\n concatenate_files(file_one_path, file_contents, headers, output_path)\\n print('Finished concatenation in %s' % (datetime.datetime.now() - start_time))\",\n \"def collect_stats_samples(\\n peaktable, \\n groups, \\n bioactivity_samples,\\n orig_bioactiv_df,\\n ):\\n rt_min = min(peaktable.loc[:,\\\"retention_time\\\"])\\n rt_max = max(peaktable.loc[:,\\\"retention_time\\\"])\\n rt_range = (rt_max - rt_min)\\n \\n #Get names of samples by mining peaktable columns\\n samples_set = set()\\n for label in peaktable.filter(regex=\\\":intensity_range:max\\\").columns:\\n samples_set.add(label.split(\\\":\\\")[1])\\n \\n #AD BIOACTIVITY\\n #If bioactivity was provided, test if 'active' samples exist in samples_set\\n #If no bioactivity provided, all samples are set as 'inactive'\\n try:\\n active_samples_set = set(bioactivity_samples.loc[:,'sample_name'])\\n inactive_samples_set = copy.deepcopy(samples_set)\\n for active_sample in active_samples_set.copy():\\n if not active_sample in samples_set:\\n active_samples_set.remove(active_sample)\\n \\n for active_sample in active_samples_set:\\n inactive_samples_set.remove(active_sample)\\n \\n orig_bioactiv = dict()\\n for id, row in orig_bioactiv_df.iterrows():\\n orig_bioactiv[row['sample_name']] = row['quant_data']\\n \\n except:\\n active_samples_set = set()\\n inactive_samples_set = copy.deepcopy(samples_set)\\n orig_bioactiv = dict()\\n \\n #AD GROUPS (METADATA)\\n #Test if all samples have been assigned their groups;\\n #If any unassigned samples, assign to reserved group \\\"GENERAL\\n grouped_samples_set = set()\\n for group in groups:\\n for sample in groups[group].copy():\\n if sample in samples_set:\\n grouped_samples_set.add(sample)\\n else:\\n groups[group].remove(sample)\\n for i in samples_set.difference(grouped_samples_set):\\n groups['GENERAL'].add(i)\\n \\n #create a dict of samples with group info attached\\n samples_list = list(samples_set)\\n samples_dict = {}\\n for sample in samples_list:\\n for group in groups:\\n if sample in groups[group]:\\n samples_dict[sample] = group\\n \\n sample_stats = {\\n \\\"rt_min\\\" : rt_min, \\n \\\"rt_max\\\" : rt_max,\\n \\\"rt_range\\\" : rt_range,\\n \\\"samples_list\\\" : samples_list,\\n \\\"samples_dict\\\" : samples_dict,\\n \\\"groups_dict\\\" : groups,\\n \\\"features_per_sample\\\" : {},\\n \\\"cliques_per_sample\\\" : {},\\n \\\"cliques\\\" : {},\\n \\\"nr_all_cliques\\\" : \\\"\\\",\\n \\\"set_all_cliques\\\" : set(),\\n \\\"set_blank_cliques\\\" : set(),\\n \\\"active_samples_set\\\" : active_samples_set,\\n \\\"inactive_samples_set\\\" : inactive_samples_set,\\n 'original_bioactivity' : orig_bioactiv,\\n 'relative_intensity_removed_features' : [],\\n 'ms2query_annotation_excluded_based_on_range_filter' : [],\\n }\\n return sample_stats\",\n \"def produce_test_all(root_dir):\\n test = 'testImnamesSe.csv'\\n all_bboxes = 'bboxesDF.csv'\\n test_lab = 'testLabeledDF.csv'\\n\\n if test in os.listdir(root_dir) and all_bboxes in os.listdir(root_dir) \\\\\\n and test_lab in os.listdir(root_dir):\\n test_imnames = pd.read_csv(osp.join(root_dir, test),\\n header=None, squeeze=True)\\n all_bboxes = pd.read_csv(osp.join(root_dir, all_bboxes))\\n test_lab = pd.read_csv(osp.join(root_dir, test_lab))\\n else:\\n _, test_imnames = process_pool_mat(root_dir)\\n all_bboxes, _ = process_images_mat(root_dir)\\n test_lab, _ = process_test_mat(root_dir)\\n\\n test_all = all_bboxes[all_bboxes['imname'].isin(test_imnames)]\\n test_all.index = range(test_all.shape[0])\\n test_all = pd.merge(test_all, test_lab, how='outer')\\n test_all['pid'] = test_all['pid'].fillna(-1)\\n test_all['is_query'] = test_all['is_query'].fillna(0)\\n test_all['pid'] = test_all['pid'].values.astype(np.int32)\\n test_all['is_query'] = test_all['is_query'].values.astype(np.int32)\\n\\n test_all.to_csv(osp.join(root_dir, 'testAllDF.csv'), index=False)\\n\\n return test_all\",\n \"def read_files(dataroot, is_test=False, preprocess=False, aug_dataset_path=None):\\n\\tall_filenames = sorted(glob.glob(dataroot), key=alphanum_key)\\n\\tsamples_raw = []\\n\\tpreprocessed = 0\\n\\tskipped = 0\\n\\n\\tfor fname in all_filenames:\\n\\t\\twith open(fname, 'r') as fp:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tproblem_data = json.load(fp)\\n\\t\\t\\t\\t#print(fname)\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tprint(f\\\"Error loading JSON from {fname}\\\", e)\\n\\t\\t\\t\\traise e\\n\\n\\t\\tproblem = MATHProblem(\\n\\t\\t\\tfname,\\n\\t\\t\\tproblem_data['problem'],\\n\\t\\t\\tproblem_data['type'],\\n\\t\\t\\tproblem_data['level'],\\n\\t\\t\\texplanation=problem_data['explanation'] if not is_test else None,\\n\\t\\t\\tsolution=problem_data['solution'] if not is_test else None,\\n\\t\\t\\tequation=problem_data['equation'] if not is_test else None,\\n\\t\\t\\tjson_data=problem_data,\\n\\t\\t\\tpreprocess=preprocess,\\n\\t\\t)\\n\\n\\t\\tif problem.no_expression:\\n\\t\\t\\tskipped += 1\\n\\t\\tsamples_raw.append(problem)\\n\\n\\tif not is_test and aug_dataset_path:\\n\\t\\tprint('Adding another dataset to the samples...')\\n\\t\\twith open(aug_dataset_path) as fp:\\n\\t\\t\\tdata = json.load(fp)\\n\\t\\t\\tfor data_item in data:\\n\\t\\t\\t\\tproblem = MATHProblem(\\n\\t\\t\\t\\t\\t'augmented',\\n\\t\\t\\t\\t\\tdata_item['sQuestion'],\\n\\t\\t\\t\\t\\t'MWP',\\n\\t\\t\\t\\t\\t'NA',\\n\\t\\t\\t\\t\\tsolution=str(data_item['lSolutions'][0]),\\n\\t\\t\\t\\t\\tequation=data_item['new_equation'],\\n\\t\\t\\t\\t)\\n\\n\\t\\t\\t\\tif problem.no_expression:\\n\\t\\t\\t\\t\\tskipped += 1\\n\\t\\t\\t\\tsamples_raw.append(problem)\\t\\n\\n\\tprint('There are no expressions for {} in {} items'.format(skipped, len(samples_raw)))\\n\\treturn samples_raw\",\n \"def prepare_subset_vcf_files_by_population():\\n if not os.path.exists(VCF_BY_POPULATION_PATH):\\n print(\\\"preparing subset vcf by population\\\")\\n os.makedirs(VCF_BY_POPULATION_PATH)\\n sleep(10)\\n vcf_tools_runner = VCFToolsDockerRunner()\\n samples = glob(f\\\"{SAMPLES_FOLDER}/*.csv\\\")\\n\\n with tqdm(total=len(samples)) as pbar:\\n for sample in samples:\\n sample = sample.replace('\\\\\\\\', '/')\\n sample_name = get_filename_from_path(sample)\\n sample_path = \\\"/\\\".join([IMAGE_SHARE_FOLDER_PATH] + sample.split('/')[1:])\\n pbar.set_description(f\\\"Processing {sample_name}\\\")\\n vcf_tools_runner(\\n f\\\"vcf-subset -c {sample_path} \\\"\\n f\\\"{IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_VCF_FILE_NAME} | fill-an-ac > \\\"\\n f\\\"{IMAGE_SHARE_FOLDER_PATH}/{VCF_BY_POPULATION_FOLDER}/{sample_name}.vcf\\\")\\n pbar.update(1)\\n else:\\n print(f\\\"Subset VCF files by population already exist in: {VCF_BY_POPULATION_PATH}\\\")\",\n \"def gather_sample(self):\\n\\n for _pid in self._select_processes():\\n if not self.__trackers.get(_pid):\\n self.__trackers[_pid] = ProcessTracker(_pid, self._logger, self.__id)\\n\\n self._reset_absolute_metrics()\\n\\n for _tracker in self.__trackers.values():\\n _metrics = _tracker.collect()\\n self.record_metrics(_tracker.pid, _metrics)\\n\\n self._calculate_aggregated_metrics()\\n self._remove_dead_processes()\\n\\n self.print_metrics()\",\n \"def create_sample_listing():\\n entries = []\\n for task_name, dataset in chain(MCBackgroundsSampleDictionaryUnordered, SignalMCSampleDictionaryUnordered, DataDictionaryMuonChannelUnordered):\\n matching_output = [x for x in TaskDictionaryNameUnordered if x[0] == task_name]\\n if len(matching_output) > 1:\\n raise RuntimeError(\\\"More than 1 match for %s\\\" % task_name)\\n if len(matching_output) == 0:\\n print \\\"No match for task %s\\\" % task_name\\n continue\\n output_name = matching_output[0][1]\\n entries.append(Sample(task_name, dataset, output_name))\\n\\n return entries\",\n \"def samples(self):\\n if self._samples:\\n return self._samples\\n if SAMPLE_DF_KEY not in self or self[SAMPLE_DF_KEY] is None:\\n _LOGGER.debug(\\\"No samples are defined\\\")\\n return []\",\n \"def pool_test_dataset_dir(obs_dir_fp,exp_dir_fp,file_name_delimiter=\\\"--\\\",\\\\\\n file_name_field_order=\\\\\\n {'file_type':0,\\\"prediction_method\\\":1,\\\"weighting_method\\\":2,\\\"holdout_method\\\":3,\\\\\\n \\\"distance\\\":4,\\\"organism\\\":5},strict=False, verbose=True,pool_by=['distance']):\\n trials = defaultdict(list)\\n #We'll want a quick unzip fn for converting points to trials\\n #TODO: separate out into a 'get_paired_data_from_dirs' function\\n\\n pooled_observations = {}\\n pooled_expectations = {}\\n pairs = iter_prediction_expectation_pairs(obs_dir_fp,exp_dir_fp,file_name_field_order,file_name_delimiter,verbose=verbose)\\n file_number = 0\\n for obs_table,exp_table,filename in pairs:\\n #print \\\"analyzing filename:\\\",filename \\n filename_metadata= get_metadata_from_filename(filename,file_name_field_order,\\\\\\n file_name_delimiter,verbose=verbose)\\n \\n #base_tag = '%s\\\\t%s\\\\t' %(filename_metadata['holdout_method'],filename_metadata['prediction_method'])\\n #tags = [base_tag+'all_results']\\n if 'file_type' in pool_by:\\n pool_by.remove('file_type') #we do this manually at the end\\n combined_tag = ['all']*len(file_name_field_order.keys())\\n for field in file_name_field_order.keys():\\n #print combined_tag\\n #print file_name_field_order\\n idx = file_name_field_order[field]\\n #print idx\\n if field in pool_by:\\n combined_tag[idx] = filename_metadata[field]\\n tags=[file_name_delimiter.join(combined_tag)]\\n \\n if verbose:\\n print \\\"Pooling by:\\\", pool_by\\n print \\\"Combined tags:\\\",tags\\n \\n pooled_observations,pooled_expectations =\\\\\\n update_pooled_data(obs_table,exp_table,tags,pooled_observations,\\\\\\n pooled_expectations,str(file_number),verbose=verbose)\\n file_number += 1\\n\\n return pooled_observations,pooled_expectations\",\n \"def create_sample_files(bam_file_name, fractions):\\n\\n print(\\\"running sample retreival: \\\\n\\\\n\\\")\\n\\n sample_folder = \\\"./%s_sample_stats/\\\" % bam_file_name.replace(\\n \\\"_mRNA.bam\\\", \\\"\\\")\\n\\n if not os.path.exists(sample_folder):\\n os.mkdir(sample_folder)\\n os.chdir(sample_folder)\\n else:\\n os.chdir(sample_folder)\\n print(\\\"fractions: \\\", fractions)\\n for decimal in fractions:\\n print(\\\"%s: started\\\" % decimal)\\n file_var = \\\"%s_sample.bam\\\" % str(decimal).replace(\\\"0.\\\", \\\"\\\")\\n\\n # extract sample\\n print(bam_file_name, file_var)\\n sample_cmd = \\\"samtools view ../%s -s %s -b -@12 > %s\\\" % (\\n bam_file_name, '{:f}'.format(decimal), file_var)\\n print(sample_cmd)\\n os.system(sample_cmd)\",\n \"def data_sample(complexe_list, taille):\\n\\n indices = random.sample(range(len(complexe_list)), taille)\\n\\n complex_file_names = [complexe_list[i] for i in indices]\\n\\n return(complex_file_names)\",\n \"def generate_figures_and_xls_all_strains(outdir, cols_starts, region2data, ext, xls, group2pos, feature_names, samples):\\n all_freqs = []\\n # concatenate all pos and samples into one dataframe\\n dframes = []\\n for ri, (ref, pos) in enumerate(region2data.keys()): #regions): #[3]#; print(ref, pos, mt)\\n mer, calls = region2data[(ref, pos)]\\n for c, s in zip(calls, samples): \\n df = pd.DataFrame(c, columns=feature_names)\\n df[\\\"Strain\\\"] = s\\n df[\\\"chr_pos\\\"] = \\\"%s:%s\\\"%(ref, pos)\\n dframes.append(df)\\n # read all tsv files\\n df = pd.concat(dframes).dropna().reset_index()\\n chr_pos, strains = df[\\\"chr_pos\\\"].unique(), df[\\\"Strain\\\"].unique() \\n # compare individual methods\\n for clf, method in (\\n (KMeans(n_clusters=2), \\\"KMeans\\\"), \\n (KNeighborsClassifier(), \\\"KNN\\\"), \\n #(iso_new.iForest(ntrees=100, random_state=0), \\\"GMM+eIF\\\"), \\n (GaussianMixture(random_state=0, n_components=2), \\\"GMM\\\"), \\n (AgglomerativeClustering(n_clusters=2), \\\"AggClust\\\"), \\n #(OneClassSVM(), \\\"OCSVM\\\"), \\n (IsolationForest(random_state=0), \\\"IF\\\"), \\n #(iso_new.iForest(ntrees=100, random_state=0), \\\"eIF\\\"), \\n (RandomForestClassifier(), \\\"RF\\\"), \\n ):\\n fname = method\\n for i, cols_start in enumerate(cols_starts, 1):\\n results = []\\n feat_name = \\\"_\\\".join(cols_start)\\n fname = \\\"%s.%s\\\"%(method, feat_name); print(fname)\\n outfn = os.path.join(outdir, \\\"%s.%s\\\"%(fname, ext))\\n # narrow down the features to only signal intensity & trace\\n cols = list(filter(lambda n: n.startswith(cols_start), feature_names))#; print(cols) #, \\\"DT\\\"\\n # compare all samples to 0%\\n s0 = samples[0]\\n for s in samples[3:]: \\n with np.errstate(under='ignore'):\\n if \\\"+\\\" in method:\\n clf2_name = method.split(\\\"+\\\")[-1]\\n results += get_mod_freq_two_step(df, cols, chr_pos, [s0, s], feat_name, \\n OFFSET=0.5, clf2_name=clf2_name, clf2=clf)\\n elif method in (\\\"KNN\\\", \\\"RF\\\"):\\n results += get_mod_freq_clf_train_test(df, cols, chr_pos, [s0, s], samples[1:3], clf, feat_name)\\n else:\\n results += get_mod_freq_clf(df, cols, chr_pos, [s0, s], clf, feat_name)\\n \\n # and store mod_freq predicted by various methods\\n freqs = pd.DataFrame(results, columns=[\\\"chr_pos\\\", \\\"features\\\", \\\"mod_freq wt\\\", \\\"mod_freq strain\\\", \\\"strain\\\"])\\n freqs[\\\"diff\\\"] = freqs.max(axis=1)-freqs.min(axis=1); freqs\\n for name, pos in group2pos.items(): #((\\\"negative\\\", negatives), (\\\"pU\\\", pU_pos), (\\\"Nm\\\", Nm_pos)):\\n freqs.loc[freqs[\\\"chr_pos\\\"].isin(pos), \\\"group\\\"] = name\\n #freqs.to_csv(outfn, sep=\\\"\\\\t\\\"); freqs.head()\\n freqs.to_excel(xls, fname, index=False)\\n # plot differences between methods\\n for group, pos in group2pos.items():\\n freqs.loc[freqs[\\\"chr_pos\\\"].isin(pos), \\\"modification\\\"] = group\\n #return freqs\\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))#, sharey=\\\"all\\\")\\n sns.barplot(x=\\\"chr_pos\\\", y=\\\"mod_freq strain\\\", hue=\\\"strain\\\", edgecolor=\\\"white\\\", palette=[\\\"#f8786fff\\\", \\\"#7aae02ff\\\", \\\"#00bfc2ff\\\", \\\"#c67afeff\\\"], \\n data=freqs[(freqs[\\\"features\\\"]==feat_name)&(freqs[\\\"group\\\"]==\\\"pU\\\")], ax=ax1)\\n sns.barplot(x=\\\"chr_pos\\\", y=\\\"mod_freq strain\\\", hue=\\\"strain\\\", edgecolor=\\\"white\\\", palette=[\\\"#ed823aff\\\", \\\"#1c6ca9ff\\\", \\\"#35d1bbff\\\", \\\"#c978fdff\\\"], \\n data=freqs[(freqs[\\\"features\\\"]==feat_name)&(freqs[\\\"group\\\"]==\\\"Nm\\\")], ax=ax2)\\n ax1.set_ylabel(\\\"Per-site stoichiometry\\\"); ax2.set_ylabel(\\\"\\\")\\n ax1.get_legend().remove(); ax2.get_legend().remove()#ax1.legend([]); ax2.legend([])\\n ax1.set_ylim(0, 1); ax2.set_ylim(0, 1); #ax2.set(aspect=1.7)\\n ax1.set_title(\\\"pU modifications\\\"); ax2.set_title(\\\"Nm modifications\\\")\\n fig.suptitle(fname)\\n fig.savefig(outfn)\\n plt.close() # clear axis\\n freqs[\\\"name\\\"] = fname\\n all_freqs.append(freqs)\\n return all_freqs\",\n \"def output_files(self):\\n # Output file for Moller generation\\n if 'moller' in self.name:\\n return ['moller.stdhep']\\n # Output file for beam generation\\n return ['beam.stdhep']\",\n \"def __init__(self):\\n self.nlp = spacy.load('en_core_web_sm')\\n self.summary_file = open(\\\"results/question_driven_answer_summ_collection_stats.txt\\\", \\\"w\\\", encoding=\\\"utf-8\\\")\",\n \"def getEMPSampleList(self, study_id, web_app_user_id):\\n samples = []\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor()\\n con.cursor().callproc('qiime_assets.get_emp_sample_list', [study_id, web_app_user_id, results])\\n for row in results:\\n # sample_id, avg_emp_score, user_emp_score, sample_name, emp_status, sample_location, \\n # sample_progress, description, altitude, samp_size,\\n # temp, samp_store_temp, country, depth, elevation, env_biome, env_feature, \\n # env_matter, ph, latitude, longitude, chem_administration, samp_store_loc\\n samples.append((row[0], row[1], row[2], row[3], row[4], row[5], row[6], \\n row[7], row[8], row[9], row[10], row[11], row[12], row[13], row[14], \\n row[15], row[16], row[17], row[18], row[19], row[20], row[21], row[22]))\\n \\n \\n return samples\",\n \"def test_multiple_output_files(self):\\r\\n convert_fastq(self.fasta_file_path, self.qual_file_path,\\r\\n multiple_output_files=True,\\r\\n output_directory=self.output_dir,\\r\\n per_file_buffer_size=23)\\r\\n\\r\\n sample_ids = [('PC.634', expected_fastq_634_default),\\r\\n ('PC.354', expected_fastq_354_default),\\r\\n ('PC.481', expected_fastq_481_default)]\\r\\n for sample_id, expected_output in sample_ids:\\r\\n actual_output_file_path = get_filename_with_new_ext(\\r\\n self.fasta_file_path,\\r\\n '_' + sample_id + '.fastq',\\r\\n self.output_dir)\\r\\n\\r\\n actual_output_file = open(actual_output_file_path)\\r\\n actual_output = actual_output_file.read()\\r\\n actual_output_file.close()\\r\\n self._files_to_remove.append(actual_output_file_path)\\r\\n\\r\\n self.assertEquals(actual_output, expected_output)\",\n \"def adjust_body_records_to_samples(self):\\r\\n if len(self.list_of_samples_to_be_combined) == 0:\\r\\n self.determinate_samples_to_be_combined()\\r\\n Body_header_line.list_of_samples_to_be_combined = self.list_of_samples_to_be_combined\\r\\n Body_record.list_of_samples_to_be_combined = self.list_of_samples_to_be_combined\\r\\n for body_object in self.list_of_body_objects:\\r\\n body_object.update_line()\",\n \"def get_samples(paths_to_batches_info, google_bucket_id, sublist=None):\\n paths_to_samples = pd.read_excel(paths_to_batches_info, index_col=0)\\n df_list = []\\n\\n for tsca_id, paths in paths_to_samples.iterrows():\\n if sublist is not None and tsca_id not in sublist:\\n continue\\n # Make data Firecloud-compatible\\n batch_data = prepare_batch_samples_for_metadata_export(paths.path_to_samples_info, tsca_id, google_bucket_id)\\n df_list.append(batch_data)\\n\\n all_samples = pd.concat(df_list, axis=0)\\n \\n # Add cohort codes to data\\n cohort_formatted_names = pd.read_table('cohort_files/cohort_names_dictionary.txt', header=None, names=['Collection', 'cohort_code'])\\n all_samples = pd.merge(all_samples, cohort_formatted_names, on='Collection', how='left')\\n return all_samples\",\n \"def _fetch_data(self, samples):\\n pass\",\n \"def test_multiple_output_files(self):\\r\\n convert_fastaqual(self.fasta_file_path,\\r\\n multiple_output_files=True,\\r\\n output_directory=self.output_dir,\\r\\n per_file_buffer_size=23)\\r\\n\\r\\n sample_id_s = [('PC.634', expected_fasta_634_default,\\r\\n expected_qual_634_default),\\r\\n ('PC.354', expected_fasta_354_default,\\r\\n expected_qual_354_default),\\r\\n ('PC.481', expected_fasta_481_default,\\r\\n expected_qual_481_default)]\\r\\n for sample_id, expected_fasta, expected_qual in sample_id_s:\\r\\n actual_output_fasta_path = get_filename_with_new_ext(\\r\\n self.fasta_file_path,\\r\\n '_' + sample_id + '.fna',\\r\\n self.output_dir)\\r\\n\\r\\n actual_output_qual_path = get_filename_with_new_ext(\\r\\n self.fasta_file_path,\\r\\n '_' + sample_id + '.qual',\\r\\n self.output_dir)\\r\\n\\r\\n actual_output_fasta = open(actual_output_fasta_path)\\r\\n actual_output_qual = open(actual_output_qual_path)\\r\\n actual_fasta = actual_output_fasta.read()\\r\\n actual_output_fasta.close()\\r\\n actual_qual = actual_output_qual.read()\\r\\n actual_output_qual.close()\\r\\n self._files_to_remove.append(actual_output_fasta_path)\\r\\n self._files_to_remove.append(actual_output_qual_path)\\r\\n\\r\\n self.assertEquals(actual_fasta, expected_fasta)\\r\\n self.assertEquals(actual_qual, expected_qual)\",\n \"def test_pipeline_data(self, doc_list):\\n summaries = []\\n for item in doc_list:\\n summaries.append(self.preprocess(item))\\n return summaries\",\n \"def test01Summarize(self):\\n \\n summaryFile = re.sub(\\\"\\\\.csv\\\",\\\"\\\",self.parsedFile)+\\\"_summary.csv\\\"\\n if os.path.exists(summaryFile):\\n os.remove(summaryFile)\\n\\n self.bm.create_summarized(self.parsedFile,uniprot=True)\\n self.assertTrue(os.path.exists(summaryFile))\",\n \"def run_sample(smp: sample.Sample,\\n run_dir: Text,\\n summary_file: Optional[Text] = None,\\n generate_sample_ns: Optional[int] = None):\\n start = time.time()\\n # Create a script named 'run.sh' for rerunning the sample.\\n args = [\\n SAMPLE_RUNNER_MAIN_PATH,\\n '--logtostderr',\\n '--input_file=sample.x',\\n '--options_file=options.pbtxt',\\n ]\\n\\n _write_to_file(run_dir, 'sample.x', smp.input_text)\\n _write_to_file(run_dir, 'options.pbtxt', smp.options.to_pbtxt())\\n args_filename = 'args.txt'\\n _write_to_file(\\n run_dir, args_filename, sample.args_batch_to_text(smp.args_batch)\\n )\\n args.append('--args_file=args.txt')\\n ir_channel_names_filename = None\\n if smp.ir_channel_names is not None:\\n ir_channel_names_filename = 'ir_channel_names.txt'\\n _write_to_file(run_dir, ir_channel_names_filename,\\n sample.ir_channel_names_to_text(smp.ir_channel_names))\\n args.append('--ir_channel_names_file=ir_channel_names.txt')\\n args.append(run_dir)\\n _write_to_file(\\n run_dir,\\n 'run.sh',\\n f'#!/bin/sh\\\\n\\\\n{subprocess.list2cmdline(args)}\\\\n',\\n executable=True)\\n logging.vlog(1, 'Starting to run sample')\\n logging.vlog(2, smp.input_text)\\n runner = sample_runner.SampleRunner(run_dir)\\n runner.run_from_files(\\n 'sample.x', 'options.pbtxt', args_filename, ir_channel_names_filename\\n )\\n timing = runner.timing\\n\\n timing.total_ns = int((time.time() - start) * 1e9)\\n if generate_sample_ns:\\n # The sample generation time, if given, is not part of the measured total\\n # time, so add it in.\\n timing.total_ns += generate_sample_ns\\n timing.generate_sample_ns = generate_sample_ns\\n\\n logging.vlog(1, 'Completed running sample, elapsed: %0.2fs',\\n time.time() - start)\\n\\n if summary_file:\\n _write_ir_summaries(run_dir, timing, summary_file)\",\n \"def generate_sample_sheet(self):\\n pool = self.pool\\n bcl2fastq_sample_ids = []\\n i7_names = []\\n i7_sequences = []\\n i5_names = []\\n i5_sequences = []\\n wells = []\\n plate = pool.container.external_id\\n sample_ids = []\\n sequencer_type = self.sequencer.equipment_type\\n\\n for component in pool.components:\\n lp_composition = component['composition']\\n # Get the well information\\n wells.append(lp_composition.container.well_id)\\n # Get the i7 index information\\n i7_comp = lp_composition.i7_composition.primer_set_composition\\n i7_names.append(i7_comp.external_id)\\n i7_sequences.append(i7_comp.barcode)\\n # Get the i5 index information\\n i5_comp = lp_composition.i5_composition.primer_set_composition\\n i5_names.append(i5_comp.external_id)\\n i5_sequences.append(i5_comp.barcode)\\n # Get the sample id\\n sample_id = lp_composition.normalized_gdna_composition.\\\\\\n gdna_composition.sample_composition.content\\n sample_ids.append(sample_id)\\n\\n # Transform te sample ids to be bcl2fastq-compatible\\n bcl2fastq_sample_ids = [\\n SequencingProcess._bcl_scrub_name(sid) for sid in sample_ids]\\n # Reverse the i5 sequences if needed based on the sequencer\\n i5_sequences = SequencingProcess._sequencer_i5_index(\\n sequencer_type, i5_sequences)\\n\\n data = SequencingProcess._format_sample_sheet_data(\\n bcl2fastq_sample_ids, i7_names, i7_sequences, i5_names,\\n i5_sequences, wells=wells, sample_plate=plate,\\n description=sample_ids, sample_proj=self.run_name,\\n lanes=self.lanes, sep=',')\\n\\n contacts = {c.name: c.email for c in self.contacts}\\n pi = self.principal_investigator\\n principal_investigator = {pi.name: pi.email}\\n sample_sheet_dict = {\\n 'comments': SequencingProcess._format_sample_sheet_comments(\\n principal_investigator=principal_investigator,\\n contacts=contacts),\\n 'IEMFileVersion': '4',\\n 'Investigator Name': pi.name,\\n 'Experiment Name': self.experiment,\\n 'Date': str(self.date),\\n 'Workflow': 'GenerateFASTQ',\\n 'Application': 'FASTQ Only',\\n 'Assay': self.assay,\\n 'Description': '',\\n 'Chemistry': 'Default',\\n 'read1': self.fwd_cycles,\\n 'read2': self.rev_cycles,\\n 'ReverseComplement': '0',\\n 'data': data}\\n return SequencingProcess._format_sample_sheet(sample_sheet_dict)\",\n \"def merge_summaries(root_dir: str,output_file: str=None) -> pd.DataFrame:\\n #\\n print (f'Collecting the available summary files in {root_dir}, can take time... please wait.')\\n sumfiles = glob.glob(f\\\"{root_dir}/**/*smry.txt\\\",recursive=True)\\n nsums = len(sumfiles)\\n print (f\\\"Found {nsums} summary files in {root_dir}\\\")\\n #\\n # will concatenate all smry.txt files into one temporary file and then will put it in pandas DataFrame and \\n # save as CSV\\n #\\n with tempfile.NamedTemporaryFile(mode='w') as fp:\\n for sumfile in tqdm(sumfiles,desc='Collecting the summaries'):\\n with open(sumfile,'r') as sfile:\\n fp.write(sfile.read())\\n #\\n # now read as pandas dataframe\\n #\\n colnames = [\\\"rev\\\",\\\"obsid\\\",\\\"expid\\\",\\\"mode\\\",\\\"filt\\\",\\\"tstart\\\",\\\"tend\\\",\\\"texpo\\\",\\\"mvcratio\\\", # (a rough measure of the ratio of counts in the MnKa versus continuum)\\n \\\"qboxt0\\\",\\\"qboxt1\\\",\\\"qboxt2\\\",\\\"qboxt3\\\", # x 4 (electronics quadrant box temperatures)\\n \\\"ndisclin_mean0\\\",\\\"ndisclin_mean1\\\",\\\"ndisclin_mean2\\\",\\\"ndisclin_mean3\\\", #x 4\\n \\\"mipsel0\\\",\\\"mipsel1\\\",\\\"mipsel2\\\",\\\"mipsel3\\\", #x 4 (parameter for on-board MIP rejection algorithm)\\n \\\"maxmip0\\\",\\\"maxmip1\\\",\\\"maxmip2\\\",\\\"maxmip3\\\", #x 4 (parameter for on-board MIP rejection algorithm)\\n \\\"ndisclin_med0\\\",\\\"ndisclin_med1\\\",\\\"ndisclin_med2\\\",\\\"ndisclin_med3\\\", #median x 4\\n \\\"ndisclin_std0\\\",\\\"ndisclin_std1\\\",\\\"ndisclin_std2\\\",\\\"ndisclin_std3\\\"] #, stddev x 4\\n\\n df = pd.read_csv(fp.name,delimiter='\\\\s+',header=None,skip_blank_lines=True,names=colnames)\\n #\\n # now calculate the time_delta, the difference in years from observation start and 2000-01-01\\n #\\n stime = [(datetime.strptime(x,\\\"%Y-%m-%dT%H:%M:%S\\\")-time0).total_seconds()/(365.0*24.0*3600.0) for x in df.tstart]\\n df.insert(6,\\\"delta_time\\\",pd.Series(stime,index=df.index))\\n #\\n print (f'Last observation t={df.delta_time.max():.2f} years')\\n if (output_file is not None):\\n df.to_csv(output_file)\\n fp.close()\\n return df\",\n \"def samples(\\n self,\\n pcollection_ids: Optional[Iterable[str]] = None\\n ) -> beam_fn_api_pb2.SampleDataResponse:\\n ret = beam_fn_api_pb2.SampleDataResponse()\\n\\n with self._samplers_lock:\\n samplers = self._samplers.copy()\\n\\n for pcoll_id in samplers:\\n if pcollection_ids and pcoll_id not in pcollection_ids:\\n continue\\n\\n samples = samplers[pcoll_id].flush()\\n if samples:\\n ret.element_samples[pcoll_id].elements.extend(samples)\\n\\n return ret\",\n \"def pull_sample_data(self, sample_id: str, sample_size: int or None = None, include_controls: bool = True,\\n output_format: str = 'dataframe', columns_default: str = 'marker') -> None or list:\\n db = connection.get_db(alias='core')\\n db_name = db.name\\n file_grp = self.pull_sample(sample_id)\\n if not file_grp:\\n return None\\n files = file_grp.files\\n # Fetch data\\n if not include_controls: # Fetch data for primary file only\\n file_id = [f for f in files if f.file_type == 'complete'][0].file_id\\n connection.disconnect('core')\\n connection._connections = {}\\n connection._connection_settings = {}\\n connection._dbs = {}\\n FileGroup._collection = None\\n\\n complete = data_from_file(file_id=file_id,\\n filegrp_id=file_grp.id,\\n db_name=db_name,\\n sample_size=sample_size,\\n output_format=output_format,\\n columns_default=columns_default)\\n connection.connect(db=db_name, alias='core')\\n return [complete]\\n # Fetch data for primary file & controls\\n files = [f.file_id for f in file_grp.files]\\n connection.disconnect('core')\\n connection._connections = {}\\n connection._connection_settings = {}\\n connection._dbs = {}\\n FileGroup._collection = None\\n\\n pool = Pool(cpu_count())\\n f = partial(data_from_file,\\n filegrp_id=file_grp.id,\\n db_name=db_name,\\n sample_size=sample_size,\\n output_format=output_format,\\n columns_default=columns_default)\\n data = pool.map(f, files)\\n pool.close()\\n pool.join()\\n connection.connect(db=db_name, alias='core')\\n return data\",\n \"def features_combine():\\n\\n\\n\\t# PROCESSING AUDIO\",\n \"def app_SN_plot_all_active_sample(self, sample_list: List, phases):\\n\\n for sample in sample_list:\\n sample_name = sample['sample_name']\\n print(f'processing: {sample_name}')\\n dfs, phase_dirs, sizer_val, styler_val = self.preprocessing_before_plotting(sample, phases=phases)\\n\\n self.app_SN_combined_master(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_combined_temperature_series(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_combined_temperature_gradient(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_combined_moisture_series(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_combined_moisture_gradient(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_combined_series_moist_vs_temp(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_separate_temperature_series(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_separate_temperature_gradient(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_separate_moisture_series(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_separate_moisture_gradient(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_separate_series_moist_vs_temp(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_last_24h_plots(dfs, phase_dirs, sizer_val, styler_val)\\n self.app_SN_hot_end_temperature(dfs, phase_dirs, sizer_val, styler_val)\",\n \"def samples_path():\\n dir = os.path.dirname(os.path.abspath(__file__))\\n samples = os.path.join(dir, 'samples')\\n return samples\",\n \"def store_overall_means(src_file: H5File) -> None:\\n perp_sum = 0\\n par_sum = 0\\n ref_sum = 0\\n counts = 0\\n for path in rawnav.pump_group_paths(src_file):\\n perp_path = path + '/perp'\\n par_path = path + '/par'\\n ref_path = path + '/ref'\\n perp_sum += src_file[perp_path].attrs['mean']\\n par_sum += src_file[par_path].attrs['mean']\\n ref_sum += src_file[ref_path].attrs['mean']\\n counts += 1\\n src_file.attrs['perp_mean'] = perp_sum / counts\\n src_file.attrs['par_mean'] = par_sum / counts\\n src_file.attrs['ref_mean'] = ref_sum / counts\\n return\",\n \"def final_output_analysis(samples_dict, dir_results_path):\\n with open(path.join(dir_results_path, 'corrupted_processes.txt'), 'w', encoding='utf-8', errors='replace') as c_out:\\n with open(path.join(dir_results_path, 'analysis.txt'), 'w', encoding='utf-8', errors='replace') as i_out:\\n with open(path.join(dir_results_path, 'syscalls.txt'), 'w', encoding='utf-8', errors='replace') as s_out:\\n for uuid in sorted(samples_dict.keys()):\\n reduced_sample = samples_dict[uuid]\\n\\n i_out.write('{} {}\\\\n'.format(string_utils.filename, uuid))\\n s_out.write('{} {}\\\\n'.format(string_utils.filename, uuid))\\n c_out.write('{} {}\\\\n'.format(string_utils.filename, uuid))\\n\\n # corrupted processes section\\n process_repr = '\\\\t\\\\t{:15s}\\\\t{:10d}\\\\t{:15s}\\\\tby:\\\\t{:15s}\\\\t{:10d}\\\\n'\\n for process in reduced_sample.corrupted_processes:\\n c_out.write(process_repr.format(process[0],\\n process[1],\\n process[2],\\n process[3],\\n process[4]))\\n\\n # instruction count section\\n i_out.write(string_utils.out_final + '\\\\t' + str(reduced_sample.total_instruction) + '\\\\n')\\n i_out.write(string_utils.out_terminating + '\\\\t' + str(reduced_sample.terminate_all) + '\\\\t')\\n i_out.write(string_utils.out_sleeping + '\\\\t' + str(reduced_sample.sleep_all) + '\\\\t')\\n i_out.write(string_utils.out_crashing + '\\\\t' + str(reduced_sample.crash_all) + '\\\\t')\\n i_out.write(string_utils.out_raising_error + '\\\\t' + str(reduced_sample.error_all) + '\\\\t')\\n i_out.write(string_utils.out_writes_file + '\\\\t' + str(reduced_sample.write_file) + '\\\\n')\\n\\n # system calls count section\\n s_out.write(string_utils.syscall_final + '\\\\t' + str(reduced_sample.total_syscalls) + '\\\\n')\\n\\n i_out.write('\\\\n')\\n s_out.write('\\\\n')\\n c_out.write('\\\\n')\",\n \"def extract_info_from_arguments(self):\\r\\n\\r\\n for sample_name in self.arguments['--sample_name']:\\r\\n self.list_of_samples_to_be_combined.append(sample_name)\\r\\n\\r\\n for file_path in self.arguments['--input_file']:\\r\\n file_object = Input_file(file_path, self.list_of_samples_to_be_combined)\\r\\n self.indices.update(file_object.indices)\\r\\n self.list_of_input_files.append(file_object)\\r\\n self.list_of_input_files_paths.append(file_path)\\r\\n\\r\\n if self.arguments['--out']:\\r\\n if self.arguments['--output_format'] == 'COMPRESSED':\\r\\n self.compressed = True\\r\\n elif self.arguments['--output_format'] == 'UNCOMPRESSED':\\r\\n self.compressed = False\\r\\n else:\\r\\n if self.list_of_input_files[0].compressed:\\r\\n self.compressed = True\\r\\n else:\\r\\n self.compressed = False\",\n \"def _generate_examples(self, files):\\n idx = 0\\n for filename in files:\\n with open(filename) as file:\\n for line in file:\\n yield idx, {\\\"text\\\": line}\\n idx += 1\",\n \"def samples(self, groups=None):\\n groups = check_parameters_for_validity(\\n groups, \\\"groups\\\", self.groups(), self.groups()\\n )\\n all_samples = []\\n\\n if \\\"train\\\" in groups:\\n all_samples.extend(self.background_model_samples())\\n groups.remove(\\\"train\\\")\\n\\n for grp in groups:\\n all_samples.extend(_sample_sets_to_samples(self.references(grp)))\\n all_samples.extend(_sample_sets_to_samples(self.probes(grp)))\\n\\n # Add znorm samples. Returning znorm samples for one group of dev or\\n # eval is enough because they are duplicated.\\n for grp in groups:\\n all_samples.extend(_sample_sets_to_samples(self.zprobes(grp)))\\n break\\n\\n # Add tnorm samples.\\n all_samples.extend(_sample_sets_to_samples(self.treferences()))\\n\\n return all_samples\",\n \"def main(output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n\\n baseurl = 'http://codeandbeer.org/virtual/BigData/Labs/'\\n files = ['Booking-20151012-1322.csv', 'Booking-20181025-1232.csv']\\n for filename in files:\\n r = requests.get(baseurl+filename, stream=True)\\n if r.status == 200:\\n with open(output_filepath+\\\"/\\\"+filename, \\\"wb\\\") as f:\\n f.raw.decode_content = True\\n shutil.copyfileobj(r.raw, f)\",\n \"def get_nested_samples(self, filename='nested_samples.dat'):\\n import numpy.lib.recfunctions as rfn\\n self.nested_samples = rfn.stack_arrays(\\n [s.asnparray()\\n for s in self.NS.nested_samples]\\n ,usemask=False)\\n if filename:\\n np.savetxt(os.path.join(\\n self.NS.output_folder, filename),\\n self.nested_samples.ravel(),\\n header=' '.join(self.nested_samples.dtype.names),\\n newline='\\\\n',delimiter=' ')\\n return self.nested_samples\",\n \"def main(input_folder, output_folder, bounding_boxes_file, cores, resampling,\\n order):\\n logger = logging.getLogger(__name__)\\n logger.info('Resampling')\\n\\n if not os.path.exists(output_folder):\\n os.mkdir(output_folder)\\n print('resampling is {}'.format(str(resampling)))\\n bb_df = pd.read_csv(bounding_boxes_file)\\n bb_df = bb_df.set_index('PatientID')\\n files_list = [\\n f for f in glob.glob(input_folder + '/**/*.nii.gz', recursive=True)\\n ]\\n resampler = Resampler(bb_df, output_folder, order, resampling=resampling)\\n with Pool(cores) as p:\\n p.map(resampler, files_list)\",\n \"def _collect_all(self):\",\n \"def collect_data(base='.'):\\n files = glob(join(base, 'accuracy_*.json'))\\n data = {}\\n for file in files:\\n fields = basename(file).split('_')\\n fields = fields[1:-2]\\n accuracy = load(open(file))\\n weights = load(open(file.replace('accuracy', 'weights')))\\n for model in accuracy.keys():\\n # add both accuracy and weights\\n key = tuple(fields + [model])\\n value = accuracy[model] + weights[model]\\n data[key] = value\\n return data\",\n \"def test_get_file_copy_list(self):\\n \\n so = sys.stdout\\n dn = open(os.devnull,\\\"w\\\")\\n \\n # Create a file hierarchy to search for files\\n root = tempfile.mkdtemp(prefix=\\\"test_casava_data_delivery_\\\")\\n date = \\\"111111\\\"\\n fcs = [\\\"{}_{}\\\".format(date,fcid) for fcid in [\\\"FCA\\\",\\\"FCB\\\"]]\\n \\n # Create some sample files\\n exp_files = []\\n samples = []\\n for n in xrange(2):\\n sample = tempfile.mkdtemp(dir=root)\\n samples.append(os.path.basename(sample))\\n for fcid in fcs:\\n fcdir = os.path.join(sample,fcid)\\n nophixdir = os.path.join(fcdir,\\\"nophix\\\")\\n for d in [fcdir,nophixdir]:\\n os.makedirs(d)\\n test_names = [\\\"{:d}_{:s}_1_1_fastq.txt.gz\\\".format(random.randint(1,8),\\n fcid),\\n \\\"{}_CGATGT_L001_R1_001.fastq.gz\\\".format(samples[-1]),\\n \\\"{}_CGATGT_L001_R1_001.fastq..gz\\\".format(samples[-1]),]\\n for test_name in test_names:\\n test_file = os.path.join(d,test_name)\\n open(test_file,\\\"w\\\").close()\\n exp_files.append([samples[-1],\\n fcid,\\n os.path.basename(d) == \\\"nophix\\\",\\n test_file,\\n os.path.join(samples[-1],fcid),\\n create_final_name(os.path.basename(test_name),date,fcid.split(\\\"_\\\")[-1],samples[-1])])\\n \\n # Get the list of files to copy under various conditions\\n \\n for deliver_all_fcs in [False, True]:\\n for fcid in fcs:\\n for deliver_nophix in [False, True]:\\n for skip_sample_list in [[],[samples[0]],[samples[1]],samples]:\\n sys.stdout = dn\\n obs_to_copy = sorted(get_file_copy_list(root,\\\"\\\",fcid,deliver_all_fcs,deliver_nophix,skip_sample_list))\\n sys.stdout = so\\n exp_to_copy = sorted([ef[3:6] for ef in exp_files if (deliver_all_fcs or ef[1] == fcid) and \\\\\\n deliver_nophix == ef[2] and \\\\\\n ef[0] not in skip_sample_list])\\n #import pdb; pdb.set_trace()\\n self.assertListEqual(obs_to_copy,\\n exp_to_copy,\\n \\\"The files to copy result did not match the expected for \\\" \\\\\\n \\\"{:s}\\\".format(\\\", \\\".join([\\\"{:s}: {:s}\\\".format(k,v) for k, v in \\\\\\n dict(zip([\\\"deliver_all_fcs\\\",\\n \\\"fcid\\\",\\n \\\"deliver_nophix\\\",\\n \\\"skip_samples\\\"],\\n [str(deliver_all_fcs),\\n fcid,\\n str(deliver_nophix),\\n \\\" \\\".join(skip_sample_list)])).items()])))\",\n \"def test_split_fasta_on_sample_ids_to_files(self):\\r\\n temp_output_dir = mkdtemp()\\r\\n self.dirs_to_remove.append(temp_output_dir)\\r\\n\\r\\n split_fasta_on_sample_ids_to_files(\\r\\n parse_fasta(self.fasta2),\\r\\n output_dir=temp_output_dir,\\r\\n per_sample_buffer_size=2)\\r\\n self.files_to_remove.extend(glob('%s/*fasta' % temp_output_dir))\\r\\n\\r\\n # confirm that all files are as expected\\r\\n self.assertEqual(open('%s/Samp1.fasta' % temp_output_dir).read(),\\r\\n \\\">Samp1_42\\\\nACCGGTT\\\\n>Samp1_43 some comme_nt\\\\nAACCG\\\\n>Samp1_44\\\\nA\\\\n\\\")\\r\\n self.assertEqual(open('%s/s2_a.fasta' % temp_output_dir).read(),\\r\\n \\\">s2_a_50\\\\nGGGCCC\\\\n\\\")\\r\\n self.assertEqual(open('%s/s3.fasta' % temp_output_dir).read(),\\r\\n \\\">s3_25\\\\nAAACCC\\\\n\\\")\\r\\n # confirm number of files is as expected\\r\\n self.assertEqual(len(glob('%s/*' % temp_output_dir)), 3)\",\n \"def fixture_samples(sample_single) -> Iterator[dict]:\\n _samples = []\\n sample_id = sample_single[\\\"sample_id\\\"]\\n for number in range(3):\\n sample = copy.deepcopy(sample_single)\\n sample[\\\"sample_id\\\"] = \\\"_\\\".join([sample_id, str(number)])\\n _samples.append(sample)\\n return _samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6286086","0.61118436","0.5901913","0.5859824","0.5801021","0.57871133","0.57731056","0.57341135","0.5628486","0.5589119","0.55838966","0.5580174","0.55663544","0.5565593","0.5554349","0.5533319","0.5505845","0.5455812","0.54384106","0.54338044","0.5421907","0.54131556","0.5412779","0.5400555","0.5393032","0.53882027","0.5384524","0.5355982","0.5347648","0.5346229","0.5340677","0.5309327","0.5295293","0.5294872","0.5289483","0.52834135","0.52732605","0.5270524","0.52702177","0.5268191","0.5264975","0.52593696","0.524985","0.5245488","0.5230109","0.5217889","0.52058953","0.52056867","0.5201352","0.5200033","0.5183609","0.5183085","0.5179869","0.5179869","0.517032","0.5169661","0.516871","0.51679546","0.5167331","0.51548773","0.5154324","0.51536155","0.51471883","0.51441425","0.51387584","0.51361513","0.51350856","0.5118891","0.51139265","0.51123434","0.51122373","0.5097918","0.50957096","0.50936157","0.5093366","0.5091957","0.50882685","0.50874555","0.50820535","0.5081025","0.50791645","0.50744754","0.50702035","0.50685817","0.5066349","0.5066097","0.5049766","0.50487924","0.5046008","0.50409955","0.5038979","0.5038522","0.5036728","0.5036081","0.5033062","0.50288975","0.5027657","0.5026108","0.50160265","0.50154114"],"string":"[\n \"0.6286086\",\n \"0.61118436\",\n \"0.5901913\",\n \"0.5859824\",\n \"0.5801021\",\n \"0.57871133\",\n \"0.57731056\",\n \"0.57341135\",\n \"0.5628486\",\n \"0.5589119\",\n \"0.55838966\",\n \"0.5580174\",\n \"0.55663544\",\n \"0.5565593\",\n \"0.5554349\",\n \"0.5533319\",\n \"0.5505845\",\n \"0.5455812\",\n \"0.54384106\",\n \"0.54338044\",\n \"0.5421907\",\n \"0.54131556\",\n \"0.5412779\",\n \"0.5400555\",\n \"0.5393032\",\n \"0.53882027\",\n \"0.5384524\",\n \"0.5355982\",\n \"0.5347648\",\n \"0.5346229\",\n \"0.5340677\",\n \"0.5309327\",\n \"0.5295293\",\n \"0.5294872\",\n \"0.5289483\",\n \"0.52834135\",\n \"0.52732605\",\n \"0.5270524\",\n \"0.52702177\",\n \"0.5268191\",\n \"0.5264975\",\n \"0.52593696\",\n \"0.524985\",\n \"0.5245488\",\n \"0.5230109\",\n \"0.5217889\",\n \"0.52058953\",\n \"0.52056867\",\n \"0.5201352\",\n \"0.5200033\",\n \"0.5183609\",\n \"0.5183085\",\n \"0.5179869\",\n \"0.5179869\",\n \"0.517032\",\n \"0.5169661\",\n \"0.516871\",\n \"0.51679546\",\n \"0.5167331\",\n \"0.51548773\",\n \"0.5154324\",\n \"0.51536155\",\n \"0.51471883\",\n \"0.51441425\",\n \"0.51387584\",\n \"0.51361513\",\n \"0.51350856\",\n \"0.5118891\",\n \"0.51139265\",\n \"0.51123434\",\n \"0.51122373\",\n \"0.5097918\",\n \"0.50957096\",\n \"0.50936157\",\n \"0.5093366\",\n \"0.5091957\",\n \"0.50882685\",\n \"0.50874555\",\n \"0.50820535\",\n \"0.5081025\",\n \"0.50791645\",\n \"0.50744754\",\n \"0.50702035\",\n \"0.50685817\",\n \"0.5066349\",\n \"0.5066097\",\n \"0.5049766\",\n \"0.50487924\",\n \"0.5046008\",\n \"0.50409955\",\n \"0.5038979\",\n \"0.5038522\",\n \"0.5036728\",\n \"0.5036081\",\n \"0.5033062\",\n \"0.50288975\",\n \"0.5027657\",\n \"0.5026108\",\n \"0.50160265\",\n \"0.50154114\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57615197"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":11580,"cells":{"query":{"kind":"string","value":"Builds the sbatch file in order to combine genomics.vcf samples contained in current_batch in a single one."},"document":{"kind":"string","value":"def build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, scratch=False, interval=None):\n \n name_batch1 = os.path.basename([item for item in combined_gvcf_files if \"batch1\" in item][0])\n interval_name = \"\"\n #there must be at least one batch so look for it, not elegant but works\n if name_batch1.split(\"batch1\") != \".g.vcf.gz\":\n interval_name = name_batch1.split(\"batch1\")[1].split(\".\")[0]\n job_name = \"GenotypeGVCFs{}\".format(interval_name)\n output_file = \"{}_joincalled{}.g.vcf.gz\".format(CONFIG[\"output_header\"], interval_name)\n #create the sbatch file to analyse the current batch of samples\n sbatch_file = os.path.join(working_dir, \"sbatch\", \"{}.sbatch\".format(job_name))\n with open(sbatch_file, \"w\") as GenotypeGVCFs:\n slurm = slurm_header(CONFIG[\"uppmax_project\"], job_name, working_dir)\n GenotypeGVCFs.write(slurm)\n GenotypeGVCFs.write(\"\\n\")\n #rsync to scratch all samples\n if scratch:\n GenotypeGVCFs.write(\"mkdir -p $SNIC_TMP/{} \\n\".format(job_name)) # create tmp directory\n GenotypeGVCFs.write(\"mkdir -p $SNIC_TMP/{}/VCF/ \\n\".format(job_name)) # create tmp directory\n #now cycle over the samples, build the GATK command\n combined_gvcf_string_input = \"\"\n for combined_gvcf in combined_gvcf_files:\n combined_gvcf_path_dir = combined_gvcf\n if scratch:\n GenotypeGVCFs.write(\"rsync -rptoDLv {}* $SNIC_TMP/{}/\\n\".format(combined_gvcf, job_name))\n combined_gvcf_name = os.path.basename(combined_gvcf)\n combined_gvcf_path_dir = \"$SNIC_TMP/{}/{}\".format(job_name, combined_gvcf_name)\n combined_gvcf_string_input += \"-V {} \\\\\\n\".format(combined_gvcf_path_dir)\n\n GATK_command= \"java -Xmx250g -jar {} -T GenotypeGVCFs \\\\\\n\".format(CONFIG[\"GATK\"])\n for option in CONFIG[\"walkers\"][\"GenotypeGVCFs\"]:\n GATK_command += \"{} \\\\\\n\".format(option)\n GATK_command += \"{} \".format(combined_gvcf_string_input)\n if interval is not None:\n GATK_command += \"-L {} \\\\\\n\".format(interval)\n\n if scratch:\n GATK_command += \"-o $SNIC_TMP/{}/VCF/{}\\n\".format(job_name, output_file)\n #once this is done rsync back to lupus\n GATK_command += \"rsync $SNIC_TMP/{}/VCF/{}* {}/VCF/\\n\".format(job_name, output_file , working_dir)\n else:\n GATK_command += \"-o {}/VCF/{}\\n\\n\".format(working_dir, output_file)\n GenotypeGVCFs.write(GATK_command)\n #return path to sbach file\n return sbatch_file"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def GenotypeGVCFs():\n #creates sbatch files to merge batches of batch_size genomics vcf\n cwd = os.getcwd()\n sbatch_files = []\n if not os.path.isdir(os.path.join(cwd, \"01_CombineGVCFs\")):\n sys.exit(\"Directory 01_CombineGVCFs does not exits exists, something went wrong here.\")\n if os.path.isdir(os.path.join(cwd, \"02_GenotypeGVCFs\")):\n print \"WARNING: 02_GenotypeGVCFs already present, assuming this step has been completed with success.\"\n return sbatch_files\n else:\n #create the folder structure\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"sbatch\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"std_err\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"std_out\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"VCF\"))\n #Build the sbatch files for the join calling step\n working_dir = os.path.join(cwd, \"02_GenotypeGVCFs\")\n #now retrive the VCF stored in 01_CombineGVCFs/VCF/\n combined_gvcfs_to_process = []\n if len(CONFIG[\"intervals_list\"]) == 0:\n #no intervals, I have one file for each batch\n combined_gvcf_files = []\n for current_batch in range(1, CONFIG[\"batch_number\"] +1):\n # for each batch create the vcf file that need to be created by combine step\n combined_gvcf_name = \"{}_batch{}.g.vcf.gz\".format(CONFIG[\"output_header\"], current_batch)\n combined_gvcf_full_path = os.path.join(cwd, \"01_CombineGVCFs\", \"VCF\", combined_gvcf_name)\n combined_gvcf_files.append(combined_gvcf_full_path)\n combined_gvcfs_to_process.append(combined_gvcf_files)\n else:\n for interval in CONFIG[\"intervals_list\"]:\n interval_name = os.path.basename(interval).split(\".\")[0]\n combined_gvcf_files = []\n for current_batch in range(1, CONFIG[\"batch_number\"] +1):\n # for each batch create the vcf file that need to be created by combine step\n combined_gvcf_name = \"{}_batch{}_{}.g.vcf.gz\".format(CONFIG[\"output_header\"], current_batch, interval_name)\n combined_gvcf_full_path = os.path.join(cwd, \"01_CombineGVCFs\", \"VCF\", combined_gvcf_name)\n combined_gvcf_files.append(combined_gvcf_full_path)\n #now ceate a list with interval file and all gvcf to be combines\n interval_plus_gvcfs = [interval ,combined_gvcf_files]\n combined_gvcfs_to_process.append(interval_plus_gvcfs)\n for interval_plus_gvcfs in combined_gvcfs_to_process:\n interval = interval_plus_gvcfs[0]\n combined_gvcf_files = interval_plus_gvcfs[1]\n sbatch_file = build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, CONFIG[\"scratch\"], interval)\n sbatch_files.append(sbatch_file)\n return sbatch_files","def build_batches(self):\n\n # get directories for all batches and logs\n batches_dir = join(self.path, 'batches')\n logs_dir = join(self.path, 'log')\n\n # create index file for batches\n index_path = join(batches_dir, 'index.txt')\n index = open(index_path, 'w')\n\n # write file containing simulation paths for each batch\n for i, simulation_path in self.simulation_paths.items():\n\n # determine batch ID\n batch_id = i // self.batch_size\n\n # process new batch\n if i % self.batch_size == 0:\n\n # open batch file and append to index\n batch_path = join(batches_dir, '{:d}.txt'.format(batch_id))\n index.write('{:s}\\n'.format(relpath(batch_path, self.path)))\n batch_file = open(batch_path, 'w')\n\n # create log directory for batch\n mkdir(join(logs_dir, '{:d}'.format(batch_id)))\n\n # write paths to batch file\n batch_file.write('{:s}\\n'.format(simulation_path))\n\n # close batch file\n if i % self.batch_size == (self.batch_size - 1):\n batch_file.close()\n chmod(batch_path, 0o755)\n\n index.close()\n\n chmod(index_path, 0o755)","def build(self):\n writer = None\n out_complete = ''\n\n reader = self.module_loader.get_reader()()\n reader.set_configs(self.configs)\n reader.verify_parameters()\n input_path = self.configs.get_resolved('parameters', 'input', 'path')\n output_path = self.configs.get_resolved('parameters', 'output', 'path')\n pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)\n \n files = reader.fetch_input_files(input_path)\n\n for i, group in enumerate(files):\n\n obs = self.params('output_block_size') if self.configs.exists('parameters', 'output_block_size') else 1\n if obs is None:\n obs = len(files)\n\n first_of_batch = (i % obs == 0)\n\n if first_of_batch:\n output_name = reader.output_filename(group['id'])\n writer = self.module_loader.get_writer()(output_path, output_name)\n out_complete = writer.file_path()\n\n Logger.log('started_r_files', group['files'])\n\n with writer.appending(not first_of_batch) as dataset:\n Logger.log('writing_file', out_complete, '' if first_of_batch else '(appending)')\n \n self.read_attributes(dataset)\n self.read_variables(dataset)\n \n if reader.data_grouping:\n complete_path = tuple([path.join(input_path, f) for f in group['files']])\n else:\n complete_path = path.join(input_path, group['files'])\n reader.read_to(dataset, complete_path, self.configs, not first_of_batch)\n\n Logger.info('done')","def _fillBatches(self):\n\n batchRE = r\"\"\"\n B\n (?P<observebatch>\\d+?)\n (?P<startend>[SE])\n (?P<sequence>\\d+?)\n _SR\n (?:_(?P<extraInjections>\\d+?|\\w+?))?\n $\n \"\"\"\n batchRE = re.compile(batchRE, re.VERBOSE)\n # We canot infer batches unless we have runorder\n if 'Run Order' in self.sampleMetadata.keys():\n currentBatch = 0\n # Loop over samples in run order\n for index, row in self.sampleMetadata.sort_values(by='Run Order').iterrows():\n nameComponents = batchRE.search(row['Sample File Name'])\n if nameComponents:\n # Batch start\n if nameComponents.group('startend') == 'S':\n # New batch - increment batch no\n if nameComponents.group('sequence') == '1':\n currentBatch = currentBatch + 1\n\n # Don't include the dilution series or blanks\n if not ((row['AssayRole'] == AssayRole.LinearityReference) or (row['SampleType'] == SampleType.ProceduralBlank)):\n self.sampleMetadata.loc[index, 'Batch'] = currentBatch\n self.sampleMetadata.loc[index, 'Correction Batch'] = currentBatch\n\n else:\n warnings.warn('Unable to infer batches without run order, skipping.')\n return","def run(self):\n lineage_csv_gz = self.input_files_local[0][0]\n output_db = self.output_files_local()[0]\n log.write(f\"input: {lineage_csv_gz} output: {output_db}\")\n\n with IdSeqDictForUpdate(output_db, IdSeqDictValue.VALUE_TYPE_ARRAY) as lineage_dict:\n batch_list = {}\n with gzip.open(lineage_csv_gz, \"rt\") as gzf:\n for line in gzf:\n fields = line.rstrip().split(\",\")\n taxid = fields[0]\n species, genus, family = fields[-1:-4:-1]\n batch_list[taxid] = [species, genus, family]\n if len(batch_list) >= BATCH_INSERT_SIZE:\n lineage_dict.batch_inserts(batch_list.items())\n batch_list = {}\n lineage_dict.batch_inserts(batch_list.items())","def createScript_sbatch(self):\n tools_createScript_sbatch(\n sbatch_script_file_name = self.sbatchFile_addMEM,\n executable = self.executable_addMEM,\n command_line_parameters = self.cfgFiles_addMEM_modified,\n input_file_names = self.inputFiles,\n output_file_names = self.outputFiles,\n script_file_names = self.shFiles_addMEM_modified,\n log_file_names = self.logFiles_addMEM,\n keep_logs = False,\n working_dir = self.workingDir,\n max_num_jobs = 100000000, # it's really silly to limit the number of jobs; use an enormous number as the ,,fix''\n cvmfs_error_log = self.cvmfs_error_log,\n pool_id = self.pool_id,\n use_home = self.use_home,\n validate_outputs = self.check_output_files,\n max_num_submittedJobs = 2000,\n )","def gen_vars(input_fqs):\n k_sizes = range(*CONFIG['abyss_bloom']['k_mer_sizes'])\n sr = re.search(PATH_RE, input_fqs[0])\n sr2 = re.search(PATH_RE, input_fqs[1])\n # should be of conventional directory hierarchy\n try:\n assert sr.groups() == sr2.groups()\n except AssertionError:\n print '{0} != {1}'.format(sr.groups(), sr2.groups())\n raise\n\n bfs, bf_flags, fas, fa_flags = [], [], [], []\n for k_size in k_sizes:\n # for abyss_bloom\n # bn: basename\n bf_bn = '{0}_k{1}.bf.gz'.format(sr.group('celltype'), k_size)\n bf_flag_bn = '{0}.SUCCESS'.format(bf_bn)\n bf_dir = os.path.join(sr.group('prefix'), 'kon', sr.group('chr'), 'bf')\n bf = os.path.join(bf_dir, bf_bn)\n bf_flag = os.path.join(bf_dir, bf_flag_bn)\n bfs.append(bf)\n bf_flags.append(bf_flag)\n\n # for konnector\n fa_all_bn = '{0}_k{1}_allpaths.fa.gz'.format(sr.group('celltype'), k_size)\n fa_mer_bn = '{0}_k{1}_merged.fa.gz'.format(sr.group('celltype'), k_size)\n fa_flag_bn = '{0}_k{1}.SUCCESS'.format(sr.group('celltype'), k_size)\n fa_dir = os.path.join(sr.group('prefix'), 'kon', sr.group('chr'), 'fafq')\n fa_all = os.path.join(fa_dir, fa_all_bn)\n fa_mer = os.path.join(fa_dir, fa_mer_bn)\n fa_flag = os.path.join(fa_dir, fa_flag_bn)\n fas.extend([fa_all, fa_mer])\n fa_flags.append(fa_flag)\n\n return k_sizes, bfs, bf_flags, fas, fa_flags","def batch_process(in_batch, in_ref, merge_stats=True, dir_counts='', dir_stats='',\r\n in_counts=None, in_stats=None, save='all', out_folder='',\r\n out_prefix='', return_df=None):\r\n\r\n # import ref files and define variables/paths\r\n path = Path.cwd()\r\n df_ref = pd.read_csv(in_ref)\r\n if 'sgRNA_seq' not in df_ref.columns.tolist():\r\n raise Exception('in_ref is missing column: sgRNA_seq')\r\n df_batch = pd.read_csv(in_batch)\r\n list_reqcols = ['sample_id', 'fastq_file', 'condition']\r\n list_batchcols = df_batch.columns.tolist()\r\n if not all(col in list_batchcols for col in list_reqcols):\r\n list_miss = [col for col in list_reqcols if col not in list_batchcols]\r\n raise Exception('Error! in_batch is missing column(s): ' + str(list_miss))\r\n if 't0' not in df_batch['condition'].tolist():\r\n raise Exception('t0 condition not found in the in_batch file')\r\n # defaults to cwd if subdir == ''\r\n counts_path = path / dir_counts\r\n stats_path = path / dir_stats\r\n if in_counts is None:\r\n df_batch['counts_files'] = df_batch['sample_id'] + '_counts.csv'\r\n else:\r\n df_temp = pd.DataFrame(in_counts, columns=['sample_id', 'counts_files'])\r\n df_batch = df_batch.merge(df_temp, on='sample_id', how='left')\r\n if in_stats is None:\r\n df_batch['stats_files'] = df_batch['sample_id'] + '_stats.txt'\r\n else:\r\n df_temp = pd.DataFrame(in_stats, columns=['sample_id', 'stats_files'])\r\n df_batch = df_batch.merge(df_temp, on='sample_id', how='left')\r\n\r\n # import csv files and generate dfs for raw reads and log2 norm\r\n df_reads, df_log2 = df_ref.copy(), df_ref.copy()\r\n for row in df_batch.itertuples():\r\n file = counts_path / row.counts_files\r\n df_temp = pd.read_csv(file, names=['sgRNA_seq', row.sample_id])\r\n # merge on sgRNA_seq to aggregate columns\r\n df_reads = pd.merge(df_reads, df_temp, on='sgRNA_seq')\r\n # perform log2 normalization (brian/broad method)\r\n total_reads = df_reads[row.sample_id].sum()\r\n df_log2[row.sample_id] = df_reads[row.sample_id].apply(lambda x: np.log2((x * 1000000 / total_reads) + 1))\r\n\r\n # perform t0 normalization\r\n df_t0 = df_ref.copy()\r\n t0 = df_batch.loc[df_batch['condition'] == 't0']['sample_id']\r\n if t0.shape[0] != 1:\r\n raise Exception('Only a single t0 sample is allowed')\r\n t0 = t0[0]\r\n for row in df_batch.itertuples():\r\n df_t0[row.sample_id] = df_log2[row.sample_id].sub(df_log2[t0])\r\n df_t0.drop(columns=t0, inplace=True) # drop the t0 col\r\n\r\n # average replicates by condition\r\n list_conds = df_batch['condition'].unique().tolist()\r\n list_conds.remove('t0')\r\n df_conds = df_ref.copy()\r\n for cond in list_conds:\r\n reps = df_batch.loc[df_batch['condition'] == cond]['sample_id'].tolist()\r\n if len(reps) > 1:\r\n df_conds[cond] = df_t0[reps].mean(axis=1)\r\n elif len(reps) == 1:\r\n df_conds[cond] = df_t0[reps]\r\n else:\r\n raise Exception('Error! Invalid number of replicates')\r\n\r\n # merge statistics files\r\n if merge_stats:\r\n df_stats = pd.DataFrame(columns=['parameters'])\r\n for row in df_batch.itertuples():\r\n file = stats_path / row.stats_files\r\n df_temp = pd.read_csv(file, sep=': ', engine='python', names=['parameters', row.sample_id])\r\n df_stats = pd.merge(df_stats, df_temp, on='parameters', how='outer')\r\n\r\n # export files and return dataframes if necessary\r\n outpath = path / out_folder\r\n Path.mkdir(outpath, exist_ok=True)\r\n # dictionary to map kws to dfs and output file names\r\n dict_df = {'reads': (df_reads, out_prefix + 'reads.csv'),\r\n 'log2': (df_log2, out_prefix + 'log2.csv'),\r\n 't0': (df_t0, out_prefix + 't0_reps.csv'),\r\n 'conds': (df_conds, out_prefix + 't0_conds.csv')}\r\n if merge_stats:\r\n dict_df.update({'stats': (df_stats, out_prefix + 'stats.csv')})\r\n # determine which files to export\r\n if save == 'all':\r\n save = ['reads','log2','t0', 'conds', 'stats']\r\n if isinstance(save, list):\r\n for key in save:\r\n dict_df[key][0].to_csv(outpath / dict_df[key][1], index=False)\r\n elif save is None:\r\n pass\r\n else:\r\n warnings.warn('Invalid value for save. No files exported')\r\n # determine df to return\r\n print('Batch processing completed')\r\n if return_df in dict_df.keys():\r\n return dict_df[return_df][0]\r\n elif return_df is None:\r\n return\r\n else:\r\n print('Invalid value for return_df. No dataframe returned')\r\n return","def run_concat_vcfs(job, context, vcf_ids, tbi_ids):\n\n work_dir = job.fileStore.getLocalTempDir()\n\n vcf_names = ['chrom_{}.vcf.gz'.format(i) for i in range(len(vcf_ids))]\n out_name = 'genome.vcf.gz'\n\n for vcf_id, tbi_id, vcf_name in zip(vcf_ids, tbi_ids, vcf_names):\n job.fileStore.readGlobalFile(vcf_id, os.path.join(work_dir, vcf_name))\n job.fileStore.readGlobalFile(tbi_id, os.path.join(work_dir, vcf_name + '.tbi'))\n\n cmd = ['bcftools', 'concat'] + [vcf_name for vcf_name in vcf_names] + ['-O', 'z']\n \n with open(os.path.join(work_dir, out_name), 'wb') as out_file:\n context.runner.call(job, cmd, work_dir=work_dir, outfile = out_file)\n\n cmd = ['tabix', '-f', '-p', 'vcf', out_name]\n context.runner.call(job, cmd, work_dir=work_dir)\n\n out_vcf_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name))\n out_tbi_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name + '.tbi'))\n\n return out_vcf_id, out_tbi_id","def build_index(self):\n # format output and input\n ref_file = f'{self.genome_database}BSB_ref.fa'\n # collect external command\n index_command = [f'{self.bwa_path}', 'index', '-a', 'bwtsw', '-b', f'{self.block_size}', ref_file]\n # run external command\n subprocess.run(args=index_command)","def add_batch(batch_index, pCS, orphans, fasta_d, cpus, dun_use_partial):\n cur_file = \"batch{0}.fasta\".format(batch_index)\n seqids = set([r.id for r in SeqIO.parse(open(cur_file), 'fasta')])\n o = ar.run_minimap(cur_file, \"seed{0}.S.fasta\".format(batch_index), cpus=cpus)\n print >> sys.stderr, \"processing\", o\n pCS, remains = sp.process_align_to_pCS(o, seqids, pCS, MiniReader, dun_use_partial=dun_use_partial)\n print >> sys.stderr, \"pCS: {0}, tucked: {1}, orphans: {2}, remains: {3}\".format( \\\n len(pCS.S), sum(v == 'T' for v in pCS.seq_stat.itervalues()), len(orphans), len(remains))\n # write batch<i>.remains.fasta\n cur_file = \"batch{0}.remains.fasta\".format(batch_index)\n FileIO.write_seqids_to_fasta(remains, cur_file, fasta_d)\n o = ar.run_minimap(cur_file, \"seed{0}.orphans.fasta\".format(batch_index), cpus=cpus)\n print >> sys.stderr, \"processing\", o\n pCS, orphans, remains = sp.process_align_to_orphan(o, remains, orphans, pCS, MiniReader, dun_use_partial=dun_use_partial)\n print >> sys.stderr, \"pCS: {0}, tucked: {1}, orphans: {2}, remains: {3}\".format( \\\n len(pCS.S), sum(v == 'T' for v in pCS.seq_stat.itervalues()), len(orphans), len(remains))\n # write batch<i>.remains2.fasta and self align\n cur_file = \"batch{0}.remains2.fasta\".format(batch_index)\n FileIO.write_seqids_to_fasta(remains, cur_file, fasta_d)\n o = ar.run_minimap(cur_file, cur_file, cpus=cpus)\n print >> sys.stderr, \"processing\", o\n pCS, remains = sp.process_self_align_into_seed(o, remains, MiniReader, pCS, dun_use_partial=dun_use_partial)\n print >> sys.stderr, \"pCS: {0}, tucked: {1}, orphans: {2}, remains: {3}\".format( \\\n len(pCS.S), sum(v == 'T' for v in pCS.seq_stat.itervalues()), len(orphans), len(remains))\n # combine remains+orphans to new orphans\n orphans = orphans.union(remains)\n FileIO.write_preClusterSet_to_fasta(pCS, \"seed{0}.S.fasta\".format(batch_index+1), fasta_d)\n FileIO.write_seqids_to_fasta(orphans, \"seed{0}.orphans.fasta\".format(batch_index+1), fasta_d)\n\n return pCS, orphans","def build(self, ref_path, reffile_template):\n SMALL_INDEX_MAX_SIZE = 4 * 1024**3 - 200 # From bowtie2-build wrapper\n assert os.stat(ref_path).st_size <= SMALL_INDEX_MAX_SIZE\n self.check_logger()\n for line in self.yield_output(['--wrapper',\n 'micall-0',\n '--quiet',\n '-f',\n ref_path,\n reffile_template],\n stderr=subprocess.STDOUT):\n if line != 'Building a SMALL index\\n':\n self.logger.debug(line)","def prepareBatchFile(self,detector,exposureTime):\n \n inp = open(ini.Ini().getParTestFile(\"BEST\",\"best_batch_file_template\"), 'r')\n t = Template(inp.read())\n s = t.substitute(besthome=ini.Ini().getPar(\"BEST\",\"besthome\"),\n bestbin=ini.Ini().getPar(\"BEST\",\"best_bin\"),\n detector=detector,exposure_time=exposureTime,folder=self.runFolder)\n \n self.completePath = os.path.join(self.runFolder,ini.Ini().getPar(\"BEST\",\"best_batch_file\") )\n outp = open(self.completePath, 'w')\n outp.write(s)\n outp.close()\n # give execute permissions\n #os.chmod(self.completePath, 0755)\n self.make_exe(self.completePath) \n self.log.logger.debug(\"Batch best file created: \" + self.completePath)\n \n return self.completePath","def cuffmerge(job, config, name, samples, manifest):\n\n stats_root = \"{}_cuffmerge_stats\".format(config['run_id'])\n logfile = \"{}.cuffmerge.log\".format(config['run_id'])\n\n command = [\"{}\".format(config['cuffmerge']['bin']),\n \"-g {}\".format(config['transcript_reference']),\n \"-s {}\".format(config['reference']),\n \"-p {}\".format(config['cuffmerge']['num_cores']),\n \"{}\".format(manifest)]\n\n job.fileStore.logToMaster(\"Cuffmerge Command: {}\\n\".format(command))\n pipeline.run_and_log_command(\" \".join(command), logfile)\n\n pwd = os.getcwd()\n config['merged_transcript_reference'] = os.path.join(pwd, \"merged.gtf\")\n\n return stats_root","def batch(self, coeff_count=13, db=False):\n mfccs, _ = self.mfcc(coeff_count)\n if db:\n mfccs = utils.dbspec(mfccs)\n delta1, delta2 = self.delta_coeffs(mfccs)\n self._annotate(mfccs)\n\n mfccs_len = mfccs.shape[1]\n batch_x = np.concatenate((mfccs, delta1, delta2), axis=0).transpose()\n batch_y = np.array(self.annotated_samples)\n print(\"AudioClip--Generated Batch\")\n return (batch_x, batch_y)","def generate_batch(self):\n\n # sbatch = list()\n # tbatch = list()\n # for i in range(self.dict_paras['batch_size']):\n # sbatch.append(self.lst_triplet_train_map[self.data_index])\n # self.data_index = (self.data_index + 1) % self.triplet_train_size\n\n sbatch = random.sample(self.lst_triplet_train_map, self.dict_paras['batch_size'])\n tbatch = list()\n\n for ele in sbatch:\n corrupted1, corrupted2 = self.get_corrupted_triplet(ele)\n tbatch.append((ele, corrupted1))\n tbatch.append((ele, corrupted2))\n return tbatch","def run(self, dataset_size=4, n_jobs=-1, starting_block=0):\n data_files = sorted(self.input_directory.glob(\"**/*.txt\"))\n log.info(f\"Creating shape file based on {len(data_files)} samples.\")\n\n n_blocks = int(len(data_files) / dataset_size)\n data_file_blocks = split(data_files, n_blocks)\n dataset_blocks_ids = np.arange(len(data_file_blocks))\n\n if starting_block != 0:\n data_file_blocks = data_file_blocks[starting_block:]\n dataset_blocks_ids = dataset_blocks_ids[starting_block:]\n log.info(f\"Starting at a different block number: {starting_block}.\")\n n_blocks = int(len(data_file_blocks))\n\n log.info(f\"Going through {n_blocks} blocks in parallel.\")\n Parallel(n_jobs=n_jobs)(\n delayed(self.generate_single_block)(data_file_block, dataset_block_id)\n for (data_file_block, dataset_block_id) in tqdm(\n zip(data_file_blocks, dataset_blocks_ids)\n )\n )\n\n log.info(\"Combining the separate index files..\")\n index_floorplan = sorted(self.output_directory.glob(\"index_floorplans_*.csv\"))\n log.info(f\"Found {len(index_floorplan)} index block files.\")\n index_files = pd.concat([pd.read_csv(_file) for _file in index_floorplan])\n index_files = index_files.fillna(0)\n index_files.to_csv(self.output_directory / \"index_floorplans.csv\", index=False)","def _cmd_batch(args):\n logging.info(\"CNVkit %s\", __version__)\n # Validate/restrict options, beyond what argparse mutual exclusion can do\n bad_args_msg = \"\"\n if args.reference:\n bad_flags = [\n flag\n for is_used, flag in (\n (args.normal is not None, \"-n/--normal\"),\n (args.fasta, \"-f/--fasta\"),\n (args.targets, \"-t/--targets\"),\n (args.antitargets, \"-a/--antitargets\"),\n (args.access, \"-g/--access\"),\n (args.annotate, \"--annotate\"),\n (args.short_names, \"--short-names\"),\n (args.target_avg_size, \"--target-avg-size\"),\n (args.antitarget_avg_size, \"--antitarget-avg-size\"),\n (args.antitarget_min_size, \"--antitarget-min-size\"),\n )\n if is_used\n ]\n if bad_flags:\n bad_args_msg = (\n \"If -r/--reference is given, options to construct \"\n \"a new reference (%s) should not be used.\" % \", \".join(bad_flags)\n )\n elif args.normal is None:\n bad_args_msg = (\n \"Option -n/--normal must be given to build a new \"\n \"reference if -r/--reference is not used.\"\n )\n elif args.seq_method in (\"hybrid\", \"amplicon\") and not args.targets:\n bad_args_msg = (\n \"For the '%r' sequencing method, option -t/--targets \"\n \"(at least) must be given to build a new reference if \"\n \"-r/--reference is not used.\" % args.seq_method\n )\n if bad_args_msg:\n sys.exit(bad_args_msg + \"\\n(See: cnvkit.py batch -h)\")\n\n # Ensure sample IDs are unique to avoid overwriting outputs\n seen_sids = {}\n for fname in (args.bam_files or []) + (args.normal or []):\n sid = core.fbase(fname)\n if sid in seen_sids:\n sys.exit(f\"Duplicate sample ID {sid!r} (from {fname} and {seen_sids[sid]})\")\n seen_sids[sid] = fname\n\n if args.processes < 1:\n args.processes = multiprocessing.cpu_count()\n\n if not args.reference:\n # Build a copy number reference; update (anti)targets upon request\n args.reference, args.targets, args.antitargets = batch.batch_make_reference(\n args.normal,\n args.targets,\n args.antitargets,\n args.male_reference,\n args.diploid_parx_genome,\n args.fasta,\n args.annotate,\n args.short_names,\n args.target_avg_size,\n args.access,\n args.antitarget_avg_size,\n args.antitarget_min_size,\n args.output_reference,\n args.output_dir,\n args.processes,\n args.count_reads,\n args.seq_method,\n args.cluster,\n )\n elif args.targets is None and args.antitargets is None:\n # Extract (anti)target BEDs from the given, existing CN reference\n ref_arr = read_cna(args.reference)\n targets, antitargets = reference.reference2regions(ref_arr)\n ref_pfx = os.path.join(args.output_dir, core.fbase(args.reference))\n args.targets = ref_pfx + \".target-tmp.bed\"\n args.antitargets = ref_pfx + \".antitarget-tmp.bed\"\n tabio.write(targets, args.targets, \"bed4\")\n tabio.write(antitargets, args.antitargets, \"bed4\")\n\n if args.bam_files:\n if args.processes == 1:\n procs_per_bam = 1\n logging.info(\"Running %d samples in serial\", len(args.bam_files))\n else:\n procs_per_bam = max(1, args.processes // len(args.bam_files))\n logging.info(\n \"Running %d samples in %d processes (that's %d processes per bam)\",\n len(args.bam_files),\n args.processes,\n procs_per_bam,\n )\n\n with parallel.pick_pool(args.processes) as pool:\n for bam in args.bam_files:\n pool.submit(\n batch.batch_run_sample,\n bam,\n args.targets,\n args.antitargets,\n args.reference,\n args.output_dir,\n args.male_reference,\n args.diploid_parx_genome,\n args.scatter,\n args.diagram,\n args.rscript_path,\n args.count_reads,\n args.drop_low_coverage,\n args.seq_method,\n args.segment_method,\n procs_per_bam,\n args.cluster,\n args.fasta,\n args.diploid_parx_genome,\n )\n else:\n logging.info(\n \"No tumor/test samples (but %d normal/control samples) \"\n \"specified on the command line.\",\n len(args.normal),\n )","def skesa_assemble(self):\n with progressbar(self.metadata) as bar:\n for sample in bar:\n # Initialise the assembly command\n sample.commands.assemble = str()\n try:\n if sample.general.trimmedcorrectedfastqfiles:\n # If the sample is a pure isolate, assemble it. Otherwise, run the pre-metagenome pipeline\n try:\n status = sample.run.Description\n except AttributeError:\n status = 'unknown'\n if status == 'metagenome':\n self.merge(sample)\n else:\n # Set the output directory\n sample.general.assembly_output = os.path.join(sample.general.outputdirectory,\n 'assembly_output')\n make_path(sample.general.assembly_output)\n sample.general.assemblyfile = os.path.join(sample.general.assembly_output,\n '{name}_unfiltered.fasta'\n .format(name=sample.name))\n sample.general.bestassemblyfile = os.path.join(sample.general.assembly_output,\n '{name}.fasta'\n .format(name=sample.name))\n fastqfiles = sample.general.trimmedcorrectedfastqfiles\n\n # Set the the forward fastq files\n sample.general.assemblyfastq = fastqfiles\n forward = fastqfiles[0]\n gz = True if '.gz' in forward else False\n # If there are two fastq files\n if len(fastqfiles) == 2:\n # Set the reverse fastq name https://github.com/ncbi/SKESA/issues/7\n sample.commands.assemble = 'skesa --fastq {fastqfiles} --cores {threads} ' \\\n '--use_paired_ends --vector_percent 1 ' \\\n '--contigs_out {contigs}'\\\n .format(fastqfiles=','.join(fastqfiles),\n threads=self.cpus,\n contigs=sample.general.assemblyfile)\n # Same as above, but use single read settings for the assembler\n else:\n sample.commands.assemble = 'skesa --fastq {fastqfiles} --cores {threads} ' \\\n '--vector_percent 1 --contigs_out {contigs}'\\\n .format(fastqfiles=','.join(fastqfiles),\n threads=self.cpus,\n contigs=sample.general.assemblyfile)\n # Specify that the files are gzipped\n if gz:\n sample.commands.assemble += ' --gz'\n # If there are no fastq files, populate the metadata appropriately\n else:\n sample.general.assembly_output = 'NA'\n sample.general.assemblyfastq = 'NA'\n sample.general.bestassemblyfile = 'NA'\n except AttributeError:\n sample.general.assembly_output = 'NA'\n sample.general.assemblyfastq = 'NA'\n sample.general.trimmedcorrectedfastqfiles = 'NA'\n sample.general.bestassemblyfile = 'NA'\n if sample.commands.assemble and not os.path.isfile(sample.general.assemblyfile):\n # Run the assembly\n out, err = run_subprocess(sample.commands.assemble)\n write_to_logfile(sample.commands.assemble,\n sample.commands.assemble,\n self.logfile,\n sample.general.logout,\n sample.general.logerr,\n None,\n None)\n write_to_logfile(out,\n err,\n self.logfile,\n sample.general.logout,\n sample.general.logerr,\n None,\n None)","def prepare_subset_vcf_files_by_population():\n if not os.path.exists(VCF_BY_POPULATION_PATH):\n print(\"preparing subset vcf by population\")\n os.makedirs(VCF_BY_POPULATION_PATH)\n sleep(10)\n vcf_tools_runner = VCFToolsDockerRunner()\n samples = glob(f\"{SAMPLES_FOLDER}/*.csv\")\n\n with tqdm(total=len(samples)) as pbar:\n for sample in samples:\n sample = sample.replace('\\\\', '/')\n sample_name = get_filename_from_path(sample)\n sample_path = \"/\".join([IMAGE_SHARE_FOLDER_PATH] + sample.split('/')[1:])\n pbar.set_description(f\"Processing {sample_name}\")\n vcf_tools_runner(\n f\"vcf-subset -c {sample_path} \"\n f\"{IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_VCF_FILE_NAME} | fill-an-ac > \"\n f\"{IMAGE_SHARE_FOLDER_PATH}/{VCF_BY_POPULATION_FOLDER}/{sample_name}.vcf\")\n pbar.update(1)\n else:\n print(f\"Subset VCF files by population already exist in: {VCF_BY_POPULATION_PATH}\")","def seqff(self):\r\n\r\n start = time.time()\r\n\r\n # load bininfo\r\n bininfo = load_bininfo(self.bininfodata_loc)\r\n\r\n # load input files\r\n if os.path.isdir(self.input_loc):\r\n input_list = [self.input_loc + x for x in os.listdir(self.input_loc)]\r\n\r\n elif os.path.isfile(self.input_loc):\r\n input_list = [self.input_loc]\r\n\r\n else:\r\n raise FileNotFoundError(\"error occurred : inputData is not a Directory or File\")\r\n\r\n for i, file in enumerate(input_list):\r\n filetype = file.split(\".\")[-1]\r\n # filetype : 'sam' or 'bam' or 'newtemp'\r\n if 'sam' in filetype:\r\n bincount = load_sam(file)\r\n\r\n elif 'newtemp' in filetype:\r\n bincount = load_counts(file)\r\n file = file.replace(\".newtemp\", \"\") # TEMP .newtemp -> .bam\r\n\r\n elif 'bam' in filetype:\r\n bincount = load_bam(file)\r\n\r\n else:\r\n continue\r\n\r\n #CREATE newtemp file in \"output_loc\"/newtemp/\r\n create_newtemp(bincount, file, self.newtemp_loc)\r\n\r\n newtemp = pd.DataFrame.from_dict(bincount, orient='index')\r\n newtemp.reset_index(level=0, inplace=True)\r\n newtemp.rename(columns={'index': 'binName', 0: 'counts'}, inplace=True)\r\n\r\n temp_bininfo = bininfo.copy(deep=True)\r\n temp_bininfo = temp_bininfo.merge(newtemp, on='binName',\r\n how='left') # missing value : NaN, not NA in pandas\r\n temp_bininfo['counts'] = temp_bininfo['counts'].fillna(0)\r\n\r\n temp_bininfo.sort_values(by='binorder', inplace=True)\r\n temp_bininfo.reset_index(drop=True)\r\n\r\n ####DATA PROCESSING #######################\r\n autosomebinsonly = []\r\n for index in range(61927):\r\n boolean = (temp_bininfo['FRS'][index] != 'NA') and \\\r\n (float(temp_bininfo['GC'][index]) > 0.316) and \\\r\n (temp_bininfo['CHR'][index] != 'chrX') and \\\r\n (temp_bininfo['CHR'][index] != 'chrY')\r\n autosomebinsonly.append(boolean)\r\n autosomebinsonly = pd.Series(autosomebinsonly)\r\n\r\n alluseablebins = []\r\n for index in range(61927):\r\n boolean = (temp_bininfo['FRS'][index] != \"NA\") and (float(temp_bininfo['GC'][index]) > 0.316)\r\n alluseablebins.append(boolean)\r\n alluseablebins = pd.Series(alluseablebins)\r\n\r\n #CREATE alluseablebins file in \"output_loc\"/alluseablebins\r\n #create_alluseablebins(alluseablebins, file, self.alluseablebins_loc)\r\n\r\n sum_counts = pd.Series(temp_bininfo['counts'])\r\n sum_counts = sum_counts[autosomebinsonly].sum(skipna=True)\r\n\r\n autoscaledtemp = pd.Series(temp_bininfo['counts'].loc[(autosomebinsonly)],\r\n copy=True) / sum_counts # NA-related code removed\r\n allscaledtemp = pd.Series(temp_bininfo['counts'].loc[(alluseablebins)], copy=True) / sum_counts\r\n\r\n gc_index = {}\r\n cnt = 0\r\n for index, isauto in enumerate(autosomebinsonly):\r\n if isauto:\r\n if temp_bininfo['GC'].iat[index] in gc_index:\r\n gc_index[temp_bininfo['GC'].iat[index]].append(float(autoscaledtemp.iat[cnt]))\r\n cnt += 1\r\n\r\n else:\r\n gc_index[temp_bininfo['GC'].iat[index]] = [float(autoscaledtemp.iat[cnt])]\r\n cnt += 1\r\n\r\n key_list = []\r\n val_list = []\r\n for key, val in gc_index.items():\r\n key_list.append(key)\r\n val_list.append(np.median(val))\r\n\r\n loess_var = loess(key_list, val_list) # default span : 0.75\r\n loess_var.fit()\r\n # y = loess.loess_prediction(newData, loessVar)\r\n # temp_loessPredict.loess_debugging(loessVar)\r\n\r\n ###prediction###\r\n loess_x = [float(gc) for index, gc in enumerate(temp_bininfo['GC']) if (alluseablebins[index])]\r\n # print(temp_bininfo['GC'])\r\n loess_fitted = loess_var.predict(loess_x)\r\n loess_fitted = list(loess_fitted.values)\r\n # print(loess_fitted)\r\n\r\n median_autoscaledtemp = np.median(autoscaledtemp)\r\n median_autoscaledtemp = float(median_autoscaledtemp) # for fixed constant\r\n\r\n normalizedbincount = [(x + (median_autoscaledtemp - loess_fitted[index])) for index, x in\r\n enumerate(allscaledtemp)]\r\n\r\n #CREATE normalizedbincount in \"output_loc\"/normalizedbincount\r\n create_normalizedbincount(normalizedbincount, file, self.normalizedbincount_loc)\r\n\r\n bincounts = pd.Series(data=np.repeat(a=0.0, repeats=61927), index=temp_bininfo['binName'], dtype=np.float64)\r\n\r\n sum_normalizedbincount = sum([val for val in normalizedbincount if not math.isnan(val)])\r\n sum_normalizedbincount = float(sum_normalizedbincount) # deep copy temporarily\r\n\r\n cnt = 0\r\n for index, x in enumerate(alluseablebins):\r\n if x == True:\r\n data = (normalizedbincount[cnt] / sum_normalizedbincount) * len(normalizedbincount)\r\n bincounts.iat[index] = data\r\n cnt += 1\r\n\r\n #CREATE bincounts in \"output_loc\"/bincounts\r\n create_bincounts(bincounts, file, self.bincounts_loc)\r\n\r\n wrsc = self.prediction(bincounts, self.B, self.mu, self.parameter_1, self.parameter_2)\r\n enet = np.dot(bincounts, (self.elnetbeta)) + (self.elnetintercept)\r\n ff = (wrsc+enet) / 2\r\n\r\n result_lines = list()\r\n result_lines.append(\"SeqFF\\tEnet\\tWRSC\")\r\n result_lines.append(\"{}\\t{}\\t{}\".format(ff, enet, wrsc))\r\n\r\n #CREATE results of seqff (seqff paper result covered) in \"output_loc\"/results\r\n create_results(result_lines, file, self.results_loc)\r\n\r\n end = time.time()\r\n elapsed = end - start\r\n h = int(elapsed) // 3600\r\n m = (int(elapsed) - (h * 3600)) // 60\r\n s = (int(elapsed) % 60)\r\n print(\"elapsed time: %d hr %d min %d sec\" % (h, m, s))\r\n print(\"elapsed :\", elapsed)\r\n print(\"progress : {} / {}\".format(i + 1, self.progress))","def __iter__(self):\n\n batch_sp = []\n batch_noise = []\n batch_mix = []\n batch_count = 0\n\n while True:\n\n # Randomizing wav lists\n random.shuffle(self._lst_spk_files)\n random.shuffle(self._lst_noise_files)\n\n for spk_file, noise_file in zip(self._lst_spk_files, self._lst_noise_files):\n\n # Read wav files\n sig_spk = self.__read_wav_file(spk_file)\n sig_noise = self.__read_wav_file(noise_file)\n\n # Align signal\n min_length = min(sig_spk.shape[0], sig_noise.shape[0])\n\n if min_length < self._fftsize:\n raise Exception(\"ERROR: Too short signals in dataset\")\n\n sig_spk = sig_spk[:min_length]\n sig_noise = sig_noise[:min_length]\n\n # Generate need SNR\n need_snr = random.uniform(self._min_snr, self._max_snr)\n\n # Calc scaled signals\n sig_spk, sig_noise = self.__mix_with_snr(sig_spk, sig_noise, need_snr)\n\n # Calc STFT\n stft_spk = stft(sig_spk, fftsize=self._fftsize, overlap=self._overlap)\n stft_noise = stft(sig_noise, fftsize=self._fftsize, overlap=self._overlap)\n stft_mix = stft_spk + stft_noise\n\n # Skip small segments\n frames, bin = stft_mix.shape\n if frames <= self._context_size:\n continue\n\n # Collect batch\n i = 0\n while i + self._context_size < frames:\n\n batch_sp.append(stft_spk[i:i + self._context_size, :])\n batch_noise.append(stft_noise[i:i + self._context_size, :])\n batch_mix.append(stft_mix[i:i + self._context_size, :])\n\n i += self._context_size // 2\n batch_count += 1\n\n if batch_count == self._batch_size:\n sp = np.array(batch_sp).reshape((self._batch_size,\n self._context_size, -1))\n noise = np.array(batch_noise).reshape((self._batch_size,\n self._context_size, -1))\n mix = np.array(batch_mix).reshape((self._batch_size,\n self._context_size, -1))\n yield sp, noise, mix\n\n batch_sp = []\n batch_noise = []\n batch_mix = []\n batch_count = 0","def addVCFSubsetJobs(self, workflow=None, inputData=None, db_vervet=None, sampleIDFile=None, transferOutput=True,\\\n\t\t\t\t\t\trefFastaFList=None, GenomeAnalysisTKJar=None,\\\n\t\t\t\t\t\tmaxContigID=None, outputDirPrefix=\"\"):\n\t\tif workflow is None:\n\t\t\tworkflow = self\n\t\tif GenomeAnalysisTKJar is None:\n\t\t\tGenomeAnalysisTKJar = workflow.GenomeAnalysisTKJar\n\t\tif refFastaFList is None:\n\t\t\trefFastaFList = self.refFastaFList\n\t\t\n\t\tsys.stderr.write(\"Adding vcf-subset jobs for %s vcf files ... \"%(len(inputData.jobDataLs)))\n\t\tno_of_jobs= 0\n\t\t\n\t\t\n\t\ttopOutputDir = \"%sVCFSubset\"%(outputDirPrefix)\n\t\ttopOutputDirJob = self.addMkDirJob(outputDir=topOutputDir)\n\t\tno_of_jobs += 1\n\t\t\n\t\treturnData = PassingData()\n\t\treturnData.jobDataLs = []\n\t\tfor jobData in inputData.jobDataLs:\n\t\t\tinputF = jobData.vcfFile\n\t\t\tchr = self.getChrFromFname(inputF.name)\n\t\t\tif maxContigID:\n\t\t\t\tcontig_id = self.getContigIDFromFname(inputF.name)\n\t\t\t\ttry:\n\t\t\t\t\tcontig_id = int(contig_id)\n\t\t\t\t\tif contig_id>maxContigID:\t#skip the small contigs\n\t\t\t\t\t\tcontinue\n\t\t\t\texcept:\n\t\t\t\t\tsys.stderr.write('Except type: %s\\n'%repr(sys.exc_info()))\n\t\t\t\t\timport traceback\n\t\t\t\t\ttraceback.print_exc()\n\t\t\tinputFBaseName = os.path.basename(inputF.name)\n\t\t\tcommonPrefix = inputFBaseName.split('.')[0]\n\t\t\toutputVCF = File(os.path.join(topOutputDir, '%s.subset.vcf'%(commonPrefix)))\n\t\t\tvcfSubsetJob = self.addVCFSubsetJob(workflow, executable=workflow.vcfSubset, vcfSubsetPath=workflow.vcfSubsetPath, \\\n\t\t\t\t\t\tsampleIDFile=sampleIDFile,\\\n\t\t\t\t\t\tinputVCF=inputF, outputF=outputVCF, \\\n\t\t\t\t\t\tparentJobLs=[topOutputDirJob]+jobData.jobLs, transferOutput=False, job_max_memory=200,\\\n\t\t\t\t\t\textraArguments=None, extraDependentInputLs=None)\n\t\t\t\n\t\t\t#2012.10.5\n\t\t\t#selectVariants would generate AC, AF so that TrioCaller could read it.\n\t\t\t#samtools uses 'AC1' instead of AC, 'AF1' instead of AF.\n\t\t\tVCF4OutputF = File(os.path.join(topOutputDir, '%s.niceformat.vcf'%commonPrefix))\n\t\t\tvcfConvertJob = self.addSelectVariantsJob(workflow, SelectVariantsJava=workflow.SelectVariantsJava, \\\n\t\t\t\t\tinputF=vcfSubsetJob.output, outputF=VCF4OutputF, \\\n\t\t\t\t\trefFastaFList=refFastaFList, parentJobLs=[vcfSubsetJob], \\\n\t\t\t\t\textraDependentInputLs=[], transferOutput=False, \\\n\t\t\t\t\textraArguments=None, job_max_memory=2000, interval=chr)\n\t\t\t\n\t\t\tVCFGzipOutputF = File(\"%s.gz\"%VCF4OutputF.name)\n\t\t\tVCFGzipOutput_tbi_F = File(\"%s.gz.tbi\"%VCF4OutputF.name)\n\t\t\tbgzip_tabix_VCF_job = self.addBGZIP_tabix_Job(workflow, bgzip_tabix=workflow.bgzip_tabix, \\\n\t\t\t\t\tparentJobLs=[vcfConvertJob], inputF=vcfConvertJob.output, outputF=VCFGzipOutputF, \\\n\t\t\t\t\ttransferOutput=transferOutput)\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\treturnData.jobDataLs.append(PassingData(jobLs=[bgzip_tabix_VCF_job], vcfFile=VCFGzipOutputF, \\\n\t\t\t\t\t\t\t\t\ttbi_F=VCFGzipOutput_tbi_F, \\\n\t\t\t\t\t\t\t\t\tfileLs=[VCFGzipOutputF, VCFGzipOutput_tbi_F]))\n\t\t\t\n\t\tsys.stderr.write(\"%s jobs.\\n\"%(self.no_of_jobs))\n\t\treturn returnData","def generate_flatbuffer_binaries():\n for element in FLATBUFFERS_CONVERSION_DATA:\n schema = element.schema\n output_path = element.output_path\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n for json in element.input_files:\n target = processed_json_path(json)\n if needs_rebuild(json, target) or needs_rebuild(schema, target):\n convert_json_to_flatbuffer_binary(\n json, schema, output_path)","def filesToBlender(context, prefix, max_blocks=200):\n # Get reference matrix\n refMatrix = None\n if context.scene.maps_models_importer_is_ref_matrix_valid:\n values = context.scene.maps_models_importer_ref_matrix\n refMatrix = Matrix((values[0:4], values[4:8], values[8:12], values[12:16]))\n\n drawcallId = 0\n while max_blocks <= 0 or drawcallId < max_blocks:\n if not os.path.isfile(\"{}{:05d}-indices.bin\".format(prefix, drawcallId)):\n break\n\n try:\n indices, positions, uvs, img, constants = loadData(prefix, drawcallId)\n except FileNotFoundError as err:\n print(\"Skipping ({})\".format(err))\n continue\n\n uvOffsetScale, matrix, refMatrix = extractUniforms(constants, refMatrix)\n\n # Make triangles from triangle strip index buffer\n n = len(indices)\n tris = [ [ indices[i+j] for j in [[0,1,2],[0,2,1]][i%2] ] for i in range(n - 3)]\n tris = [ t for t in tris if t[0] != t[1] and t[0] != t[2] and t[1] != t[2] ]\n verts = [ [ p[0], p[1], p[2] ] for p in positions ]\n\n [ou, ov, su, sv] = uvOffsetScale\n uvs = [ [ (floor(u * 65535.0 + 0.5) + ou) * su, (floor(v * 65535.0 + 0.5) + ov) * sv ] for u, v in uvs ]\n \n if len(indices) == 0:\n continue\n\n mesh_name = \"BuildingMesh-{:05d}\".format(drawcallId)\n obj = addMesh(context, mesh_name, verts, tris, uvs)\n obj.matrix_world = matrix\n\n mat_name = \"BuildingMat-{:05d}\".format(drawcallId)\n addImageMaterial(mat_name, obj, img)\n\n drawcallId += 1\n\n # Save reference matrix\n if refMatrix:\n values = sum([list(v) for v in refMatrix], [])\n context.scene.maps_models_importer_ref_matrix = values\n context.scene.maps_models_importer_is_ref_matrix_valid = True","def generate_batch(self) -> Tuple[np.ndarray, np.ndarray, List[str]]:\n batch = []\n labels = []\n filelist = []\n for i in range(self.batch_size):\n filename = self.filelist[self.data_index]\n filelist.append(self.filelist[self.data_index].split(\"\\\\\")[-1].split(\"/\")[-1])\n greyimg, colorimg = read_img(filename)\n batch.append(greyimg)\n labels.append(colorimg)\n self.data_index = (self.data_index + 1) % self.size\n batch = np.asarray(batch) / 255\n labels = np.asarray(labels) / 255\n return batch, labels, filelist","def make_batch(self, batch_size):\n filenames = self.get_filenames()\n\n if os.path.isdir(filenames):\n num_records = len(os.listdir(filenames))\n print(\"Loading from directory. \" + str(num_records) + \" tfRecords found.\")\n files = tf.data.Dataset.list_files(filenames + \"/\" + \"*.tfrecord\").shuffle(num_records)\n dataset = files.apply(\n tf.contrib.data.parallel_interleave(\n lambda x: tf.data.TFRecordDataset(x, num_parallel_reads=256, buffer_size=8*1024*1024),\n cycle_length=32, sloppy=True)\n )\n else:\n print(\"Loading from single tfRecord...\")\n dataset = tf.data.TFRecordDataset(filenames + \".tfrecord\").repeat()\n \n dataset = dataset.map(self.parser, num_parallel_calls=128)\n \n if self.subset == 'train':\n min_queue_examples = int(\n Cifar10DataSet.num_examples_per_epoch(self.subset) * 0.4)\n # Ensure that the capacity is sufficiently large to provide good random\n # shuffling.\n dataset = dataset.shuffle(buffer_size=min_queue_examples + 3 * batch_size)\n \n dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))\n dataset = dataset.prefetch(10)\n \n iterator = dataset.make_one_shot_iterator()\n seq_batch, input_batch, map_batch, transformation_batch = iterator.get_next()\n\n return seq_batch, input_batch, map_batch, transformation_batch","def write_batch_file(self, dot_input, dot_aln, hyphy_batch_file=\"\", hyphy_result_file=\"\"):\r\n gene_id = os.path.basename(dot_input).split(os.path.extsep)[0]\r\n path_main = os.path.splitext(dot_input)[0]\r\n\r\n if \"\" == hyphy_batch_file:\r\n hyphy_batch_file = path_main + \".bf\"\r\n\r\n if \"\" == hyphy_result_file:\r\n hyphy_result_file = path_main + \".result\"\r\n\r\n if \"\" == self.batch_content:\r\n raise BFError\r\n\r\n # replace begins here\r\n batch_content, num_hits = re.subn(self.f_input, dot_input, self.batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n # partition is optional\r\n if (0, 0) == self.partition:\r\n if self.f_partition in batch_content:\r\n batch_content, num_hits = re.subn(self.f_partition, \"\", batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n else:\r\n batch_content, num_hits = re.subn(self.f_partition, \"%d-%d\" % self.partition, batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n batch_content, num_hits = re.subn(self.f_mdl, self.mdl_file, batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n # only support 1 matrix now :2014-5-26\r\n batch_content, num_hits = re.subn(self.f_matrix_name, self.matrix_name[0], batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n if self.use_given_tree:\r\n tree_newick_string = self.tree_definition_external\r\n else:\r\n genes_share_aln = pHdata.aln_reader(dot_aln)\r\n tree_newick_string = self.build_tree(genes_share_aln)\r\n\r\n batch_content, num_hits = re.subn(self.f_tree, tree_newick_string, batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n batch_content, num_hits = re.subn(self.f_output, hyphy_result_file, batch_content)\r\n self._error_no_hit(num_hits)\r\n\r\n self.check_whether_incomplete(batch_content)\r\n\r\n with open(name=hyphy_batch_file, mode=\"w\") as bf_writer:\r\n bf_writer.write(batch_content)","def save_batch(self):\n self._batch_counter += 1\n write_to_disk(\n self._batch_cases,\n os.path.join(\n self.crop.location,\n \"batches\",\n BTCH_NM.format(self._batch_counter),\n ),\n )\n self._batch_cases = []\n self._counter = 0","async def addToFingerPrint(samples, sampleset=, allsampleset=\"all\", workspace=WORKSPACE, sid=, vcf_list=None, \nvcf_list_dir=, working_dir, crosscheck_batch_size, recreate_batch, bamcolname,\ntaiga_dataset, taiga_filename):\n bams = samples[bamcolname]\n bams[sid] = bams.index\n print('adding '+str(len(bams))+' new samples to the fingerprint')\n wm = dm.WorkspaceManager(workspace).disable_hound()\n \n # Create batch files listing all vcfs in fingerprints dir and upload to bucket\n # (NEW VERSION ONLY) will only needed if need to recreate batches\n if recreate_batch:\n if not vcf_list:\n vcf_list = gcp.lsFiles([vcf_list_dir])\n vcf_list = wm.get_samples()[\"fingerprint_vcf\"].tolist()\n batches = []\n for i, l in enumerate(range(0, len(vcf_list), crosscheck_batch_size)):\n f = open(working_dir + \"vcf_batch_\"+str(i), 'w')\n f.write(\"\\n\".join(vcf_list[l:l + crosscheck_batch_size]))\n f.close()\n batches.append(working_dir+\"vcf_batch_\"+str(i))\n gcp.cpFiles(batches, vcf_list_dir)\n\n # Upload sample sheet\n samples_df = pd.DataFrame()\n samples_df[[\"bam_filepath\", \"bai_filepath\", \"sample_id\",\n \"participant_id\"]] = bams[bamcolname + [sid, sid]].values\n samples_df = samples_df.set_index('sample_id')\n wm.upload_samples(samples_df, add_participant_samples=True)\n wm.update_sample_set(sampleset, samples_df.index)\n\n # Submit jobs \n submission_id = wm.create_submission(\"fingerprint_bam_with_liftover\", sampleset, \n 'sample_set', expression='this.samples')\n await terra.waitForSubmission(workspace, submission_id)\n\n #1.2 Crosscheck Fingerprint VCFs\n #Here we use Dalmation to run the crosscheck_vcfs workflow on Terra. \n # This workflow calls Picard CrosscheckFingerprints to compare the new \n # fingerprint vcfs to batches of existing fingerprint vcfs in fingerprints_dir\n # Create list with new vcfs and upload to bucket\n f = open(working_dir + sampleset, 'w')\n f.write(('\\n').join(wm.get_samples().loc[samples_df.index, 'fingerprints'].tolist()))\n f.close()\n gcp.cpFiles(working_dir + sampleset, vcf_list_dir)\n os.system('rm '+working_dir + sampleset)\n\n # Upload sample sheet\n if recreate_batch:\n sample_group_df = pd.DataFrame(data={\"entity:sample_group_id\" : batches, \"vcf_group\" : [vcf_list_dir + x for x in batches]}).set_index('entity:sample_group_id')\n else:\n sample_group_df = pd.DataFrame(data={\"entity:sample_group_id\" : [sampleset], \"vcf_group\" : [vcf_list_dir+sampleset]}).set_index('entity:sample_group_id')\n \n print(wm.get_entities('sample_group').index.tolist())\n wm.upload_entities(\"sample_group\", sample_group_df)\n try:\n wm.update_entity_set(\"sample_group\", set_id=allsampleset,\n entity_ids=wm.get_entities('sample_group').index)\n except:\n print(\"still can't update entitis, please upload directly from the file in ../temp.tsv\")\n #in case it does not work\n sample_group_df.to_csv(\"../temp.tsv\", sep='\\t')\n\n # Submit jobs\n conf = wm.get_config(\"crosscheck_vcfs\")\n conf['inputs']['crosscheck.run_crosscheck.vcf_second_input_file'] = '\"'+vcf_list_dir+sampleset+'\"'\n wm.update_config(conf)\n submission_id = wm.create_submission(\"crosscheck_vcfs\", allsampleset, \n 'sample_set',expression='this.samples')\n await terra.waitForSubmission(workspace, submission_id)\n\n #1.3 Update LOD matrix\n #Here we update the fingerprint LOD matrix on taiga with the new fingerprints\n # Generate matrix with LOD score for new fingerprint vcfs\n new_lod_list = []\n samples_df = wm.get_entities(\"sample_group\")['cross_checks_out'].tolist()\n for batch in samples_df:\n # could be pd concat\n df = pd.read_csv(batch, sep='\\t', comment='#')\n lod_mat = df.pivot(index=\"LEFT_SAMPLE\",\n columns=\"RIGHT_SAMPLE\", values=\"LOD_SCORE\")\n new_lod_list.append(lod_mat)\n new_lod_mat = pd.concat(new_lod_list)\n new_lod_mat.index.name = None\n new_lod_mat = new_lod_mat.T\n\n # Update LOD matrix ( have to update (A+a)*(B+b) = (AB)+(aB)+(Ab)+(ab))\n prev_lod_mat = tc.get(name=taiga_dataset,file=taiga_filename)\n new_ids = set(new_lod_mat.index)\n old_ids = set(prev_lod_mat.index) - set(new_ids)\n updated_lod_mat = pd.concat((prev_lod_mat.loc[old_ids,old_ids],\n new_lod_mat.loc[new_ids,old_ids]), axis=0)\n updated_lod_mat = pd.concat((updated_lod_mat.loc[new_ids.union(old_ids), old_ids], \n new_lod_mat.transpose().loc[new_ids.union(old_ids, new_ids)]), axis=1)\n updated_lod_mat.to_csv(working_dir+taiga_filename+'.csv')\n \n # Upload updated LOD matrix to Tiaga\n tc.update_dataset(dataset_permaname=taiga_dataset,\n changes_description=\"New bam fingerprints added for \"+sampleset,\n upload_files=[\n {\n \"path\": working_dir+taiga_filename+'.csv',\n \"name\": taiga_filename,\n \"format\": \"NumericMatrixCSV\",\n \"encoding\": \"utf-8\"\n }\n ],\n add_all_existing_files=True)\n\n # finding issues with the dataset\n v = updated_lod_mat.loc[new_ids]\n ref = tracker.getTracker()\n ref = ref.append(samples)\n should = {}\n print(\"\\n\\nsamples that should match but don't:\")\n for u in set(fbams.arxspan_id):\n res = v.loc[fbams[fbams.arxspan_id == u].index,\n ref[ref.arxspan_id == u].index.tolist()]\n for i, j in [(res.index[x], res.columns[y]) for x, y in np.argwhere(res.values < 100)]:\n print('__________________________')\n print(res.loc[i, j])\n print(i, ':', tuple(ref.loc[i, ['arxspan_id', 'version', 'datatype', 'participant_id']].values), j, ':', tuple(\n ref.loc[j, ['arxspan_id', 'version', 'datatype', 'participant_id', 'blacklist']]))\n \n print(\"\\n\\nsamples that shouldn't match but do\")\n previ = ''\n shouldnt = {}\n for i, j in [(v.index[x], v.columns[y]) for x, y in np.argwhere(v.values > 500)]:\n if i == j:\n continue\n if ref.loc[i]['participant_id'] == ref.loc[j]['participant_id']:\n continue\n if i != previ:\n if previ != '':\n shouldnt.update({'_'.join(ref.loc[previ, ['arxspan_id', 'version', 'datatype',\n 'participant_id', \n 'stripped_cell_line_name']].astype(str).values.tolist()): n})\n n = [tuple(ref.loc[j, ['arxspan_id', 'version', 'datatype',\n 'participant_id', 'stripped_cell_line_name']].values)]\n else:\n n.append(tuple(ref.loc[j, ['arxspan_id', 'version', 'datatype',\n 'participant_id', 'stripped_cell_line_name']].values))\n previ = i\n return updated_lod_mat, should, shouldnt","def test_no_batches(self):\n\n gas = {\n \"class\": \"PASGas\",\n \"path\": \"C:\\\\Python\\\\SensorTools\\\\Tests\\\\PAS_gas_testdata\",\n \"filename\": \"NW1017.ASD\", \n \"temperature_channel\": 0,\n \"audio_channel\": 2,\n \"pd_channel\": 1,\n \"temperature_inverted\": True,\n \"audio_inverted\": True,\n }\n liquid = {\n \"class\": \"PASLiquid\",\n \"path\": \"C:\\\\Python\\\\SensorTools\\\\Tests\\\\PAS_liquid_testdata\",\n \"filename\": \"NW1006.ASD\",\n \"temperature_channel\": 0,\n \"audio_channel\": 1,\n \"temperature_inverted\": True, \n }\n mess = [gas.copy(), gas.copy(), gas.copy(), gas.copy(), liquid.copy(), liquid.copy(), liquid.copy(), liquid.copy(), liquid.copy()]\n mess[0][\"filename\"] = \"NW1017.ASD\"\n mess[1][\"filename\"] = \"NW1018.ASD\"\n mess[2][\"filename\"] = \"NW1019.ASD\"\n mess[3][\"filename\"] = \"NW1020.ASD\"\n \n mess[4][\"filename\"] = \"NW1001.ASD\"\n mess[5][\"filename\"] = \"NW1002.ASD\"\n mess[6][\"filename\"] = \"NW1003.ASD\"\n \n mess[7][\"filename\"] = \"NW1004.ASD\"\n mess[8][\"filename\"] = \"NW1008.ASD\"\n \n self.P = MLS.MultiLinearSpectra(verbose = self.verbose, mess = mess)\n self.P.import_data(filetype = \"ASD\")\n self.P.select_single_slope()\n min_x, max_x = self.P.get_min_max_x() \n self.P.bin_data(x_resolution = 0.01, min_x = min_x, max_x = max_x)\n self.P.calculate_signal() \n\n batches = []\n self.P.make_batches(batches)","def _generate_and_load_initial_batch(self, working_directory: Path):\n\n template_dir = Path(working_directory) / \"template_1\"\n template_dir.mkdir()\n # changes here should often be reflected in\n # data_generator_opts and data_loader_opts\n\n channel_decl = self.channel_configs[0]\n\n plugin_options = {\n \"pid\": \"0\",\n \"big_ids\": \"True\",\n }\n # if it's efficient to do the whole load in one go, let's just do that.\n if self.run_until.gap < MIN_PORTION_SIZE:\n num_records = self.run_until.gap\n else:\n num_records = 1 # smallest possible batch to get to parallelizing fast\n results = self._generate_and_load_batch(\n template_dir,\n channel_decl.org_config,\n {\n \"generator_yaml\": self.options.get(\"recipe\"),\n \"num_records\": num_records,\n \"num_records_tablename\": self.run_until.sobject_name or COUNT_REPS,\n \"loading_rules\": self.loading_rules,\n \"vars\": channel_decl.merge_recipe_options(self.recipe_options),\n \"plugin_options\": plugin_options,\n \"bulk_mode\": self.bulk_mode,\n },\n )\n self.update_running_totals_from_load_step_results(results)\n\n # rename directory to reflect real number of sets created.\n wd = SnowfakeryWorkingDirectory(template_dir)\n if self.run_until.sobject_name:\n self.sets_finished_while_generating_template = wd.get_record_counts()[\n self.run_until.sobject_name\n ]\n else:\n self.sets_finished_while_generating_template = num_records\n\n new_template_dir = data_loader_new_directory_name(template_dir, self.run_until)\n shutil.move(template_dir, new_template_dir)\n template_dir = new_template_dir\n\n # don't send data tables to child processes. All they\n # care about are ID->OID mappings\n wd = SnowfakeryWorkingDirectory(template_dir)\n self._cleanup_object_tables(*wd.setup_engine())\n\n return template_dir, wd.relevant_sobjects()","def combine_gvcf(self, reference, gvcf_list, output, input_is_sorted=False, extension_list=[\"g.vcf\",],\n tmp_dir=\"./tmp_combine_gvcf/\", max_files_per_merging=50, iteration=0, threads=None,\n remove_intermediate_files=False):\n\n filtered_gvcf_list = []\n for filename in gvcf_list:\n for extension in extension_list:\n if extension == filename[-len(extension):]:\n filtered_gvcf_list.append(filename)\n break\n \n if len(filtered_gvcf_list) <= max_files_per_merging:\n options = self.parse_options(reference, filtered_gvcf_list, output, input_is_sorted, extension_list=extension_list)\n self.execute(options, runtype=\"cp\")\n if remove_intermediate_files:\n shutil.rmtree(tmp_dir, ignore_errors=True)\n\n else:\n self.safe_mkdir(tmp_dir)\n iteration_dir = \"%s/iteration_%i/\" % (tmp_dir, iteration)\n self.safe_mkdir(iteration_dir)\n\n number_of_files = len(filtered_gvcf_list)\n\n bins = np.arange(0, number_of_files, max_files_per_merging)\n #print(bins)\n if bins[-1] != number_of_files:\n if number_of_files - bins[-1] < 2:\n bins[-1] = number_of_files\n else:\n bins = np.append(bins, number_of_files)\n\n output_file_list = []\n options_list = []\n\n merged_files = 0\n for i in range(0, len(bins)-1):\n output_file = \"%s/%i.g.vcf\" % (iteration_dir, i)\n output_file_list.append(output_file)\n #print(bins[i], bins[i+1])\n\n merged_files += bins[i+1] - bins[i]\n options_list.append(self.parse_options(reference,\n filtered_gvcf_list[bins[i]:bins[i+1]],\n output_file,\n input_is_sorted, extension_list=extension_list))\n print(\"%i/%i files will be merged\" % (merged_files, number_of_files))\n\n self.parallel_execute(options_list, threads=threads, runtype=\"cp\")\n\n self.combine_gvcf(reference, output_file_list, output, input_is_sorted=input_is_sorted,\n extension_list=extension_list,\n tmp_dir=tmp_dir,\n max_files_per_merging=max_files_per_merging, iteration=iteration+1)","def wrt_gau_input(self):\n fp = open(\"myfiles.dat\", \"w\") \n nmol = self.model['nmol']\n prefix = self.config['job_prefix']\n incr = self.config['incr']\n for i in xrange(0, nmol, incr):\n self.wrt_gau_input_once(i)\n print >>fp, \"%sx%s.gjf\" % (prefix, i)\n fp.close()\n \n # extra jobs\n fp = open(\"link.sh\", \"w\")\n print >>fp, \"#! /bin/bash\"\n print >>fp, \"# sampling shell input\"\n print >>fp, \"rm linking.gjf\"\n print >>fp, \"myfiles=`more myfiles.dat`\"\n print >>fp, \"\"\"\n for onefile in $myfiles;\n do cat $onefile >> linking.gjf;\n echo -e '\\\\n--Link1--\\\\n' >> linking.gjf;\n done\n \"\"\"\n fp.close()\n return","def build(self):\r\n self.dirty = 0\r\n \r\n # Files first\r\n for output in self.files.keys():\r\n params = self.files[output]\r\n if (params[1] != -1):\r\n filename = params[0]\r\n freq = params[1]\r\n if (output == 'energies'):\r\n self.myOutputs.append(OutputEnergies.OutputEnergies(filename, freq, 1,0,1.0,0))\r\n elif (output == 'dcdtrajpos'):\r\n if (os.path.exists(filename)): # Continue\r\n self.myOutputs.append(OutputDCDTrajectory.OutputDCDTrajectory(filename, freq, 1, 1))\r\n else: # Overwrite\r\n self.myOutputs.append(OutputDCDTrajectory.OutputDCDTrajectory(filename, freq, 1, 0))\r\n elif (output == 'dcdtrajvel'):\r\n if (os.path.exists(filename)):\r\n self.myOutputs.append(OutputDCDTrajectoryVel.OutputDCDTrajectoryVel(filename, freq, 1, 1))\r\n else:\r\n self.myOutputs.append(OutputDCDTrajectoryVel.OutputDCDTrajectoryVel(filename, freq, 1, 0))\r\n elif (output == 'xyztrajforce'):\r\n self.myOutputs.append(OutputXYZTrajectoryForce.OutputXYZTrajectoryForce(filename, freq))\r\n elif (output == 'xyztrajpos'):\r\n self.myOutputs.append(OutputXYZTrajectoryPos.OutputXYZTrajectoryPos(filename, freq, 1))\r\n elif (output == 'xyztrajvel'):\r\n self.myOutputs.append(OutputXYZTrajectoryVel.OutputXYZTrajectoryVel(filename, freq))\r\n elif (output == 'gui'):\r\n self.myOutputs.append(OutputFAHGUI.OutputFAHGUI(filename, freq, 52753, 1, \"MDL_3.0\", 0.0, 0))\r\n\r\n if (self.screen != -1):\r\n self.myOutputs.append(OutputScreen.OutputScreen(self.screen))\r\n\r\n\r\n # Now plots\r\n for plot in self.plots.keys():\r\n freq = self.plots[plot]\r\n if (freq != -1):\r\n\r\n # Initialize a plot\r\n if (not self.doMPL): # Gnuplot\r\n self.xyData[plot] = []\r\n self.graphs[plot] = Gnuplot(debug=0)\r\n else: # Matplotlib\r\n self.xData[plot] = []\r\n self.yData[plot] = []\r\n self.figures[plot] = 0\r\n\r\n # Add the function to plot the data,\r\n # and the frequency at which to execute it\r\n self.myPlots.append([self.plotFunctions[plot], freq])","def build(self, file_number, data):\n pass","def create(self):\n\n for key in self.dirs.keys():\n if type(self.dirs[key]) == dict:\n for dir_type in self.dirs[key].keys():\n create_if_not_exists(self.dirs[key][dir_type])\n else:\n create_if_not_exists(self.dirs[key])\n\n self.inputFileIds = {}\n for sample_name, sample_info in self.samples.items():\n if not sample_info['use_it']:\n continue\n\n process_name = sample_info[\"process_name_specific\"]\n is_mc = (sample_info[\"type\"] == \"mc\")\n\n if not is_mc:\n continue\n\n logging.info(\"Creating configuration files to run '%s' for sample %s\" % (self.executable, process_name))\n\n inputFileList = generateInputFileList(sample_info, self.max_files_per_job)\n key_dir = getKey(process_name)\n\n outputFile = os.path.join(\n self.dirs[key_dir][DKEY_HISTO], \"%s.root\" % process_name\n )\n self.outputFiles[process_name] = {\n 'inputFiles' : [],\n 'outputFile' : outputFile,\n }\n if os.path.isfile(outputFile) and tools_is_file_ok(outputFile, min_file_size = 2000):\n logging.info('File {} already exists --> skipping job'.format(outputFile))\n continue\n\n for jobId in inputFileList.keys():\n\n key_file = getKey(sample_name, jobId)\n\n self.inputFiles[key_file] = inputFileList[jobId]\n if len(self.inputFiles[key_file]) == 0:\n logging.warning(\n \"'%s' = %s --> skipping job !!\" % (key_file, self.inputFiles[key_file])\n )\n continue\n\n self.cfgFiles_projection[key_file] = os.path.join(\n self.dirs[key_dir][DKEY_CFGS], \"project_%s_%i_cfg.txt\" % (process_name, jobId)\n )\n self.outputFiles_tmp[key_file] = os.path.join(\n self.dirs[key_dir][DKEY_HISTO_TMP], \"histogram_%i.root\" % jobId\n )\n self.logFiles_projection[key_file] = os.path.join(\n self.dirs[key_dir][DKEY_LOGS], \"project_%s_%i.log\" % (process_name, jobId)\n )\n self.scriptFiles_projection[key_file] = os.path.join(\n self.dirs[key_dir][DKEY_CFGS], \"project_%s_%i_cfg.sh\" % (process_name, jobId)\n )\n projection_module = self.projection_module\n if projection_module == \"count\":\n projection_module = \"countHistogramAll\"\n if sample_name.startswith('/TTTo'):\n projection_module += \"CompTopRwgt\"\n elif sample_info['sample_category'].startswith('ttH'):\n projection_module += \"CompHTXS\"\n elif isSplitByNlheJet(process_name):\n projection_module += \"SplitByLHENjet\"\n elif isSplitByNlheHT(process_name):\n projection_module += \"SplitByLHEHT\"\n elif isSplitByNlheJetHT(process_name, sample_name):\n projection_module += \"SplitByLHENjetHT\"\n self.jobOptions_sbatch[key_file] = {\n 'histName' : process_name,\n 'inputFiles' : self.inputFiles[key_file],\n 'cfgFile_path' : self.cfgFiles_projection[key_file],\n 'outputFile' : self.outputFiles_tmp[key_file],\n 'logFile' : self.logFiles_projection[key_file],\n 'scriptFile' : self.scriptFiles_projection[key_file],\n 'projection_module' : projection_module,\n }\n if self.projection_module != 'puHist':\n self.jobOptions_sbatch[key_file]['ref_genWeight'] = self.ref_genWeights[process_name]\n if process_name not in self.ref_genWeights:\n raise RuntimeError(\"Unable to find reference LHE weight for process %s\" % process_name)\n self.createCfg_project(self.jobOptions_sbatch[key_file])\n self.outputFiles[process_name]['inputFiles'].append(self.outputFiles_tmp[key_file])\n\n if self.is_sbatch:\n logging.info(\"Creating script for submitting '%s' jobs to batch system\" % self.executable)\n self.num_jobs['project'] += self.createScript_sbatch(\n self.executable, self.sbatchFile_projection, self.jobOptions_sbatch\n )\n\n logging.info(\"Creating Makefile\")\n lines_makefile = []\n self.addToMakefile_project(lines_makefile)\n self.addToMakefile_hadd(lines_makefile)\n if self.plot:\n self.addToMakefile_plot(lines_makefile)\n self.addToMakefile_finalHadd(lines_makefile)\n self.createMakefile(lines_makefile)\n logging.info(\"Done\")\n\n return self.num_jobs","def __iter__(self):\n\n batch_sp = []\n batch_noise = []\n batch_mix = []\n batch_count = 0\n\n while True:\n\n # Randomizing wav lists\n random.shuffle(self._lst_spk_files)\n random.shuffle(self._lst_noise_files)\n\n for spk_file, noise_file in zip(self._lst_spk_files, self._lst_noise_files):\n\n # Read wav files\n sig_spk, rate = self.__read_wav_file(spk_file)\n sig_noise, _ = self.__read_wav_file(noise_file)\n\n # Skip silence file\n if np.mean(sig_spk ** 2) < self.energy_silence_threshold or \\\n np.mean(sig_noise ** 2) < self.energy_silence_threshold:\n continue\n\n # Apply reverberations\n if self._enable_rir:\n rev_prob = np.random.uniform(0, 1) < self._rir_prob\n if rev_prob:\n filter_num = random.randint(0, self._rir_filters_num - 1)\n\n filter_sp_name = self.RIR_PREF + str(filter_num) + self.RIR_SP_SUF\n filter_n_name = self.RIR_PREF + str(filter_num) + self.RIR_N_SUF\n\n sig_spk = reverb_util.reverb_matlab(sig_spk, rate, os.path.join(self._rir_dir, filter_sp_name))\n sig_noise = reverb_util.reverb_matlab(sig_noise, rate, os.path.join(self._rir_dir, filter_n_name))\n\n # Align signal\n min_length = min(sig_spk.shape[0], sig_noise.shape[0])\n spk_length = sig_spk.shape[0]\n noise_length = sig_noise.shape[0]\n\n if min_length < self._fftsize:\n raise Exception(\"ERROR: Too short signals in dataset\")\n\n if spk_length > min_length:\n start_ind = random.randint(0, spk_length - min_length)\n sig_spk = sig_spk[start_ind:start_ind + min_length]\n elif noise_length > min_length:\n start_ind = random.randint(0, noise_length - min_length)\n sig_noise = sig_noise[start_ind:start_ind + min_length]\n\n # Generate need SNR\n need_snr = random.uniform(self._min_snr, self._max_snr)\n\n # Calc scaled signals\n sig_spk, sig_noise = self.__mix_with_snr(sig_spk, sig_noise, need_snr)\n\n # Normalization\n norm_const = np.max([np.max(np.abs(sig_spk)), np.max(np.abs(sig_noise))])\n sig_spk /= norm_const\n sig_noise /= norm_const\n\n # Calc STFT\n stft_spk = stft(sig_spk, fftsize=self._fftsize, overlap=self._overlap)\n stft_noise = stft(sig_noise, fftsize=self._fftsize, overlap=self._overlap)\n stft_mix = stft_spk + stft_noise\n\n # Skip small segments\n frames, bin = stft_mix.shape\n if frames <= self._context_size:\n continue\n\n # Collect batch\n i = 0\n while i + self._context_size < frames:\n\n batch_sp.append(stft_spk[i:i + self._context_size, :])\n batch_noise.append(stft_noise[i:i + self._context_size, :])\n batch_mix.append(stft_mix[i:i + self._context_size, :])\n\n i += self._context_size // 2\n batch_count += 1\n\n if batch_count == self._batch_size:\n sp = np.array(batch_sp).reshape((self._batch_size,\n self._context_size, -1))\n noise = np.array(batch_noise).reshape((self._batch_size,\n self._context_size, -1))\n mix = np.array(batch_mix).reshape((self._batch_size,\n self._context_size, -1))\n yield sp, noise, mix\n\n batch_sp = []\n batch_noise = []\n batch_mix = []\n batch_count = 0","def build(self, is_easy=False) -> None:\n allocation = ['train', 'dev', 'test']\n\n bm25_helper = self.__build_bm25_helper(is_easy)\n\n for entry in allocation:\n with open(self.__json_location + '/merged_' + entry + '.json', 'r') as f:\n json_data = json.load(f)\n\n output_file_name = 'data_' + entry\n if is_easy:\n json2training_converter = Json2EasyTraining(json_data, bm25_helper)\n output_file_name += '_easy'\n else:\n json2training_converter = JSON2Training(json_data, bm25_helper)\n\n training_set = json2training_converter.convert()\n dialog_lookup_table = json2training_converter.get_dialog_lookup_table()\n\n self.__write_tsv(output_file_name + '.tsv', training_set)\n self.__write_array(output_file_name + '_lookup' '.txt', dialog_lookup_table)","def bowtie_build(self):\n if self.params[\"data_type\"] != \"barcode\":\n command = [\n \"bowtie2-build\",\n \"-f\",\n \"--threads\", str(self.proc),\n os.path.join(self.params[\"index_path\"], \"ref.fa\"),\n os.path.join(self.params[\"index_path\"], \"reference\")\n ]\n\n self.run_subprocess(command)","def GatherVcfs(\n b: hb.Batch,\n input_vcfs: List,\n disk_size: int,\n output_vcf_path: str = None,\n) -> Job:\n j = b.new_job('VQSR: FinalGatherVcf')\n j.image(utils.GATK_IMAGE)\n j.memory(f'16G')\n j.storage(f'{disk_size}G')\n j.declare_resource_group(\n output_vcf={'vcf.gz': f'{NAME}_gathered.vcf.gz', 'vcf.gz.tbi': f'{NAME}_gathered.vcf.gz.tbi'}\n )\n\n input_cmdl = ' '.join([f'--input {v}' for v in input_vcfs])\n j.command(\n f\"\"\"set -euo pipefail\n # --ignore-safety-checks makes a big performance difference so we include it in \n # our invocation. This argument disables expensive checks that the file headers \n # contain the same set of genotyped samples and that files are in order \n # by position of first record.\n gatk --java-options -Xms6g \\\\\n GatherVcfsCloud \\\\\n --gather-type BLOCK \\\\\n {input_cmdl} \\\\\n --output {j.output_vcf['vcf.gz']}\n tabix {j.output_vcf['vcf.gz']}\"\"\"\n )\n if output_vcf_path:\n b.write_output(j.output_vcf, f'{output_vcf_path}{NAME}_gathered{LABEL}')\n return j","def generate_megafile():\n\n print(\"\\nFetching testing dataset…\")\n testing = get_testing()\n\n print(\"\\nFetching ECDC dataset…\")\n ecdc = get_ecdc()\n\n location_mismatch = set(testing.location).difference(set(ecdc.location))\n for loc in location_mismatch:\n print(f\"<!> Location '{loc}' has testing data but is absent from ECDC data\")\n\n print(\"\\nFetching OxCGRT dataset…\")\n cgrt = get_cgrt()\n\n all_covid = (\n ecdc\n .merge(testing, on=[\"date\", \"location\"], how=\"outer\")\n .merge(cgrt, on=[\"date\", \"location\"], how=\"left\")\n .sort_values([\"location\", \"date\"])\n )\n\n # Add ISO codes\n print(\"Adding ISO codes…\")\n iso_codes = pd.read_csv(os.path.join(INPUT_DIR, \"iso/iso3166_1_alpha_3_codes.csv\"))\n\n missing_iso = set(all_covid.location).difference(set(iso_codes.location))\n if len(missing_iso) > 0:\n print(missing_iso)\n raise Exception(\"Missing ISO code for some locations\")\n\n all_covid = iso_codes.merge(all_covid, on=\"location\")\n\n # Add continents\n print(\"Adding continents…\")\n continents = pd.read_csv(\n os.path.join(INPUT_DIR, \"owid/continents.csv\"),\n names=[\"_1\", \"iso_code\", \"_2\", \"continent\"],\n usecols=[\"iso_code\", \"continent\"],\n header=0\n )\n\n all_covid = continents.merge(all_covid, on=\"iso_code\", how=\"right\")\n\n # Add macro variables\n # - the key is the name of the variable of interest\n # - the value is the path to the corresponding file\n macro_variables = {\n \"population\": \"un/population_2020.csv\",\n \"population_density\": \"wb/population_density.csv\",\n \"median_age\": \"un/median_age.csv\",\n \"aged_65_older\": \"wb/aged_65_older.csv\",\n \"aged_70_older\": \"un/aged_70_older.csv\",\n \"gdp_per_capita\": \"wb/gdp_per_capita.csv\",\n \"extreme_poverty\": \"wb/extreme_poverty.csv\",\n \"cardiovasc_death_rate\": \"gbd/cardiovasc_death_rate.csv\",\n \"diabetes_prevalence\": \"wb/diabetes_prevalence.csv\",\n \"female_smokers\": \"wb/female_smokers.csv\",\n \"male_smokers\": \"wb/male_smokers.csv\",\n \"handwashing_facilities\": \"un/handwashing_facilities.csv\",\n \"hospital_beds_per_thousand\": \"owid/hospital_beds.csv\",\n \"life_expectancy\": \"owid/life_expectancy.csv\",\n \"human_development_index\": \"un/human_development_index.csv\",\n }\n all_covid = add_macro_variables(all_covid, macro_variables)\n\n print(\"Writing to CSV…\")\n all_covid.to_csv(os.path.join(DATA_DIR, \"owid-covid-data.csv\"), index=False)\n\n print(\"Writing to XLSX…\")\n all_covid.to_excel(os.path.join(DATA_DIR, \"owid-covid-data.xlsx\"), index=False)\n\n print(\"Writing to JSON…\")\n df_to_json(all_covid, os.path.join(DATA_DIR, \"owid-covid-data.json\"), macro_variables.keys())\n\n # Store the last updated time\n timestamp_filename = os.path.join(DATA_DIR, \"owid-covid-data-last-updated-timestamp.txt\")\n with open(timestamp_filename, \"w\") as timestamp_file:\n timestamp_file.write(datetime.utcnow().replace(microsecond=0).isoformat())\n\n print(\"All done!\")","def upload_data_new_batch(tsca_id, latest_tsca_id, paths_to_batches_info, namespace, workspace, google_bucket_id):\n paths_to_batches_info_df = pd.read_excel(paths_to_batches_info, index_col=0)\n path_to_samples_info = paths_to_batches_info_df.loc[tsca_id, 'path_to_samples_info']\n\n # DF of remote [sample < > sample set ]\n remote_sample_sets = pd.read_table('remote_files/sample_set_membership_%s.tsv'%latest_tsca_id)\n # DF of remote [pair < > pair set]\n remote_pair_sets = pd.read_table('remote_files/pair_set_membership_%s.tsv'%latest_tsca_id)\n\n all_samples = get_samples(paths_to_batches_info, google_bucket_id)\n # Add cohorts for older batches\n all_samples = add_cohort_to_old_batches(all_samples)\n\n ##### Remove blacklisted samples ##\n # Blacklisted samples\n blacklisted = pd.read_table(\"samples_blacklist.txt\", header=None, names=[\"entity:sample_id\"])\n blacklisted_sample_ids = blacklisted[\"entity:sample_id\"].values.tolist()\n all_samples = all_samples[~all_samples[\"entity:sample_id\"].isin(blacklisted_sample_ids)]\n\n ########## Participants ##########\n print(\"Uploading participants...\") \n participants = prepare_participants_for_metadata_export(path_to_samples_info, tsca_id)\n r1 = save_and_upload_participants(participants, namespace, workspace, tsca_id)\n ##################################\n\n ########## Samples ############\n print(\"Uploading samples...\")\n batch_samples = prepare_batch_samples_for_metadata_export(path_to_samples_info, tsca_id, google_bucket_id)\n r2 = save_and_upload_samples(batch_samples, namespace, workspace, tsca_id)\n #################################\n\n ########## Pairs #############\n print(\"Uploading pairs...\")\n pairs = create_pairs_list(all_samples)\n r3 = save_and_upload_pairs(namespace, workspace, pairs)\n #################################\n\n ########## Sample Sets #########\n print(\"Uploading sample sets...\")\n batch_sample_set, batch_tumor_set, batch_normal_set = prepare_batch_sample_set_for_metadata_export(path_to_samples_info, tsca_id)\n # Remove the samples that have already been uploaded \n uploaded_sample_ids = remote_sample_sets['sample'].tolist()\n batch_sample_set_clean = batch_sample_set[~batch_sample_set['sample_id'].isin(uploaded_sample_ids)]\n batch_tumor_set_clean = batch_tumor_set[~batch_tumor_set['sample_id'].isin(uploaded_sample_ids)]\n batch_normal_set_clean = batch_normal_set[~batch_normal_set['sample_id'].isin(uploaded_sample_ids)]\n r4a, r4b, r4c = save_and_upload_batch_sample_sets(batch_sample_set_clean, batch_tumor_set_clean, batch_normal_set_clean, tsca_id, namespace, workspace)\n #################################\n\n ########## PoNs ###############\n print(\"Uploading PoNs...\")\n \n # Number of latest tsca id\n latest_tsca_id_int = int(re.findall('\\d+', latest_tsca_id )[0])\n # Array with list of all previous TSCA ids\n previous_tsca_ids = [\"TSCA%s\"%i for i in np.arange(14, latest_tsca_id_int+1)]\n previous_tsca_ids.insert(0, \"TSCA1213\")\n\n pon, name = create_panel_of_normals_advanced(tsca_id, all_samples,\\\n num_normals_per_cohort_involved = 3, \\\n batches_to_pick_from = previous_tsca_ids)\n\n # Only upload PoN if it hasn't been uploaded already\n if not name in remote_sample_sets['membership:sample_set_id'].unique().tolist():\n r5 = upload_pon(pon, name, namespace, workspace) \n else: \n print(\"PoN already exists...\")\n r5 = {}\n #################################\n \n ########## Pair Set ###########\n print(\"Uploading pair sets...\")\n # Upload cumulative pair sets\n tn_cum_pairsets, tp_cum_pairsets = prepare_cumulative_pairsets_for_metadata_export(pairs, tsca_id)\n r6 = upload_pairsets(namespace, workspace, tn_cum_pairsets, \"TN\")\n r7 = upload_pairsets(namespace, workspace, tp_cum_pairsets, \"TP\")\n\n # Batch pair sets\n tn_pairsets, tp_pairsets = prepare_batch_pairsets_for_metadata_export(all_samples, pairs, tsca_id)\n uploaded_pair_ids = remote_pair_sets['pair'].tolist()\n tn_pairsets_clean = tn_pairsets[~tn_pairsets['pair_id'].isin(uploaded_pair_ids)]\n tp_pairsets_clean = tp_pairsets[~tp_pairsets['pair_id'].isin(uploaded_pair_ids)]\n\n r8 = upload_pairsets(namespace, workspace, tn_pairsets_clean, \"TN\")\n r9 = upload_pairsets(namespace, workspace, tp_pairsets_clean, \"TP\")\n #################################\n\n return (r1, r2, r3, r4a, r4b, r4c, r5, r6, r7, r8, r9)","def concatenate_sft_files(self):\n\n SFTFilename = (\n f\"{self.detectors[0]}-{self.nsfts}_{self.detectors}_{self.Tsft}SFT_mfdv4\"\n )\n # We don't try to reproduce the NB filename convention exactly,\n # as there could be always rounding offsets with the number of bins,\n # instead we use wildcards there.\n outfreq = int(np.floor(self.fmin))\n outwidth = int(np.floor(self.Band))\n SFTFilename += f\"_NBF{outfreq:04d}Hz*W{outwidth:04d}Hz*\"\n SFTFilename += f\"-{self.tstart}-{self.duration}.sft\"\n SFTFile_fullpath = os.path.join(self.outdir, SFTFilename)\n if os.path.isfile(SFTFile_fullpath):\n logger.info(\n f\"Removing previous file(s) {SFTFile_fullpath} (no caching implemented).\"\n )\n os.remove(SFTFile_fullpath)\n\n inpattern = os.path.join(self.tmp_outdir, \"*sft\")\n cl_splitSFTS = \"lalpulsar_splitSFTs\"\n cl_splitSFTS += \" -fs {} -fb {} -fe {} -n {} -- {}\".format(\n self.fmin, self.Band, self.fmin + self.Band, self.outdir, inpattern\n )\n utils.run_commandline(cl_splitSFTS)\n utils.run_commandline(f\"rm -r {self.tmp_outdir}\")\n outglob = glob.glob(SFTFile_fullpath)\n if len(outglob) != 1:\n raise IOError(\n \"Expected to produce exactly 1 merged file\"\n f\" matching pattern '{SFTFile_fullpath}',\"\n f\" but got {len(outglob)} matches: {outglob}\"\n \" Something went wrong!\"\n )\n self.sftfilepath = outglob[0]\n logger.info(f\"Successfully wrote SFTs to: {self.sftfilepath}\")","def makeJob(kallisto, index, meta, bootstraps, files, single, s=1, l=180): \n cmd = \"%(kallisto)s quant -i %(index)s -o %(meta)s \" % locals()\n for file in files: \n cmd += \" ../%s\" % file \n if single: \n cmd += \" --single -l %(l)i -s %(s)i\" % locals()\n cmd += \" &> %s.log.txt\" % meta\n return cmd","def buildClusters(self):\n oldLatFile = 'needed_files/lat.in'\n oldFile = open(oldLatFile, 'r')\n oldLines = [line for line in oldFile]\n oldFile.close()\n \n newFile = open('enum/lat.in','w')\n for i in xrange(len(oldLines)):\n if 'Number pairs' in oldLines[i-1] and i>=1: #bch use label on previous line\n for num in self.clusterNums:\n newFile.write(str(num) + \" \")\n newFile.write(\"\\n\")\n else:\n newFile.write(oldLines[i])\n newFile.close()\n \n lastDir = os.getcwd()\n os.chdir(lastDir + '/enum')\n if sum(self.clusterNums)<=1500: #the 1500 assumes you are running Main with 16G. \n subprocess.call([self.uncleExec, '10'], stdout=self.uncleOut)\n else:\n subprocess.call(['echo','Warning: BLOCKING CLUSTER JOB to save time'])\n# clustersjob = ClustersBuild.clustersjob()\n# clustersjob.clustBuild()\n# \n os.chdir(lastDir)","def regenerate_gas_strat_lut_files():\n for record in gas_records.values():\n record(force_strat_calculation=True, save_strat=True)","def quant(input_folder,fastq_dict,species_kmers,output_folder,transcriptome_folder,bsub_out=\"bsub_out\"):\n print(\"Starting new quantification run for batch of %d samples from %s\"%(len(fastq_dict),input_folder))\n mkdir_p(bsub_out)\n #no subfolders needed for SRA data\n print(\"bsub logs stored in %s folder\"%bsub_out)\n mkdir_p(output_folder)\n print(\"kallisto output in %s\"%output_folder)\n for i in fastq_dict:\n print(\"===processing fastq files from sample ID: %s===\"%i)\n outdir = path.join(output_folder,i) #separate folder for each fastq, within the output folder\n mkdir_p(outdir)\n cmd = kw.CMD_BASE.format(fastq_id=i,bsub_out=bsub_out)\n cmd = shlex.split(cmd) #convert to list of arguments\n species = fastq_dict[i][\"species\"]\n t_index = path.join(transcriptome_folder,kw.species2transcriptomeindex(species,kmer_size=species_kmers[species]))\n f1 = [path.join(input_folder,r+\"_1.fastq.gz\") for r in fastq_dict[i][\"fastq_list\"]]\n if fastq_dict[i][\"is_paired_end\"]:\n f2 = [path.join(input_folder,r+\"_2.fastq.gz\") for r in fastq_dict[i][\"fastq_list\"]]\n flist = \" \".join(imap(lambda x,y: x+\" \"+y,f1,f2))\n cmd.append(\"kallisto quant -i {ti} -o {out} {flist}\".format(ti=t_index,out=outdir,flist = flist))\n else: #case of single end reads\n flen = median(array(fastq_dict[i][\"avgLengths\"]))\n flist = \" \".join(f1)\n cmd.append(\"kallisto quant --single -i {ti} -o {out} -l {flen} -s {fsd} {flist}\".format(ti=t_index,out=outdir,flen=flen,fsd=flen/5.0,flist = flist))\n #note, fsd is the standard deviation of the fragment length distribution. flen/5 is just a placeholder. We should actually estimate this in the future!\n #print(cmd)\n subprocess.call(cmd)","def Param_gen_and_write(self):\n SF_start_params = self.source_df.groupby(\n \"FinalID\").apply(self.start_params)\n SF_start_params.reset_index(level=0, inplace=True)\n self.source_df = pd.merge(\n self.source_df,\n SF_start_params,\n on=\"FinalID\")\n self.source_df.to_csv(\"../Data/Biotraits_with_start_params.csv\")","def build_input_file(self, replica):\n\n file_name = self.inp_basename + \"_\" + \\\n str(replica.id) + \"_\" + \\\n str(replica.cycle) + \".md\"\n\n fo = open(file_name, \"wb\")\n for i in range(1,500):\n fo.write(str(random.randint(i, 500) + i*2.5) + \" \");\n if i % 10 == 0:\n fo.write(str(\"\\n\"));\n fo.close()","def run(self):\n first_index, last_index = \\\n self.get_initial_blocks()\n while last_index - first_index > self.block_size:\n first_index, last_index = \\\n self.join_blocks(first_index, last_index)\n self.merge_blocks(self.output_file_name, first_index, last_index)","def build_batch_sampler(\n self,\n build_batch_sampler: dict,\n target_dir: str,\n cache_dir: str,\n mode: str,\n data_csv: str,\n dataset: Dataset,\n ):\n\n def _build_batch_sampler(\n train: dict = None, valid: dict = None, test: dict = None\n ):\n if mode == \"train\":\n return FixedBatchSizeBatchSampler(dataset, **train)\n elif mode == \"valid\":\n return FixedBatchSizeBatchSampler(dataset, **valid)\n elif mode == \"test\":\n return FixedBatchSizeBatchSampler(dataset, **test)\n\n return _build_batch_sampler(**build_batch_sampler)","def gtf_processing(genome=None, prefix='gencov'):\n all_bed = prefix + \".all.bed\"\n\n if not os.path.exists(all_bed) or os.stat(all_bed).st_size == 0:\n log.info(\"Preprocessing annotation...\")\n features = ('exon', 'gene', 'intron', 'intergenic')\n merged_exons, merged_genes = map(preprocess, features[:2])\n ins = {\n 'intron': [merged_genes, merged_exons],\n 'intergenic': [merged_genes, genome]\n }\n intron_bed, intergenic_bed = map(preprocess, features[2:], [ins, ins])\n\n log.info(\"Concatenate bed files for all elements...\")\n with open(all_bed, 'w') as out_bed:\n cat_all(merged_exons, merged_genes, intron_bed, intergenic_bed, out_bed=out_bed)\n\n for f in (merged_exons, merged_genes, intron_bed, intergenic_bed):\n os.remove(f)\n\n return all_bed","def get_batches(variants, batch_queue, header, vep=False, results_queue=None, \n annotation_keyword = 'Annotation'):\n\n logger.debug(\"Set beginning to True\")\n beginning = True\n logger.debug(\"Create first empty batch\")\n # A batch is a ordered dictionary with variants\n batch = OrderedDict()\n new_chrom = None\n current_chrom = None\n current_features = set()\n chromosomes = []\n\n start_parsing_time = datetime.now()\n start_chrom_time = start_parsing_time\n start_twenty_time = start_parsing_time\n \n nr_of_variants = 0\n nr_of_batches = 0\n \n header_line = header.header\n vep_header = header.vep_columns\n logger.info(\"Start parsing the variants\")\n \n for line in variants:\n if not line.startswith('#'):\n \n variant = get_variant_dict(line, header_line)\n variant_id = get_variant_id(variant)\n variant['variant_id'] = variant_id\n variant['info_dict'] = get_info_dict(variant['INFO'])\n\n if vep:\n variant['vep_info'] = get_vep_dict(\n vep_string=variant['info_dict']['CSQ'], \n vep_header=vep_header,\n allele=variant['ALT'].split(',')[0]\n )\n \n logger.debug(\"Checking variant {0}\".format(variant_id))\n\n nr_of_variants += 1\n new_chrom = variant['CHROM']\n if new_chrom.startswith('chr'):\n new_chrom = new_chrom[3:]\n\n logger.debug(\"Update new chrom to {0}\".format(new_chrom))\n\n new_features = get_annotation(\n variant = variant, \n vep = vep,\n annotation_key = annotation_keyword\n )\n logger.debug(\"Adding {0} to variant {1}\".format(\n ', '.join(new_features), variant_id\n ))\n\n variant['annotation'] = new_features\n\n if nr_of_variants % 20000 == 0:\n logger.info(\"{0} variants parsed\".format(nr_of_variants))\n logger.info(\"Last 20.000 took {0} to parse.\".format(\n str(datetime.now() - start_twenty_time)))\n start_twenty_time = datetime.now()\n\n if beginning:\n logger.debug(\"First variant.\")\n current_features = new_features\n\n logger.debug(\"Adding %s to variant batch\" % variant_id)\n batch[variant_id] = variant\n\n logger.debug(\"Updating current chrom to {0}\".format(new_chrom))\n current_chrom = new_chrom\n\n chromosomes.append(current_chrom)\n logger.debug(\"Adding chr {0} to chromosomes\".format(new_chrom)) \n\n beginning = False\n logger.debug(\"Updating beginning to False\")\n\n else:\n # If we should put the batch in the queue:\n logger.debug(\"Updating send to True\") \n send = True\n \n # Check if the variant ovelapps any features\n if len(new_features) != 0:\n # Check if the features overlap the previous variants features\n if new_features.intersection(current_features):\n logger.debug(\"Set send to False since variant features overlap\") \n send = False\n # If we are at a new chromosome we finish the current batch:\n if new_chrom != current_chrom:\n if current_chrom not in chromosomes:\n chromosomes.append(current_chrom)\n logger.debug(\"Adding chr {0} to chromosomes\".format(new_chrom)) \n # New chromosome means new batch\n send = True\n logger.info(\"Chromosome {0} parsed. Time to parse\"\\\n \" chromosome: {1}\".format(\n current_chrom, datetime.now()-start_chrom_time))\n start_chrom_time = datetime.now()\n current_chrom = new_chrom\n\n if send:\n # Put the job in the queue\n if len(batch) > 0:\n logger.debug(\"Adding batch in queue\")\n batch_queue.put(batch)\n nr_of_batches += 1\n #Reset the variables\n current_features = new_features\n logger.debug(\"Initializing empty batch\") \n batch = {}\n else:\n current_features = current_features.union(new_features)\n \n # Add variant to batch\n batch[variant_id] = variant\n\n if current_chrom not in chromosomes:\n logger.debug(\"Adding chr {0} to chromosomes\".format(current_chrom))\n chromosomes.append(current_chrom)\n\n logger.info(\"Chromosome {0} parsed. Time to parse\"\\\n \" chromosome: {0}\".format(\n current_chrom, datetime.now()-start_chrom_time))\n\n if len(batch) > 0:\n nr_of_batches += 1\n batch_queue.put(batch)\n logger.debug(\"Adding batch to queue\") \n\n logger.info(\"Variants parsed. Time to parse variants: {0}\".format(\n str(datetime.now() - start_parsing_time)\n ))\n\n logger.info(\"Number of variants in variant file: {0}\".format(nr_of_variants))\n logger.info(\"Number of batches created: {0}\".format(nr_of_batches))\n\n return chromosomes","def combine(self):\n\n import re\n \n print 'Creating file', self.__filename\n \n bname = (os.path.split(self.__filename))[1]\n bname2 = bname\n \n # bugfix: if file contains characters like +,.,[]\n # properly escape them, otherwise re will fail to match.\n for a, b in zip(['+', '.', '[', ']','$', '(', ')'],\n ['\\+','\\.','\\[','\\]','\\$', '\\(', '\\)']):\n bname2 = bname2.replace(a, b)\n \n chunkre = re.compile(bname2 + '-' + '[0-9]+')\n \n chunkfiles = []\n for f in os.listdir(\".\"):\n print f\n if chunkre.match(f):\n chunkfiles.append(f)\n\n\n print 'Number of chunks', len(chunkfiles), '\\n'\n chunkfiles.sort(self.sort_index)\n\n data=''\n for f in chunkfiles:\n\n try:\n print 'Appending chunk', os.path.join(\".\", f)\n data += open(f, 'rb').read()\n except (OSError, IOError, EOFError), e:\n print e\n continue\n\n try:\n f = open(bname, 'wb')\n f.write(data)\n f.close()\n except (OSError, IOError, EOFError), e:\n raise FileSplitterException, str(e)\n\n print 'Wrote file', bname","def go(self):\n\n self._write_master()\n num_fofs = self.fofs['fofid'].max()\n fof_splits = split.get_splits(num_fofs, self['chunksize'])\n\n njobs=0\n fobj=None\n\n icondor=0\n for isplit,fof_split in enumerate(fof_splits):\n if njobs % self['jobs_per_sub']==0:\n if fobj is not None:\n fobj.close()\n fobj = self._open_condor_script(icondor)\n icondor += 1\n\n self._write_split(fobj, isplit, fof_split)\n\n njobs += 1","def summarize(self):\n self.smalltalk += \"\\n Data IDs in this bundle: \\n\"\n self._files = {}\n inv_dict = {}\n # sort IDs to make sure pdfs are printed in same oder as they were\n # taken\n for k, v in self.stage_summaries.items():\n for qc_id in flatten_list(v):\n inv_dict[qc_id] = k\n sorted_ids = list(flatten_list(self.stage_summaries.values()))\n sorted_ids.sort(key=int)\n # for stage, value in self.stage_summaries.items():\n for qc_run_id in sorted_ids:\n # stage = inv_dict[qc_run_id]\n # if stage[0:7] == 'failed_':\n # stage = stage[7:]\n # try:\n # s = self.comments[qc_run_id]\n # except KeyError:\n # s = ''\n # self.comments[qc_run_id] = 'Classified as poor result.\\n' + s\n ds = Dataset(qc_run_id, self.db_name)\n device_name = ds.device_name\n f_folder = os.path.join(self.db_folder, \"tuning_results\", device_name)\n # for qc_run_id in flatten_list(value):\n self.smalltalk += str(qc_run_id) + \", \"\n\n # filename = stage + '_fit_ds'\n # filename += str(qc_run_id) + '.png'\n filename = os.path.join(f_folder, str(ds.ds.guid) + \".png\")\n\n self._files[str(qc_run_id)] = filename","def stim_build(stim_file, random_prototype_selection):\n\n\t# Open training file and turn each row into a list of integers instead of strings. Count the number of stimulus (rows) there are in the file. \n\twith open(stim_file, encoding='utf-8-sig') as sf:\n\t\tsf = csv.reader(sf)\n\t\trows = list(sf)\n\t\tcount = (list(range(len(rows))))\n\n\t# Make a new list that contains a list for each row in the stimlus file as integers instead of strings.\n\t\tstimulus_list = []\n\t\tfor i in count:\n\t\t\tnew_row = []\n\t\t\tfor element in rows[i]:\n\t\t\t\tnew_row.append(int(element))\n\t\t\tstimulus_list.append(new_row)\n\n\t# create a list of all 1's to compare to the randomly selected prototype later\n\trandpro = random_prototype_selection\n\tall1proto = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n\n\t# Compare the randomly selected prototype to the all 1's. If it is the same, then take the stimulus file as is and add the necessary columns.\n\tif random_prototype_selection == all1proto:\n\t\n\t\tstim_id = []\n\t\tsums_feat = []\n\t\tcategory = []\n\t\tcorrect_key = []\n\t\tfor item in stimulus_list:\n\t\t\t# calculate the sum of the number of features each stimulus has in common with the protoype\n\t\t\tsums = sum(item)\n\t\t\tsums_feat.append(sums)\n\n\t\t\t# define the categories based on sum_feat \n\t\t\tif sums > 5:\n\t\t\t\tcat = 'batmans'\n\t\t\telse:\n\t\t\t\tcat = 'robins'\n\n\t\t# define which key is correct based on the category it is assigned\n\t\t\tif cat == 'batmans':\n\t\t\t\tkey_corr = 'f'\n\t\t\telif cat == 'robins':\n\t\t\t\tkey_corr = 'j'\n\t\t\tcorrect_key += [key_corr]\n\n\t\t# append cat to category. Have to use different operation because .append doesn't work for strings\n\t\t\tcategory += [cat]\n\n\t\t\t# now that all the manipulations are done, we can to concatenate the recoded stimuli to match the file names\n\t\t\tstring = ''.join([str(item) for item in item])\n\t\t\t# added quotations so that the leading zeros are not removed when writing to the csv file. Will take them off in psychopy\n\t\t\tstim_id += [string]\n\t\t\n\t# If the randomly selected prototype is not all 1's then we need to recode the stimulus and add the necessary rows. \n\telse:\n\t\t# get the number of stimuli in the stim_file and create a list of the range of numbers.\n\t\tcount_stim_list = list(range(len(stimulus_list)))\n\t\t\n\t\t# count the number of items in the prototype and create a list for the range of numbers. Should always be 10 for the 10 features.\n\t\tcount_randpro = (list(range(len(randpro))))\n\t\n\t\t# create empty list for each column we want in our csv\n\t\tcomparison = []\n\t\tstim_id = []\n\t\tsums_feat = []\n\t\tcategory = []\n\t\tcorrect_key = []\n\n\t\t# make a new variable defining the stimulus we are currently working with as \"stim\"\n\t\tfor i in count_stim_list:\n\t\t\tstim = stimulus_list[i]\n\n\t\t\t# create another empty list to store the comparisons between the prototype and the current stimulus\n\t\t\tnew_code = []\n\t\t\tfor n in count_randpro:\n\t\t\t\tif stim[n] == randpro[n]:\n\t\t\t\t\toutput = bool('True')\n\t\t\t\telse:\n\t\t\t\t\toutput = bool('')\n\n\t\t\t\t# turn the boolean phrases into into integers for the new code. Append each item to the rest of the items within the stimulus\n\t\t\t\tnew_code.append(output * 1)\n\n\t\t\t# Add the recoded stimuli into the same list. We only need this list to check that the recode worked. It won't be going in the csv\n\t\t\tcomparison.append(new_code)\n\n\t\t\t# compare the new code to the stimuli to get the number of features that match so we can add them up and determine the category\n\t\t\tcompare_cat = []\n\t\t\tfor x in count_randpro:\n\t\t\t\tif new_code[x] == randpro[x]:\n\t\t\t\t\toutput = bool('True')\n\t\t\t\telse:\n\t\t\t\t\toutput = bool('')\n\t\t\t\tcompare_cat.append(output*1)\n\n\t\t\t# find a sum of the number of features each stimulus has in common with the prototype\n\t\t\tsums = sum(compare_cat)\n\t\t\tsums_feat.append(sums)\n\t\t\n\t\t\t# define the categories based on the number of features each stimulus has in common with the prototype\n\t\t\tif sums > 5:\n\t\t\t\tcat = 'batmans'\n\t\t\telse:\n\t\t\t\tcat = 'robins'\n\t\t\tcategory += [cat]\n\n\t\t\t# define which key is correct based on the category it is assigned\n\t\t\tif cat == 'batmans':\n\t\t\t\tkey_corr = 'f'\n\t\t\telif cat == 'robins':\n\t\t\t\tkey_corr = 'j'\n\t\t\tcorrect_key += [key_corr]\n\n\t\t\t# now that all the manipulations are done, we can to concatenate the recoded stimuli to match the file names\n\t\t\tstring = ''.join([str(item) for item in new_code])\n\t\t\t# added quotations so that the leading zeros are not removed when writing to the csv file. Will take them off in psychopy\n\t\t\tstim_id += [string]\n\t\t\n\t# write the results to a new csv file. \n\twith open('/Volumes/shares/Cabi/exp/joplin/joplin1/data/_stimfile.csv', 'w', newline='') as stim_file:\n\t\twriter = csv.writer(stim_file)\n\t\twriter.writerow(('stim_id', 'sums_feat', 'category', 'correct_key'))\n\t\tfor i in range(len(stim_id)):\n\t\t\twriter.writerow((stim_id[i], sums_feat[i], category[i], correct_key[i]))","def make_master_flats(dc):\n\n\t## Make EXTcheck: is there always the same number of extensions in each file\n\tprint \"Making master flats\"\n\t\n\t## Choose extensions you are using\n\t\n\tfor flat_type in ['FFS']: # Currently FFD is unsupported. If you have FFDs, add them to the list but you must have ONLY FFDs or ONLY FFSs in the dir. Otherwise the first element in the list will get overwritten!\n\t\t#~ print \"\\n\", flat_type, \"\\n\"\n\t\tfor i in dc:\n\t\t\tTRIM, TRIM1, VR, PS, PS1, OS, OS1 = CCD_sections((i[0], i[1]))\n\t\t\tfilelist = []\n\t\t\tfor f in glob.glob(RAW+'*'+flat_type+'*fits'):\n\t\t\t\tccd_conf = []\n\t\t\t\theader0 = fits.getheader(f)\n\t\t\t\theader1 = fits.getheader(f, ext=1)\n\t\t\t\tif header0['OBSMODE']==flat_type:\n\t\t\t\t\tfor KW in ['BINX', 'BINY']:\n\t\t\t\t\t\tccd_conf.append(header0[KW])\n\t\t\t\t\tfor KW in ['NAXIS1', 'NAXIS2']:\n\t\t\t\t\t\tccd_conf.append(header1[KW])\n\t\t\t\t\t\tif tuple(ccd_conf)==i:\n\t\t\t\t\t\t\tfilelist.append(f)\n\t\t\tlfl = len(filelist)\n\t\t\tif lfl > 0:\n\t\t\t\tBIN=CD+'/'+str(i[0])+'x'+str(i[1])+'/'\n\t\t\t\tWD=BIN+str(i[-2])+'x'+str(i[-1])+'/' # Bottom level dir with calibrated and master frames\n\t\t\t\tB=check_exist(WD, 'MF.fits', i)\n\t\t\t\tif B=='n':\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\thdul = fits.HDUList()\n\t\t\t\t\thdul.append(fits.ImageHDU())\n\t\t\t\t\t#~ MB = fits.open(WD+'MB.fits')\n\t\t\t\t\tx = np.array(range(0,i[-1]))\n\t\t\t\t\tfor EXT in (extensions):\n\t\t\t\t\t\tprint \"##################################################\"\n\t\t\t\t\t\tprint \"Stacking \"+`lfl`+' '+`i[-2]`+'x'+`i[-1]`+' channel '+`EXT`+' flat frames!'\n\t\t\t\t\t\tif EXT==1:\n\t\t\t\t\t\t\tPSC=PS1\n\t\t\t\t\t\t\tOSC=OS1\n\t\t\t\t\t\t\tTR=TRIM1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tPSC=PS1\n\t\t\t\t\t\t\tOSC=OS\n\t\t\t\t\t\t\tTR=TRIM\n\t\t\t\t\t\tsc = -1 # counts how many flats have mean>limit\n\t\t\t\t\t\tfor n, fn in enumerate(filelist):\n\t\t\t\t\t\t\tprint \"Files left:\",`lfl-n`+'/'+`lfl`\n\t\t\t\t\t\t\tim = fits.getdata(fn, ext=EXT)\n\t\t\t\t\t\t\tmeanval = np.mean(im[VR[0]:VR[1], TR[0]:TR[1]])\n\t\t\t\t\t\t\t#~ maxval = np.max(im[VR[0]:VR[1], TR[0]:TR[1]])\n\t\t\t\t\t\t\tmaxval = stats.scoreatpercentile(im[VR[0]:VR[1], TR[0]:TR[1]], 90)\n\t\t\t\t\t\t\texptime = fits.getheader(fn)['EXPTIME']\n\t\t\t\t\t\t\t#~ if meanval > 15000. and meanval < 40000. and maxval < 50000. and exptime>5.:\n\t\t\t\t\t\t\tif meanval > 16000. and meanval < 40000. and exptime>=5.:\n\t\t\t\t\t\t\t\tsc+=1\n\t\t\t\t\t\t\t\t#~ im[im<1]=1\n\t\t\t\t\t\t\t\tmscrow, sigmarow = median_row(OSC, PSC, TR, im)\n\t\t\t\t\t\t\t\tsh = np.shape(im)\n\t\t\t\t\t\t\t\tfor y in range(0, sh[0]):\n\t\t\t\t\t\t\t\t\tim[y] = im[y]-mscrow[y]\n\t\t\t\t\t\t\t\tF=im\n\t\t\t\t\t\t\t\tnorm = np.median(F[VR[0]:VR[1], TR[0]:TR[1]])\n\t\t\t\t\t\t\t\tF = F/norm #+np.min(F)+0.0001\n\t\t\t\t\t\t\t\tif sc==0:\n\t\t\t\t\t\t\t\t\tstack_arr = F\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tstack_arr = np.dstack((stack_arr, F))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tprint \"****************************************\"\n\t\t\t\t\t\t\t\tprint \"Rejected\", fn, \"AVG =\", meanval, \"EXPTIME =\", exptime\n\t\t\t\t\t\t\t\tprint \"****************************************\"\n\t\t\t\t\t\tprint 'Will stack a total of', np.shape(stack_arr)[2], 'flats'\n\t\t\t\t\t\tMF = np.median(stack_arr, axis=2)\n\t\t\t\t\t\thdul.append(fits.ImageHDU(MF))\n\t\t\t\t\t\thdul[EXT].header.set(\"NAXIS1\", np.shape(MF)[1])\n\t\t\t\t\t\thdul[EXT].header.set(\"NAXIS2\", np.shape(MF)[0])\n\t\t\t\t\thdul[0].header.set(\"CALIBR\", \"T\")\n\t\t\t\t\thdul[0].header.set(\"INSTRUME\", \"MAIA\")\n\t\t\t\t\thdul[0].header.set(\"BINX\", i[0])\n\t\t\t\t\thdul[0].header.set(\"BINY\", i[1])\n\t\t\t\t\thdul[0].header.set(\"CALMODE\", \"MASTER FLAT\")\n\t\t\t\t\thdul.writeto(WD+\"MF.fits\", clobber=True)\n\t\t\t\t\tprint \"############################################################\"\n\tprint \"Completed master flats\"","def build_indices(genome_fasta, genome_gtf, rRNA_fasta, transcriptome_fasta):\n \n if not os.path.exists(\"data/indices\"):\n os.mkdir(\"data/indices\")\n\n \n # 1. Bowtie index\n print(\"Building Bowtie index\")\n if not os.path.exists(BOWTIE_DIR):\n os.mkdir(BOWTIE_DIR)\n cmd_bowtie = 'bowtie-build' + ' ' + genome_fasta + ' ' + BOWTIE_DIR+'/yeast'\n output = subprocess.run(cmd_bowtie, shell=True)\n\n cmd_rRNA = 'bowtie-build' + ' ' + rRNA_fasta + ' ' + BOWTIE_DIR+'/rRNA'\n output = subprocess.run(cmd_rRNA, shell=True)\n \n # 2. STAR index\n print(\"Building STAR index\")\n if not os.path.exists(STAR_DIR):\n os.mkdir(STAR_DIR)\n cmd_STAR = 'STAR' + ' ' + '--runThreadN' + ' ' + '4' + ' ' + '--runMode' + ' ' + 'genomeGenerate' + ' ' + '--genomeDir' + ' ' + STAR_DIR + ' ' + '--genomeFastaFiles' + ' ' + genome_fasta + ' ' + '--sjdbGTFfile' + ' ' + genome_gtf #+ ' ' + '--sjdbOverhang' + ' ' + 'max(ReadLength)-1'\n output = subprocess.run(cmd_STAR, shell=True)\n\n\n# run build transcriptome fasta. \n if not os.path.exists(STAR_TRANSCRIPTOME_DIR):\n os.mkdir(STAR_TRANSCRIPTOME_DIR)\n cmd_STAR = 'STAR' + ' ' + '--runThreadN' + ' ' + '4' + ' ' + '--runMode' + ' ' + 'genomeGenerate' + ' ' + '--genomeDir' + ' ' + STAR_TRANSCRIPTOME_DIR + ' ' + '--genomeFastaFiles' + ' ' + transcriptome_fasta # + ' ' + '--sjdbGTFfile' + ' ' + genome_gtf #+ ' ' + '--sjdbOverhang' + ' ' + 'max(ReadLength)-1'\n output = subprocess.run(cmd_STAR, shell=True)","def batch_apply(self, batch, is_train=False, stats=None, **kwargs):\n if self.max_context == 0:\n return batch\n trf_batch = []\n doc = {}\n doc[\"src\"] = []\n doc[\"tgt\"] = []\n doc[\"indices\"] = 0\n\n for ex, _, cid in batch:\n if ex[\"tgt\"] is not None:\n cur_len = max(len(doc[\"src\"] + ex[\"src\"]), len(doc[\"tgt\"] + ex[\"tgt\"]))\n\n if len(ex[\"src\"]) == 0 and len(ex[\"tgt\"]) == 0:\n # doc break we add it, restart new doc\n trf_batch.append((doc, self, cid))\n doc = {}\n doc[\"src\"] = []\n doc[\"tgt\"] = []\n doc[\"indices\"] = ex[\"indices\"]\n elif cur_len > self.doc_length:\n if len(doc[\"src\"]) == 0:\n # case 1st ex is already longer\n trf_batch.append((ex, self, cid))\n else:\n # adding cur ex is too long we add cur doc\n # and reset doc to cur ex\n trf_batch.append((doc, self, cid))\n doc = copy.deepcopy(ex)\n else:\n if len(doc[\"src\"]) == 0:\n # we start the new doc with cur ex\n doc = copy.deepcopy(ex)\n else:\n # we cumulate cur ex to cur doc\n doc[\"src\"] += [DefaultTokens.SEP] + ex[\"src\"]\n doc[\"src_original\"] += [DefaultTokens.SEP] + ex[\"src_original\"]\n doc[\"tgt\"] += [DefaultTokens.SEP] + ex[\"tgt\"]\n doc[\"tgt_original\"] += [DefaultTokens.SEP] + ex[\"tgt_original\"]\n nb_ctx = doc[\"src\"].count(DefaultTokens.SEP)\n if nb_ctx >= self.max_context:\n trf_batch.append((doc, self, cid))\n doc = {}\n doc[\"src\"] = []\n doc[\"tgt\"] = []\n doc[\"indices\"] = ex[\"indices\"]\n else:\n cur_len = len(doc[\"src\"] + ex[\"src\"])\n doc[\"tgt\"] = None\n if len(ex[\"src\"]) == 0:\n trf_batch.append((doc, self, cid))\n doc = {}\n doc[\"src\"] = []\n doc[\"indices\"] = ex[\"indices\"]\n elif cur_len > self.doc_length:\n if len(doc[\"src\"]) == 0:\n trf_batch.append((ex, self, cid))\n else:\n trf_batch.append((doc, self, cid))\n doc = copy.deepcopy(ex)\n else:\n if len(doc[\"src\"]) == 0:\n doc = copy.deepcopy(ex)\n else:\n doc[\"src\"] += [DefaultTokens.SEP] + ex[\"src\"]\n doc[\"src_original\"] += [DefaultTokens.SEP] + ex[\"src_original\"]\n nb_ctx = doc[\"src\"].count(DefaultTokens.SEP)\n if nb_ctx >= self.max_context:\n trf_batch.append((doc, self, cid))\n doc = {}\n doc[\"src\"] = []\n doc[\"indices\"] = ex[\"indices\"]\n if len(doc[\"src\"]) > 0:\n trf_batch.append((doc, self, cid))\n return trf_batch","def compress_feats(self):\n mkpath(os.path.join(self.base_update_path,'comp_features'))\n mkpath(os.path.join(self.base_update_path,'comp_idx'))\n args = [self.base_update_path+'/', str(self.features_dim), '1', self.master_update_file, str(self.bits_num)]\n subprocess_command = [self.hashing_execpath+\"compress_feats\"] + args\n # this will work only if features to be compressed are present in self.base_update_path/features\n proc = subprocess.Popen(subprocess_command, stdout=subprocess.PIPE)\n print \"[HasherSwig.compress_feats: log] running command: {}\".format(subprocess_command)\n (out, err) = proc.communicate()\n print \"[HasherSwig.compress_feats: log] program output:\", out\n print \"[HasherSwig.compress_feats: log] program error:\", err","def build_job_scripts(model_list, scenario_list, output_dir, cassandra_config_dir, cassandra_log_dir,\n cassandra_main_script, sbatch_account, sbatch_partition='slurm', sbatch_walltime='01:00:00',\n sbatch_ntasks=3, sbatch_nodes=3, sbatch_jobname='cassie', sbatch_logdir='.', template=None):\n\n # use default configuration template file if user does not give one\n if template is None:\n template = pkg_resources.resource_filename('cassie', 'data/sbatch_template.sh')\n\n # existing tags to replace in the template file\n model_tag = '<model>'\n scenario_tag = '<scenario>'\n account_tag = '<account>'\n partition_tag = '<partition>'\n ntasks_tag = '<ntasks>'\n nodes_tag = '<nodes>'\n time_tag = '<walltime>'\n jobname_tag = '<jobname>'\n logdir_tag = '<logdir>'\n cassandra_configdir_tag = '<cassconfigdir>'\n cassandra_logdir_tag = '<casslogdir>'\n cassandra_script_tag = '<cassmainscript>'\n\n for model in model_list:\n for scenario in scenario_list:\n\n output_file = os.path.join(output_dir, f'run_{model.lower()}_{scenario}.sh')\n\n with open(output_file, 'w') as out:\n with open(template) as get:\n\n f = get.read()\n\n # replace tag names with dynamic content\n fx = f.replace(model_tag, model)\n fx = fx.replace(scenario_tag, scenario)\n\n fx = fx.replace(account_tag, sbatch_account)\n fx = fx.replace(partition_tag, sbatch_partition)\n fx = fx.replace(ntasks_tag, str(sbatch_ntasks))\n fx = fx.replace(nodes_tag, str(sbatch_nodes))\n fx = fx.replace(time_tag, sbatch_walltime)\n fx = fx.replace(jobname_tag, sbatch_jobname)\n fx = fx.replace(logdir_tag, sbatch_logdir)\n\n fx = fx.replace(cassandra_configdir_tag, cassandra_config_dir)\n fx = fx.replace(cassandra_logdir_tag, cassandra_log_dir)\n fx = fx.replace(cassandra_script_tag, cassandra_main_script)\n\n out.write(fx)","def build_index(fasta_fp, index_fp):\n subprocess.call([\" \".join([\"bowtie2-build\", fasta_fp, index_fp])],\n shell=True)","def gappyBlocks():\n path = \"./data/\"\n for file in os.listdir(path):\n if file.endswith(\".fa\") or file.endswith(\".fasta\"):\n alignin = AlignIO.read(path + file, \"fasta\")\n try:\n filecore = file.rstrip(\".fa\")\n except:\n filecore = file.rstrip(\".fasta\")\n fileout = path + filecore + \".blocks\"\n \n # constants\n align = []\n gap = []\n border = []\n blocks = []\n \n # specify cut-off of gaps in column (in percentage)\n cut_min = 0.1\n cut_max = 0.9\n \n # alignment\n for pos in range(0,(alignin.get_alignment_length())):\n column=alignin[:,pos]\n align.append(column)\n if \"-\" in column:\n col=list(column)\n gaps=col.count(\"-\")\n if gaps > (cut_min*len(col)) and gaps < (cut_max*len(col)):\n gap.append(pos)\n \n if gap != []:\n border.append(gap[0])\n border.append(gap[len(gap)-1])\n for i in range(0,(len(gap)-1)):\n if int(gap[i]+1)!=int(gap[i+1]):\n border.append(gap[i])\n \n for j in range((len(gap)-1), 0, -1):\n if int(gap[j]-1)!=int(gap[j-1]):\n border.append(gap[j])\n # list of positions for the blocks\n order=sorted(border)\n \n # get the blocks and writes to the .blocks file\n o=open(fileout, \"w\")\n \n for i in range(0,len(order)-1,2):\n beg=int(order[i])\n end=int(order[i+1])\n count = end-beg \n block=alignin[:,beg:end]\n \n # specify the minimum length of a gap\n if count < 3:\n pass\n else: \n blocks.append(block) \n o.write('***Block***'+\"\\n\"+\"Start:\"+str(beg)+\\\n \"\\n\"+\"Count:\"+str(count)+\"\\n\")\n for record in block:\n o.write(str(record.seq)+\"\\n\")\n o.close()\n else:\n o=open(fileout, \"w\")\n o.close()\n pass\n return","def write_pbs(self):\n fout = open(\"runStarCCM.pbs\", \"w\")\n fout.write(\"#PBS -S /bin/csh\\n\")\n fout.write(\"#PBS -l select=\" + str(self.numNodes) + \":ncpus=\" + str(self.numCPUs) + \":mpiprocs=\" + str(self.mpiProcs) + \":model=has,walltime=\" + self.WallTime + \"\\n\\n\")\n fout.write(\"#PBS -W group_list=\" + self.GroupID + \"\\n\")\n fout.write(\"#PBS -j oe\\n\")\n fout.write(\"#PBS -q \" + self.queue + \"\\n\")\n fout.write(\"#PBS -N \" + self.jobName + \"\\n\")\n fout.write(\"#PBS -m e\\n\")\n fout.write(\"#PBS -W block=true\\n\\n\")\n fout.write(\"cd $PBS_O_WORKDIR\\n\")\n\n if self.runVolGrid == 1:\n #fout.write(\"/bin/rm -f \" + self.simMeshFile + \".sim\\n\")\n fout.write(\"/bin/rm -f starccmMeshRun.out\\n\")\n fout.write(\"chmod u+x \" + self.cshBatch1File + \".csh\\n\")\n # do not use >>& because it will fail in some environment\n fout.write(\"./\" + self.cshBatch1File + \".csh -powerOnDemand \" + self.javaBatch1File + \".java >& starccmMeshRun.out\\n\\n\")\n else:\n fout.write(\"echo 'User chooses not to make a mesh run.'\\n\")\n\n if self.runCFD == 1:\n fout.write(\"chmod u+x \" + self.cshBatch2File + \".csh\\n\")\n fout.write(\"/bin/rm -f *.csv *.png starccmFlowRun.out\\n\")\n # do not use >>& because it will fail in some environment\n fout.write(\"./\" + self.cshBatch2File + \".csh -powerOnDemand \" + self.javaBatch2File + \".java \" + self.simMeshFile + \" >& starccmFlowRun.out\\n\\n\")\n fout.write(\"# rename the strange file names\\n\")\n fout.write(\"/bin/mv \\$PWDForceX.csv ForceX.csv\\n\")\n fout.write(\"/bin/mv \\$PWDForceY.csv ForceY.csv\\n\")\n fout.write(\"/bin/mv \\$PWDForceZ.csv ForceZ.csv\\n\")\n fout.write(\"/bin/mv \\$PWDMomentX.csv MomentX.csv\\n\")\n fout.write(\"/bin/mv \\$PWDMomentY.csv MomentY.csv\\n\")\n fout.write(\"/bin/mv \\$PWDMomentZ.csv MomentZ.csv\\n\")\n fout.write(\"/bin/mv \\$PWDResiduals.csv Residuals.csv\\n\\n\")\n fout.write(\"/bin/mv \\$PWDForceX.png ForceX.png\\n\")\n fout.write(\"/bin/mv \\$PWDForceY.png ForceY.png\\n\")\n fout.write(\"/bin/mv \\$PWDForceZ.png ForceZ.png\\n\")\n fout.write(\"/bin/mv \\$PWDMomentX.png MomentX.png\\n\")\n fout.write(\"/bin/mv \\$PWDMomentY.png MomentY.png\\n\")\n fout.write(\"/bin/mv \\$PWDMomentZ.png MomentZ.png\\n\")\n fout.write(\"/bin/mv \\$PWDResiduals.png Residuals.png\\n\")\n fout.write(\"/bin/mv \\$PWDUpperCp.png UpperCp.png\\n\")\n fout.write(\"/bin/mv \\$PWDLowerCp.png LowerCp.png\\n\")\n fout.write(\"/bin/rm -rf null\\n\")\n else:\n fout.write(\"echo 'User chooses not to make a CFD run.'\\n\")\n\n fout.close()","def get_batch_data(cur_samples, is_training=False):\n\tbatch_src_max_len, batch_trg_max_len = get_max_len(cur_samples)\n\tbatch_trg_max_len += 1\n\tsrc_words_list = list()\n\tsrc_words_mask_list = list()\n\tsrc_char_seq = list()\n\tdecoder_input_list = list()\n\tsrc_pos_seq = list()\n\tsrc_dep_seq = list()\n\tsrc_loc_seq = list()\n\targ1sweights = []\n\targ1eweights = []\n\targ2sweights = []\n\targ2eweights = []\n\n\trel_seq = list()\n\targ1_start_seq = list()\n\targ1_end_seq = list()\n\targ2_start_seq = list()\n\targ2_end_seq = list()\n\ttarget_vec_seq = []\n\ttarget_vec_mask_seq = []\n\n\tfor sample in cur_samples:\n\t\tsrc_words_list.append(get_words_index_seq(sample.SrcWords, batch_src_max_len))\n\t\tsrc_words_mask_list.append(get_padded_mask(sample.SrcLen, batch_src_max_len))\n\t\tsrc_char_seq.append(get_char_seq(sample.SrcWords, batch_src_max_len))\n\t\tsrc_pos_seq.append(get_pos_index_seq(sample.SrcWords, batch_src_max_len))\n\t\tsrc_dep_seq.append(get_dep_index_seq(sample.SrcWords, batch_src_max_len))\n\t\tsrc_loc_seq.append([i+1 for i in range(len(sample.SrcWords))] +\n\t\t\t\t\t\t [0 for i in range(batch_src_max_len - len(sample.SrcWords))])\n\n\t\tif is_training:\n\t\t\targ1_start_seq.append(get_padded_pointers(sample.TrgPointers, 0, batch_trg_max_len))\n\t\t\targ1_end_seq.append(get_padded_pointers(sample.TrgPointers, 1, batch_trg_max_len))\n\t\t\targ2_start_seq.append(get_padded_pointers(sample.TrgPointers, 2, batch_trg_max_len))\n\t\t\targ2_end_seq.append(get_padded_pointers(sample.TrgPointers, 3, batch_trg_max_len))\n\t\t\targ1sweights.append(get_pointer_location(sample.TrgPointers, 0, batch_src_max_len, batch_trg_max_len))\n\t\t\targ1eweights.append(get_pointer_location(sample.TrgPointers, 1, batch_src_max_len, batch_trg_max_len))\n\t\t\targ2sweights.append(get_pointer_location(sample.TrgPointers, 2, batch_src_max_len, batch_trg_max_len))\n\t\t\targ2eweights.append(get_pointer_location(sample.TrgPointers, 3, batch_src_max_len, batch_trg_max_len))\n\t\t\trel_seq.append(get_relation_index_seq(sample.TrgRels, batch_trg_max_len))\n\t\t\tdecoder_input_list.append(get_relation_index_seq(sample.TrgRels, batch_trg_max_len))\n\t\t\ttarget_vec_seq.append(get_target_vec(sample.TrgPointers, sample.TrgRels, batch_src_max_len))\n\t\t\ttarget_vec_mask_seq.append([0 for i in range(len(sample.TrgRels))] +\n\t\t\t\t\t\t\t\t\t [1 for i in range(batch_trg_max_len + 1 - len(sample.TrgRels))])\n\t\telse:\n\t\t\tdecoder_input_list.append(get_relation_index_seq([], 1))\n\n\treturn {'src_words': np.array(src_words_list, dtype=np.float32),\n\t\t\t'src_words_mask': np.array(src_words_mask_list),\n\t\t\t'src_chars': np.array(src_char_seq),\n\t\t\t'src_pos_tags': np.array(src_pos_seq),\n\t\t\t'src_dep_tags': np.array(src_dep_seq),\n\t\t\t'src_loc': np.array(src_loc_seq),\n\t\t\t'decoder_input': np.array(decoder_input_list),\n\t\t\t'arg1sweights': np.array(arg1sweights),\n\t\t\t'arg1eweights': np.array(arg1eweights),\n\t\t\t'arg2sweights': np.array(arg2sweights),\n\t\t\t'arg2eweights': np.array(arg2eweights),\n\t\t\t'rel': np.array(rel_seq),\n\t\t\t'arg1_start': np.array(arg1_start_seq),\n\t\t\t'arg1_end': np.array(arg1_end_seq),\n\t\t\t'arg2_start': np.array(arg2_start_seq),\n\t\t\t'arg2_end': np.array(arg2_end_seq),\n\t\t\t'target_vec': np.array(target_vec_seq),\n\t\t\t'target_vec_mask': np.array(target_vec_mask_seq)}","def makecats(self, onlyn = None, sbe_sample_scale=0.05):\n\n\t\tfilenames = io.get_filenames(self.sbedatadir)\n\t\tif onlyn != None:\n\t\t\tfilenames = filenames[:onlyn]\n\t\t\n\t\t\n\t\t# Hardcoded for now:\n\t\tstampsize = 200\n\t\tn = 32\n\t\t\n\t\tlogger.info(\"Will make %i cats...\" % len(filenames))\n\t\t\n\t\tfor filename in filenames:\n\t\t\t\n\t\t\tdatafilepath = io.datafile(filename)\n\t\t\timagefilepath = io.imagefile(filename)\n\t\t\tworkname = io.workname(filename)\n\t\t\tcatfilepath = os.path.join(self.workobsdir, workname + \"-inputcat.pkl\")\n\t\t\timageworkdirfilepath = os.path.join(self.workobsdir, workname + \"-imageworkdir\")\n\t\t\t\n\t\t\tif os.path.exists(catfilepath):\n\t\t\t\tlogger.info(\"Skipping '%s', catalog already exists\" % (workname))\n\t\t\t\tcontinue\n\t\t\t\n\t\t\t# We read the data file and turn it into an astropy table\n\t\t\tcat = astropy.io.ascii.read(datafilepath)\n\t\t\t\n\t\t\t# Let's keep the file identification also in the meta dict:\n\t\t\tcat.meta[\"sbefilename\"] = filename # very weird SBE thing, without extension...\n\t\t\tcat.meta[\"workname\"] = workname\n\t\t\t\n\t\t\t# Let's convert the true shape parameters into more convenient forms:\n\t\t\t\n\t\t\tcat[\"PSF_e1\"] = cat[\"PSF_shape_1\"] * np.cos(2.0*cat[\"PSF_shape_2\"]*np.pi/180)\n\t\t\tcat[\"PSF_e2\"] = cat[\"PSF_shape_1\"] * np.sin(2.0*cat[\"PSF_shape_2\"]*np.pi/180)\n\t\t\tcat[\"Galaxy_e1\"] = cat[\"Galaxy_shape_1\"] * np.cos(2.0*cat[\"Galaxy_shape_2\"]*np.pi/180)\n\t\t\tcat[\"Galaxy_e2\"] = cat[\"Galaxy_shape_1\"] * np.sin(2.0*cat[\"Galaxy_shape_2\"]*np.pi/180)\n\t\t\tcat[\"Galaxy_g1\"] = cat[\"Galaxy_shear_1\"] * np.cos(2.0*cat[\"Galaxy_shear_2\"]*np.pi/180)\n \t\t\tcat[\"Galaxy_g2\"] = cat[\"Galaxy_shear_1\"] * np.sin(2.0*cat[\"Galaxy_shear_2\"]*np.pi/180)\n\n\t\t\t# And for convenience, include some standard MegaLUT names for the PSFs\n\t\t\tcat[\"tru_psf_g1\"] = cat[\"PSF_e1\"]\n\t\t\tcat[\"tru_psf_g2\"] = cat[\"PSF_e2\"]\n\t\t\tcat[\"tru_psf_sigma\"] = cat[\"PSF_sigma_arcsec\"] / sbe_sample_scale\n \n\t\t\t# We add the xid, yid, x and y columns, following an explanation by Bryan\n\t\t\t# on how the data/fits files should be interpreted (\"like english text\").\n\t\t\t#cat[\"xid\"] = np.concatenate([np.arange(n) for i in range(n)])\n\t\t\t#cat[\"yid\"] = np.concatenate([np.ones(n, dtype=np.int)*(n - i -1) for i in range(n)])\n\t\t\t# Well, not exactly. One should start y at the bottom, it seems:\n\t\t\tcat[\"xid\"] = np.concatenate([np.arange(n) for i in range(n)])\n\t\t\tcat[\"yid\"] = np.concatenate([np.ones(n, dtype=np.int)*i for i in range(n)])\t\t\n\t\t\tcat[\"x\"] = stampsize/2.0 + cat[\"xid\"]*(stampsize + 1) + 0.5\n\t\t\tcat[\"y\"] = stampsize/2.0 + cat[\"yid\"]*(stampsize + 1) + 0.5\n\t\t\t\t\t\n\t\t\t# We create the ImageInfo object\n\t\t\timg = megalut.tools.imageinfo.ImageInfo(\n\t\t\t\tfilepath = imagefilepath,\n\t\t\t\txname = \"x\", yname = \"y\",\n\t\t\t\tstampsize = stampsize,\n\t\t\t\tworkdir = imageworkdirfilepath\n\t\t\t\t)\n\t\t\tcat.meta[\"img\"] = img\n\t\t\t\n\t\t\t# And save the catalog\n\t\t\tmegalut.tools.io.writepickle(cat, catfilepath)","def build_command(self, args):\n self._validate_common(args)\n self._set_manifests(args)\n\n manifest = self._manager._recipes\n\n records = self._get_matching_records(args, manifest)\n\n if not len(records):\n sys.exit(\"No matching items found.\")\n\n for record in records:\n result = self._manager.build(record['_type'], **record)\n print('The requested asset has been built: {}'.format(result['_path']))\n\n if len(records) > 1:\n print('All files have been successfully built. Thank you.')","def go(self):\n num_fofs = self.fofs['fofid'].max()\n fof_splits = split.get_splits(num_fofs, self['chunksize'])\n\n for isplit,fof_split in enumerate(fof_splits):\n logger.info('%s %s' % (isplit,fof_split))\n self._write_split(isplit, fof_split)","def main(filein, min_reads=150, n_group=2000):\n from random import sample\n assert os.path.exists(filein)\n hq = filter_reads(filein)\n logging.info('remove matching reads')\n no_pol = remove_matching_reads(hq, cont_file)\n # no_pol = 'clean_reads.fasta'\n no_pol_reads = list(SeqIO.parse(no_pol, 'fasta'))\n no_pol_reads = sample(no_pol_reads, k=len(no_pol_reads))\n covering_reads = set([])\n logging.info('blast reads in batches until enough are found')\n total_blasted = 0\n for i, group in enumerate(grouper(n_group, no_pol_reads)):\n if i > 2 and len(covering_reads) < 20:\n sys.exit('not enough reads covering V3 were found')\n logging.info('blast call %d', i + 1)\n _ = blast_reads(group)\n covering_reads.update(_)\n total_blasted += n_group\n logging.info('this blast: %d covering out of %d total - %3.2f %%', len(_), n_group,\n 100 * float(len(_)) / n_group)\n logging.info('cumulative: %d covering out of %d total - %3.2f %%', len(covering_reads), total_blasted,\n 100 * float(len(covering_reads)) / total_blasted)\n if len(covering_reads) >= min_reads:\n break\n\n logging.info('covering_reads used in MSA: %d out of %d blasted (%3.2f %%)', len(covering_reads), total_blasted,\n 100 * float(len(covering_reads)) / total_blasted)\n cov_reads, n_fwd, n_rev = extract_reads(covering_reads, no_pol)\n\n SeqIO.write(cov_reads, 'v3reads.fasta', 'fasta')\n logging.info('%d covering reads in forward orientation', n_fwd)\n logging.info('%d covering reads in reverse orientation', n_rev)\n if n_fwd + n_rev < min_reads:\n logging.error('Not enough reads: %d', n_fwd + n_rev)\n sys.exit('Not enough reads: %d' % (n_fwd + n_rev))\n\n no_singleton_reads = [s for s in SeqIO.parse('v3reads.fasta', 'fasta') if int(s.id.split('_')[-1]) > 1]\n SeqIO.write(no_singleton_reads, 'v3reads_no_singleton.fasta', 'fasta')\n\n cml = shlex.split('muscle -in v3reads_no_singleton.fasta -out msa.fasta -quiet')\n subprocess.call(cml)\n\n df, haplotypes, support = msa_2_df('msa.fasta')\n logging.info('Haplotypes supported by %d reads out of %d: %3.1f%%',\n support, n_fwd + n_rev, 100.0 * support / (n_fwd + n_rev))\n cons_seq = df_2_ambiguous_sequence(df)\n SeqIO.write([SeqRecord(Seq(cons_seq), id='v3_consensus', description='')], 'v3cons.fasta', 'fasta')\n\n haps = []\n hi = 1 # counter for haplotypes, used in fasta file\n accounted_f = 0.0 # keep track of the cumulative accounted frequency\n tot_reads = sum(haplotypes.values())\n for h, support in haplotypes.most_common():\n f = round(float(support) / tot_reads, 2)\n accounted_f += f\n sr = SeqRecord(Seq(h), id='v3_haplotype_%d-support_%3.2f' % (hi, f), description='')\n haps.append(sr)\n hi += 1\n\n SeqIO.write(haps, 'v3haplotypes.fasta', 'fasta')\n for f in ['high_quality.fastq', 'clean_reads.fasta']:\n os.remove(f)\n logging.info('Haplotypes written to haplotypes.fasta')","def main(raw_args=None):\r\n args = get_args(raw_args)\r\n dataset = DATASETS[args.dataset]\r\n dataset.set_work_dir(args.path)\r\n\r\n dataset_dir = os.path.join(dataset.get_data_path(), consts.RAW_DATA_DIR)\r\n input_file = os.path.join(dataset_dir, args.input)\r\n if args.output:\r\n out_name = args.output\r\n else:\r\n out_name = OUT_FILE % (args.chr, format_number(args.size))\r\n output_file = os.path.join(dataset_dir, out_name)\r\n\r\n print(\"Output will be in: %s\" % output_file)\r\n\r\n entries = set()\r\n out = open(output_file, 'w')\r\n input = open(input_file, 'r')\r\n\r\n is_first = True\r\n for line in input:\r\n if is_first:\r\n add_headers(line.strip(), out)\r\n is_first = False\r\n else:\r\n entries.add(line)\r\n input.close()\r\n print(\"Done reading\")\r\n\r\n # Keeps track of spacers that were already included.\r\n spacers = set()\r\n # Keeps track of the number of samples already obtained.\r\n sampled = 0\r\n # Keeps track of progress for printing.\r\n chunk = 1\r\n\r\n # While we have not sampled enough...\r\n while sampled < args.size:\r\n remaining = args.size - sampled\r\n # Sample more entries.\r\n new_entries = sample(entries, spacers, remaining, dataset)\r\n sampled += len(new_entries)\r\n for entry in new_entries:\r\n out.write(common.to_csv_line(*entry))\r\n\r\n # If we have passed a progress checkpoint - prints progress.\r\n if (args.size / PRINT_NUM) * chunk < sampled:\r\n print(\"Sampled: %d\" % sampled)\r\n chunk += 1\r\n\r\n print(\"Done\")\r\n print(STATS)","def process_datasets(self):\n\n with open(self.mappings, \"r+\") as json_file:\n emsl_to_jgi = json.load(json_file)\n emsl_to_jgi_copy = copy.deepcopy(emsl_to_jgi)\n\n contaminant_file_loc = emsl_to_jgi[\"contaminant_file_loc\"]\n # run for each dataset\n for dataset_id, values in emsl_to_jgi.items():\n if dataset_id not in [\n \"contaminant_file_loc\",\n \"analysis_activity_file_loc\",\n \"data_objects_file_loc\",\n \"STUDY\",\n \"tools_used\",\n ]:\n raw_file_loc = values[\"raw_file_loc\"]\n self.dataset_name = values[\"dataset_name\"]\n # dataset search against a fasta file\n for genome_directory, locations in values[\n \"genome_directory\"\n ].items():\n # clear object to prepare next job\n ANALYSIS_JOBS_OBJECT.clear()\n\n # create log_dir\n self.save_job_results = os.path.join(\n self.result_loc, dataset_id, genome_directory\n )\n self.log_collected_at = os.path.join(\n os.path.abspath(self.save_job_results), \"analysis_jobs_logs\"\n )\n if not os.path.exists(self.log_collected_at):\n os.makedirs(self.log_collected_at)\n\n files = [locations[\"faa_file_loc\"], contaminant_file_loc]\n contaminated_faa_file_loc = self.contaminate_fasta(files)\n\n self.register_job_in_emsl_to_jgi(\n dataset_id,\n genome_directory,\n \"contaminated_faa_file_loc\",\n contaminated_faa_file_loc,\n emsl_to_jgi_copy,\n )\n # convert .faa to .txt\n faa_txt_file = self.convert_faa2txt(\n dataset_id, contaminated_faa_file_loc\n )\n self.register_job_in_emsl_to_jgi(\n dataset_id,\n genome_directory,\n \"txt_faa_file_loc\",\n faa_txt_file,\n emsl_to_jgi_copy,\n )\n\n # log & run job\n self.run_n_log_job(\n dataset_id,\n genome_directory,\n contaminated_faa_file_loc,\n raw_file_loc,\n emsl_to_jgi_copy,\n )\n\n # merge analysis\n resultant_file = self.merge_analysis_jobs(\n dataset_id, genome_directory\n )\n self.register_job_in_emsl_to_jgi(\n dataset_id,\n genome_directory,\n \"resultant_file_loc\",\n resultant_file,\n emsl_to_jgi_copy,\n )\n\n # capture the job metadata object\n logger.info(\"Jobrun\", extra=LOGGED_ANALYSIS_JOB)\n\n # update emsl_to_jgi.json\n json_file.seek(0) # move back to BOF.\n json_file.truncate()\n json_file.write(json.dumps(emsl_to_jgi_copy, default=str, indent=4))\n pass","def FSC2(input_dir, num_reps=50, min_sims=100000, max_ecm=20, calc_CI=False, numcores=1, scratch_mb='200', time_scratch=\"01:50:00\", mem=\"200\", print1=False, overwrite=\"None\", fsc2_path=\"/storage/plzen1/home/holcovam/programs/fsc26_linux64/fsc26\"):\n Data_Files = []\n tpl_files = []\n est_files = []\n CI_Data_Files = []\n shlist = []\n\n if input_dir.endswith(\"/\") is False:\n input_dir += \"/\"\n\n for path in os.listdir(input_dir):\n if os.path.isdir(input_dir + path) and path.startswith(\"FSC2input\"):\n samp_name = path.split(\"_\")[1]\n #folder_name = samp_name\n if samp_name + \"_DSFS.obs\" in os.listdir(input_dir + path):\n for i in range(0, num_reps):\n new_file = open(input_dir + path + \"/\" + samp_name + str(i) + \"_DSFS.obs\", 'w')\n with open(input_dir + path + \"/\" + samp_name + \"_DSFS.obs\") as data_file:\n for line in data_file:\n new_file.write(line)\n new_file.close()\n Data_Files.append(input_dir + path + \"/\" + samp_name + str(i) + \"_DSFS.obs\")\n else:\n print(\"Did not find input data file for: \", samp_name)\n if calc_CI == \"True\":\n num_files = 0\n for file in os.listdir(input_dir + path):\n if file.endswith(\"_DSFS.obs\") and file.split(\"_\")[-2].split(\".\")[-1][0:3] == \"rep\" and file != samp_name + \"_DSFS.obs\":\n for i in range(0, num_reps):\n new_file = open(input_dir + path + \"/\" + samp_name + file.split(\"_\")[-2].split(\".\")[-1].split(\"_\")[0]+ \"_\" + str(i) + \"_DSFS.obs\", 'w')\n with open(input_dir + path + \"/\" + file) as data_file:\n for line in data_file:\n new_file.write(line)\n new_file.close()\n CI_Data_Files.append(input_dir + path + \"/\" + samp_name + file.split(\"_\")[-2].split(\".\")[-1].split(\"_\")[0]+ \"_\" + str(i) + \"_DSFS.obs\")\n num_files += 1\n if len(CI_Data_Files) < 1:\n print(\"Did not find bootstrap replicates for: \", samp_name)\n else:\n print(\"Found \", num_files, \" replicate dsfs files for CI calculation for \", samp_name)\n if path.endswith(\".tpl\"):\n tpl_files.append(path)\n est_files.append(path.split(\".\")[0])\n if len(tpl_files) == 0:\n print(\"Did not find any tpl files!! Aborting!!\")\n else:\n if calc_CI == \"True\":\n Data_Files = CI_Data_Files\n for file in Data_Files:\n name = file.split(\"_DSFS\")[0]\n samp_name = name.split(\"/\")[-1]\n folder_name = samp_name [0:11]\n for tpl in tpl_files:\n tpl_name = tpl.split(\".tpl\")[0]\n if os.path.isdir(name + \"_\" + tpl_name) is False or overwrite == \"hard\":\n new_tpl = open(name + \"_\" + tpl_name + \".tpl\", 'w')\n new_data = open(name + \"_\" + tpl_name + \"_DSFS.obs\", 'w')\n\n with open(file, 'r') as data:\n for i, line in enumerate(data):\n if i == 1:\n pop_info = line.strip(\"\\n\").strip(\"\\t\").split(\"\\t\")\n pop_num = int(pop_info[0])\n samp_nums = pop_info[-pop_num:]\n new_data.write(line)\n with open(input_dir + tpl, 'r') as template:\n samp_num_lines = pop_num + 4\n for i, line in enumerate(template):\n if i < samp_num_lines:\n new_tpl.write(line)\n elif i == samp_num_lines:\n for num in samp_nums:\n new_tpl.write(num + \"\\n\")\n elif i >= samp_num_lines + len(samp_nums):\n new_tpl.write(line)\n new_est = open(name + \"_\" + tpl_name + \".est\", 'w')\n try:\n with open(input_dir + tpl_name + \".est\") as est:\n for line in est:\n new_est.write(line)\n except FileNotFoundError:\n print(\"Did not find est file for: \", tpl)\n #folder_name = samp_name ''.join(i for i in s if not i.isdigit())\n shname = name + \"_\" + tpl_name + \".sh\"\n shfile5 = open(shname, 'w')\n shfile5.write('#!/bin/bash -e\\n' +\n '#PBS -N '+samp_name+'\\n' +\n '#PBS -l walltime='+str(time_scratch)+'\\n' +\n '#PBS -l select=1:ncpus='+str(numcores)+':mem='+str(mem)+'mb:scratch_local='+str(scratch_mb)+'mb\\n' +\n '#PBS -m abe\\n' +\n '#PBS -j oe\\n\\n' +\n 'module add python-3.4.1-gcc\\n'+\n 'module add python34-modules-gcc\\n'+\n 'trap \\'clean_scratch\\' TERM EXIT\\n'+\n 'if [ ! -d \"$SCRATCHDIR\" ] ; then echo \"Scratch not created!\" 1>&2; exit 1; fi \\n' +\n 'DATADIR=\"/storage/plzen1/home/holcovam/ScanTools\"\\n' +\n 'cp $DATADIR/'+ input_dir + \"FSC2input_\" + folder_name+ \"/\" + samp_name + \"_\" + tpl_name + '* $SCRATCHDIR || exit 1\\n'+\n 'cp '+fsc2_path+' $SCRATCHDIR || exit 1\\n'+\n 'cd $SCRATCHDIR || exit 2\\n' +\n 'echo data loaded at `date`\\n\\n' +\n 'chmod +x fsc26 \\n' +\n #'ls -l \\n' +\n './fsc26 -t ' + samp_name + \"_\" + tpl_name + '.tpl -e ' + samp_name + \"_\" + tpl_name + '.est -n ' + str(min_sims) + ' -u -d -q -L ' + str(max_ecm) + ' -M \\n' + \n 'rm seed.txt \\n'+\n 'rm fsc26\\n'+\n 'rm *DSFS.obs\\n'+\n 'rm *.sh\\n'+\n 'rm *.tpl \\n'+\n 'rm *.est \\n'+\n #'ls -l \\n' +\n 'cp $SCRATCHDIR/*.par $DATADIR/'+ input_dir + \"FSC2input_\" + folder_name+' || exit 1\\n'+\n 'rm *.par \\n'+\n 'cp -r $SCRATCHDIR/* $DATADIR/'+input_dir+' || export CLEAN_SCRATCH=false\\n'+\n 'printf \"\\\\nFinished\\\\n\\\\n\"\\n')\n shfile5.close()\n shlist.append(shname)\n\n############IF PROBLEM WITH EXCESS OF NONCONVERGED CHAINS, COPY /home/majda/alpine/fastsimcoal2/afterWPSG/scripts/notConverged.py here ###################\n\n else:\n print(\"Output for \" + samp_name + \"_\" + tpl_name + \" already exists. Use hard_overwrite = True to overwrite.\")\n return shlist","def addSplitNamVCFJobs(self, workflow, inputData=None, db_vervet=None, transferOutput=True,\\\n\t\t\t\t\t\tmaxContigID=None, outputDirPrefix=\"\"):\n\t\tsys.stderr.write(\"Adding split Nam VCF-file jobs for %s vcf files ... \"%(len(inputData.jobDataLs)))\n\t\tno_of_jobs= 0\n\t\t\n\t\t\n\t\ttopOutputDir = \"%sSampleInUCLAID\"%(outputDirPrefix)\n\t\ttopOutputDirJob = self.addMkDirJob(outputDir=topOutputDir)\n\t\tno_of_jobs += 1\n\t\t\n\t\treturnData = PassingData()\n\t\treturnData.jobDataLs = []\n\t\tfor jobData in inputData.jobDataLs:\n\t\t\tinputF = jobData.vcfFile\n\t\t\tif maxContigID:\n\t\t\t\tcontig_id = self.getContigIDFromFname(inputF.name)\n\t\t\t\ttry:\n\t\t\t\t\tcontig_id = int(contig_id)\n\t\t\t\t\tif contig_id>maxContigID:\t#skip the small contigs\n\t\t\t\t\t\tcontinue\n\t\t\t\texcept:\n\t\t\t\t\tsys.stderr.write('Except type: %s\\n'%repr(sys.exc_info()))\n\t\t\t\t\timport traceback\n\t\t\t\t\ttraceback.print_exc()\n\t\t\tinputFBaseName = os.path.basename(inputF.name)\n\t\t\tcommonPrefix = inputFBaseName.split('.')[0]\n\t\t\toutputVCF = File(os.path.join(topOutputDir, '%s.vcf'%(commonPrefix)))\n\t\t\tabstractMapperJob = self.addAbstractMapperLikeJob(workflow, executable=workflow.SplitNamVCFIntoMultipleSingleChrVCF, \\\n\t\t\t\t\tinputVCF=inputF, outputF=outputVCF, \\\n\t\t\t\t\tparentJobLs=[topOutputDirJob]+jobData.jobLs, transferOutput=False, job_max_memory=200,\\\n\t\t\t\t\textraArguments=None, extraDependentInputLs=[])\n\t\t\t\n\t\t\tVCFGzipOutputF = File(\"%s.gz\"%outputVCF.name)\n\t\t\tVCFGzipOutput_tbi_F = File(\"%s.gz.tbi\"%outputVCF.name)\n\t\t\tbgzip_tabix_VCF_job = self.addBGZIP_tabix_Job(workflow, bgzip_tabix=workflow.bgzip_tabix, \\\n\t\t\t\t\tparentJobLs=[abstractMapperJob], inputF=abstractMapperJob.output, outputF=VCFGzipOutputF, \\\n\t\t\t\t\ttransferOutput=transferOutput)\n\t\t\t\n\t\t\treturnData.jobDataLs.append(PassingData(jobLs=[bgzip_tabix_VCF_job], vcfFile=VCFGzipOutputF, \\\n\t\t\t\t\t\t\t\t\ttbi_F=VCFGzipOutput_tbi_F, \\\n\t\t\t\t\t\t\t\t\tfileLs=[VCFGzipOutputF, VCFGzipOutput_tbi_F]))\n\t\t\t\n\t\t\tno_of_jobs += 2\n\t\tsys.stderr.write(\"%s jobs.\\n\"%(self.no_of_jobs))\n\t\treturn returnData","def writeFileBloatReport(f, baselineName, buildName):\n logging.info('Running bloaty diff between %s and %s', baselineName, buildName)\n f.write('Comparing %s and %s:\\n\\n' % (baselineName, buildName))\n\n result = subprocess.run(\n ['bloaty', '--csv', buildName, '--', baselineName],\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n )\n\n if result.returncode != 0:\n logging.warning('Bloaty execution failed: %d', result.returncode)\n f.write('BLOAT EXECUTION FAILED WITH CODE %d:\\n' % result.returncode)\n\n content = result.stdout.decode('utf8')\n\n f.write(content)\n f.write('\\n')\n\n result = ComparisonResult(os.path.basename(buildName))\n try:\n reader = csv.reader(io.StringIO(content))\n\n for row in reader:\n section, vm, f = row\n if (section in SECTIONS_TO_WATCH) or (vm not in ['0', 'vmsize']):\n result.sectionChanges.append(SectionChange(section, int(f), int(vm)))\n except:\n pass\n\n return result","def form_cand_queries_batch(self, batch_input, gram, additional_neg_batch=0):\n N = batch_input['pair_objects'].size(0)\n\n # For each gram s,r,o get unique list of positive labels in the batch\n if gram in ['s','r','o']:\n\n labels = batch_input['labels_' + gram]\n\n cat_batch = []\n idx = []\n for j in range(N):\n\n cats = (labels[j,:]==1).nonzero().data[:,0].tolist()\n\n count = 0\n cat = cats[0]\n while count < len(cats) and cats[count]>-1:\n cat = cats[count]\n if cat not in cat_batch:\n idx.append(tuple([j,len(cat_batch)]))\n cat_batch.append(cat)\n else:\n idx.append(tuple([j,cat_batch.index(cat)]))\n count += 1\n\n # Add negatives at random (later can refine and add hard negatives)\n if additional_neg_batch>0:\n neg_cat_sampled = np.random.randint(0, len(self.vocab[gram]), size=additional_neg_batch) # can be duplicate, it is ok\n\n # Append the ones that are not positive for any example in the batch\n for neg_cat in neg_cat_sampled:\n if neg_cat not in cat_batch:\n cat_batch.append(neg_cat) \n \n\n labels_query = np.zeros((N,len(cat_batch)))\n for j in range(len(idx)):\n labels_query[idx[j][0], idx[j][1]] = 1\n\n cat_batch = Variable(torch.from_numpy(np.array(cat_batch).astype(int)))\n if self.use_gpu:\n cat_batch = cat_batch.cuda()\n\n query = self.idx_to_vocab[gram].index_select(0, cat_batch)\n\n labels_query = Variable(torch.from_numpy(labels_query)).type(query.data.type())\n query = query.unsqueeze(0) # (M,1) -> (1,M,1)\n\n\n return query, labels_query\n\n\n # The triplets for sro are all the positives\n if gram=='sro':\n\n triplet_cat_batch = np.empty((0,3), dtype=int)\n idx_triplet = []\n for j in range(N):\n\n sub_cats = (batch_input['labels_s'][j,:]==1).nonzero().data[:,0].tolist()\n obj_cats = (batch_input['labels_o'][j,:]==1).nonzero().data[:,0].tolist()\n rel_cats = (batch_input['labels_r'][j,:]==1).nonzero().data[:,0].tolist()\n\n\n # Do not add the triplets containing __background__ -> not in vocab\n for sub_cat in sub_cats:\n for obj_cat in obj_cats:\n\n if sub_cat==0 or obj_cat==0:\n continue\n\n count = 0\n while count < len(rel_cats) and rel_cats[count]>-1:\n rel_cat = rel_cats[count]\n triplet_cat = np.array([sub_cat, rel_cat, obj_cat])\n idx_triplet_cat_batch = np.where(np.logical_and(triplet_cat_batch[:,0]==triplet_cat[0], \\\n np.logical_and(\n triplet_cat_batch[:,1]==triplet_cat[1], \\\n triplet_cat_batch[:,2]==triplet_cat[2])))[0]\n if len(idx_triplet_cat_batch)==0:\n idx_triplet.append(tuple([j,triplet_cat_batch.shape[0]]))\n triplet_cat_batch = np.vstack((triplet_cat_batch, triplet_cat))\n else:\n idx_triplet.append(tuple([j,idx_triplet_cat_batch[0]]))\n\n count += 1\n\n\n # Add negatives at random\n if additional_neg_batch>0:\n\n neg_cat_sampled_sub = np.random.randint(0, len(self.vocab['s']), size=additional_neg_batch)\n neg_cat_sampled_obj = np.random.randint(0, len(self.vocab['o']), size=additional_neg_batch)\n neg_cat_sampled_rel = np.random.randint(0, len(self.vocab['r']), size=additional_neg_batch)\n neg_cat_sampled = np.vstack((neg_cat_sampled_sub, neg_cat_sampled_rel, neg_cat_sampled_obj)).T\n\n\n # Append the ones that are not positive for any example in the batch\n for j in range(len(neg_cat_sampled)):\n\n idx_batch = np.where(np.logical_and(triplet_cat_batch[:,0]==neg_cat_sampled[j,0], \\\n np.logical_and(\n triplet_cat_batch[:,1]==neg_cat_sampled[j,1], \\\n triplet_cat_batch[:,2]==neg_cat_sampled[j,2])))[0]\n\n if len(idx_batch)==0:\n triplet_cat_batch = np.vstack((triplet_cat_batch, neg_cat_sampled[j,:]))\n\n\n labels_query_sro = np.zeros((N,triplet_cat_batch.shape[0]))\n for j in range(len(idx_triplet)):\n labels_query_sro[idx_triplet[j][0], idx_triplet[j][1]] = 1\n\n triplet_cat_batch = Variable(torch.from_numpy(triplet_cat_batch))\n if self.use_gpu:\n triplet_cat_batch = triplet_cat_batch.cuda()\n query_sro = torch.cat([ self.idx_to_vocab['s'].index_select(0,triplet_cat_batch[:,0]),\\\n self.idx_to_vocab['r'].index_select(0,triplet_cat_batch[:,1]),\\\n self.idx_to_vocab['o'].index_select(0,triplet_cat_batch[:,2])], 1)\n\n labels_query_sro = Variable(torch.from_numpy(labels_query_sro)).type(query_sro.data.type()) \n query_sro = query_sro.unsqueeze(0) # (M,3) -> (1,M,3)\n\n\n return query_sro, labels_query_sro","def main():\n file_one_path, file_two_path, output_path =\\\n get_command_line_arguments(\n ['/home/ehler002/project/groups/go/Data/Cluster_Data/Dataset.txt',\n '/home/ehler002/project/groups/go/Data/Cluster_Data/translated_genes.fpkm_table',\n '/home/ehler002/project/groups/go/Data/Cluster_Data/Full_fpkm_Table.txt'])\n pattern = 'CRO_T'\n for file_path in [file_one_path, file_two_path]:\n assert os.path.exists(file_path), 'File %s does not exist.' % file_path\n start_time = datetime.datetime.now()\n print('Started concatenation at %s' % start_time)\n file_contents, headers = get_file_contents(file_two_path)\n file_contents = sort_file_contents(file_contents)\n file_contents = remove_pattern(file_contents, pattern)\n concatenate_files(file_one_path, file_contents, headers, output_path)\n print('Finished concatenation in %s' % (datetime.datetime.now() - start_time))","def generate_patches(self):\n sets = os.listdir(self.__data_dir)\n import re\n sets = [a_set for a_set in sets if re.match(r'[0-9a-zA-Z_]+', a_set)]\n total = len(sets)\n count = 1\n for a_set in sets:\n print('\\r', 'slicing {0}/{1}'.format(count, total).ljust(20), end='')\n self.__generate_slices_a_set(a_set)\n count += 1","def produce_train_all(root_dir):\n train = 'trainImnamesSe.csv'\n all_bboxes = 'bboxesDF.csv'\n train_lab = 'trainLabeledDF.csv'\n\n if train in os.listdir(root_dir) and all_bboxes in os.listdir(root_dir) \\\n and train_lab in os.listdir(root_dir):\n train_imnames = pd.read_csv(osp.join(root_dir, train),\n header=None, squeeze=True)\n all_bboxes = pd.read_csv(osp.join(root_dir, all_bboxes))\n train_lab = pd.read_csv(osp.join(root_dir, train_lab))\n else:\n train_imnames, _ = process_pool_mat(root_dir)\n all_bboxes, _ = process_images_mat(root_dir)\n train_lab = process_train_mat(root_dir)\n\n # pick out items whose `imname` appear in `train_imnames`\n # to form training set. NOTE there is another way to finish it\n # ```\n # train_all = all_bboxes[all_bboxes['imname'].isin(train_imnames)]\n # train_all.index = range(train_all.shape[0])\n # ```\n train_all = pd.merge(all_bboxes, train_imnames.to_frame(name='imname'))\n train_all = pd.merge(train_all, train_lab, how='outer') # set pids\n train_all = train_all.fillna(-1) # set -1 to unlabeled samples\n train_all['pid'] = train_all['pid'].values.astype(np.int32)\n\n train_all.to_csv(osp.join(root_dir, 'trainAllDF.csv'), index=False)\n\n return train_all","def format_preparation_files(run_dir, sample_sheet, output_dir, pipeline,\n verbose):\n sample_sheet = KLSampleSheet(sample_sheet)\n df_sheet = sample_sheet_to_dataframe(sample_sheet)\n\n if pipeline == 'atropos-and-bowtie2':\n click.echo('Stats collection is not supported for pipeline '\n 'atropos-and-bowtie2')\n else:\n stats = run_counts(run_dir, sample_sheet)\n\n stats['sample_name'] = \\\n df_sheet.set_index('lane', append=True)['sample_name']\n\n # returns a map of (run, project_name, lane) -> preparation frame\n preps = preparations_for_run(run_dir, df_sheet, pipeline=pipeline)\n\n os.makedirs(output_dir, exist_ok=True)\n\n for (run, project, lane), df in preps.items():\n fp = os.path.join(output_dir, f'{run}.{project}.{lane}.tsv')\n\n if pipeline == 'fastp-and-minimap2':\n # stats are indexed by sample name and lane, lane is the first\n # level index. When merging, make sure to select the lane subset\n # that we care about, otherwise we'll end up with repeated rows\n df = df.merge(stats.xs(lane, level=1), how='left',\n on='sample_name')\n\n # strip qiita_id from project names in sample_project column\n df['sample_project'] = df['sample_project'].map(\n lambda x: re.sub(r'_\\d+$', r'', x))\n\n # center_project_name is a legacy column that should mirror\n # the values for sample_project.\n df['center_project_name'] = df['sample_project']\n\n df.to_csv(fp, sep='\\t', index=False)\n\n if verbose:\n project_name = remove_qiita_id(project)\n # assume qiita_id is extractable and is an integer, given that\n # we have already passed error-checking.\n qiita_id = project.replace(project_name + '_', '')\n print(\"%s\\t%s\" % (qiita_id, abspath(fp)))","def prepare_runs(args):\n output_directory = _prepare_output_dir(args.output_directory)\n z_score_dir = args.z_score_dir\n region_list = args.region_list \n if args.region_list is None:\n try:\n flanking_region = int(args.flanking_region)\n except ValueError:\n logging.error('Flanking region argument needs to be an integer')\n sys.exit(COMMAND_LINE_ERROR)\n build = args.build\n bed_directory = args.bed_directory\n # Create the SNPList\n try:\n min_maf = float(args.maf)\n except:\n logging.error(\"Min Maf -m or --min-maf needs to be an floating point number\")\n sys.exit(COMMAND_LINE_ERROR)\n if args.region_list is not None:\n region_list = {}\n snp_list = []\n with open(args.region_list) as input_file:\n # When using no flaking region SNP must be valid, but it doesn't actually matter what it is, need to ensure that is actually the case.\n for i, line in enumerate(input_file):\n rsid = str(i)+ \"_\" + ''.join(line.strip().split(\"\\t\"))\n chromosome = line.strip().split(\":\")[0] \n snp = Snp(chromosome,\"1\",rsid)\n snp_list.append(snp)\n region_list[snp.rsid] = line.strip()\n else:\n snp_list = SnpList(args.snp_list, build)\n logging.info(snp_list)\n # Locus to process\n # population_to_extract_vcf\n if not args.annotation_only:\n no_flanking = args.flanking_units\n if no_flanking:\n raise NotImplementedError(\"Using a number of flanking SNPs instead of a region is not supported\")\n populations= args.populations.split(',')\n logging.info(\"Populations to process: {0}\".format(populations))\n loci = []\n gemini_databases = []\n output_vcfs = []\n for snp in snp_list:\n logging.info('Preparing output files for SNP {0}'.format(snp.rsid))\n locus = snp.rsid\n loci.append(locus)\n logging.info(\"Obtaining VCF file from the 1000 genomes project\")\n if region_list is not None:\n vcf = get_vcf_file(snp, string_region=region_list[locus])\n else: \n vcf = get_vcf_file(snp, flanking_region=flanking_region)\n for population in populations:\n tmp_vcf = extract_population_from_1000_genomes(vcf=vcf, super_population=population)\n z_score_file = get_relevant_zscore(snp.chrom, population, z_score_dir)\n pos_list_zscore = create_pos_hash_table(z_score_file)\n output_vcf = generate_zscore_and_vcf_output(output_directory=output_directory, zscore_hash=pos_list_zscore, vcf=tmp_vcf, locus=locus,population=population, multiply_rsquare=args.multiply_rsquare)\n if bed_directory is None:\n logging.info(\"Creating gemini database\")\n # TODO: Fix broxen gemini referenec\n gemini_databases.append(create_gemini_database(vcf=output_vcf))\n vcf_to_plink(locus, output_directory=output_directory, vcf=output_vcf, population=population)\n plink_to_ld_matrix(locus, output_directory=output_directory, population=population)\n logging.info(\"Generate transancestrals matrices\")\n generate_transancestral_output(loci, populations, output_directory)\n if bed_directory is None:\n logging.info(\"Generating annotation matrices to be used with Paintor\")\n logging.info(gemini_databases)\n generate_and_write_encode_annotations(databases=gemini_databases, output_directory=output_directory, loci=snp_list)\n else:\n logging.info(\"Annotation using bed files\")\n generate_bed_file_annotations(loci=loci, bed_directory=bed_directory, output_directory=output_directory) \n # So finally we need to fix the LD matrices for inputting into PAINTOR. \n\n with open(os.path.join(output_directory, 'input.files'), 'w') as out_f:\n for snp in snp_list:\n out_f.write(snp.rsid +'\\n')\n # Remove .tbi files\n for file in os.listdir('.'):\n if fnmatch.fnmatch(file, '*.tbi'):\n try:\n os.remove(file)\n except OSError:\n logging.warning(\"Could not remove a .tbi file from the 1000 genomes tabix run\")\n else: \n loci = []\n for snp in snp_list:\n loci.append(snp.rsid)\n if bed_directory is not None:\n logging.info(\"Annotation using bed files\")\n generate_bed_file_annotations(loci=loci, bed_directory=bed_directory, output_directory=output_directory) \n logging.info(\"Finemapping file preparation complete\")","def get_batch_data(cur_samples, is_training=False):\n psg1_max_len, psg2_max_len, batch_max_path_cnt, psg1_max_ems_cnt, psg2_max_ems_cnt, \\\n joint_max_ent_cnt, psg1_max_sent, psg2_max_sent, max_arg1_mention, max_arg2_mention = get_max_len(cur_samples)\n psg1_max_len += 1\n\n psg1_words_list = list()\n psg1_mask_list = list()\n psg2_words_list = list()\n psg2_mask_list = list()\n psg1_coref_list = list()\n psg2_coref_list = list()\n arg1_s_list = []\n arg1_e_list = []\n arg2_s_list = []\n arg2_e_list = []\n path_e1_s = []\n path_e1_e = []\n path_c1_s = []\n path_c1_e = []\n path_c2_s = []\n path_c2_e = []\n path_e2_s = []\n path_e2_e = []\n path_mask = []\n arg1_mask = []\n arg2_mask = []\n\n ems_path_e1 = []\n ems_path_c1 = []\n ems_path_c2 = []\n ems_path_e2 = []\n ems_path_mask = []\n\n psg1_ems_start_list = []\n psg1_ems_end_list = []\n psg1_ems_mask_list = []\n psg1_ems_adj_mat_list = []\n psg1_ems_words_mask_list = []\n\n psg2_ems_start_list = []\n psg2_ems_end_list = []\n psg2_ems_mask_list = []\n psg2_ems_adj_mat_list = []\n psg2_ems_words_mask_list = []\n\n joint_ent_list = []\n joint_ent_mask_list = []\n joint_ent_adj_mat_list = []\n joint_ent_sent_mask_list = []\n linked_entities_list = []\n linked_entities_mask = []\n\n rel_labels_list = []\n sample_idx = 0\n for sample in cur_samples:\n words_seq, words_mask = get_words_index_seq(sample.Psg1Words, psg1_max_len, True)\n psg1_words_list.append(words_seq)\n psg1_mask_list.append(words_mask)\n\n words_seq, words_mask = get_words_index_seq(sample.Psg2Words, psg2_max_len, False)\n psg2_words_list.append(words_seq)\n psg2_mask_list.append(words_mask)\n\n psg1_coref_list.append(get_padded_coref_seq(sample.Psg1Coref, psg1_max_len, True))\n psg2_coref_list.append(get_padded_coref_seq(sample.Psg2Coref, psg2_max_len, False))\n\n cur_arg1_s = [sample_idx * psg1_max_len + idx[0] for idx in sample.Arg1Loc] + \\\n [sample_idx * psg1_max_len for i in range(max_arg1_mention - len(sample.Arg1Loc))]\n cur_arg1_e = [sample_idx * psg1_max_len + idx[1] for idx in sample.Arg1Loc] + \\\n [sample_idx * psg1_max_len for i in range(max_arg1_mention - len(sample.Arg1Loc))]\n\n cur_arg2_s = [sample_idx * psg2_max_len + idx[0] for idx in sample.Arg2Loc] + \\\n [sample_idx * psg2_max_len for i in range(max_arg2_mention - len(sample.Arg2Loc))]\n cur_arg2_e = [sample_idx * psg2_max_len + idx[1] for idx in sample.Arg2Loc] + \\\n [sample_idx * psg2_max_len for i in range(max_arg2_mention - len(sample.Arg2Loc))]\n\n arg1_s_list.append(cur_arg1_s)\n arg1_e_list.append(cur_arg1_e)\n arg2_s_list.append(cur_arg2_s)\n arg2_e_list.append(cur_arg2_e)\n\n arg1_mask.append(get_arg_mask(sample.Arg1Loc, max_arg1_mention))\n arg2_mask.append(get_arg_mask(sample.Arg2Loc, max_arg2_mention))\n\n path_mask_len = batch_max_path_cnt - len(sample.Paths[0])\n cur_path_mask = [0 for i in range(len(sample.Paths[0]))] + [1 for i in range(path_mask_len)]\n path_mask.append(cur_path_mask)\n\n cur_path_e1_s = [sample_idx * psg1_max_len + idx for idx in sample.Paths[0]] + \\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\n cur_path_e1_e = [sample_idx * psg1_max_len + idx for idx in sample.Paths[1]] + \\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\n\n cur_path_c1_s = [sample_idx * psg1_max_len + idx for idx in sample.Paths[2]] + \\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\n cur_path_c1_e = [sample_idx * psg1_max_len + idx for idx in sample.Paths[3]] + \\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\n\n cur_path_c2_s = [sample_idx * psg2_max_len + idx for idx in sample.Paths[4]] + \\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\n cur_path_c2_e = [sample_idx * psg2_max_len + idx for idx in sample.Paths[5]] + \\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\n\n cur_path_e2_s = [sample_idx * psg2_max_len + idx for idx in sample.Paths[6]] + \\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\n cur_path_e2_e = [sample_idx * psg2_max_len + idx for idx in sample.Paths[7]] + \\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\n\n path_e1_s.append(cur_path_e1_s)\n path_e1_e.append(cur_path_e1_e)\n\n path_c1_s.append(cur_path_c1_s)\n path_c1_e.append(cur_path_c1_e)\n\n path_c2_s.append(cur_path_c2_s)\n path_c2_e.append(cur_path_c2_e)\n\n path_e2_s.append(cur_path_e2_s)\n path_e2_e.append(cur_path_e2_e)\n\n cur_psg1_ems_s = [sample_idx * psg1_max_len + tup[0] for tup in sample.Psg1EMs] + \\\n [sample_idx * psg1_max_len for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs))]\n cur_psg1_ems_e = [sample_idx * psg1_max_len + tup[1] for tup in sample.Psg1EMs] + \\\n [sample_idx * psg1_max_len for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs))]\n cur_psg1_ems_mask = [0 for i in range(len(sample.Psg1EMs))] + \\\n [1 for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs))]\n psg1_ems_start_list.append(cur_psg1_ems_s)\n psg1_ems_end_list.append(cur_psg1_ems_e)\n psg1_ems_mask_list.append(cur_psg1_ems_mask)\n\n cur_psg1_words_mask = []\n for tup in sample.Psg1EMsWords:\n ems_words = [1 for idx in range(psg1_max_len)]\n ems_words[tup[0]: tup[1] + 1] = [0 for i in range(tup[1] + 1 - tup[0])]\n cur_psg1_words_mask.append(ems_words)\n for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs)):\n cur_psg1_words_mask.append([0 for idx in range(psg1_max_len)])\n psg1_ems_words_mask_list.append(cur_psg1_words_mask)\n\n cur_psg2_ems_s = [sample_idx * psg2_max_len + tup[0] for tup in sample.Psg2EMs] + \\\n [sample_idx * psg2_max_len for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs))]\n cur_psg2_ems_e = [sample_idx * psg2_max_len + tup[1] for tup in sample.Psg2EMs] + \\\n [sample_idx * psg2_max_len for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs))]\n cur_psg2_ems_mask = [0 for i in range(len(sample.Psg2EMs))] + \\\n [1 for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs))]\n psg2_ems_start_list.append(cur_psg2_ems_s)\n psg2_ems_end_list.append(cur_psg2_ems_e)\n psg2_ems_mask_list.append(cur_psg2_ems_mask)\n\n cur_psg2_words_mask = []\n for tup in sample.Psg2EMsWords:\n ems_words = [1 for idx in range(psg2_max_len)]\n ems_words[tup[0]: tup[1] + 1] = [0 for i in range(tup[1] + 1 - tup[0])]\n cur_psg2_words_mask.append(ems_words)\n for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs)):\n cur_psg2_words_mask.append([0 for idx in range(psg2_max_len)])\n psg2_ems_words_mask_list.append(cur_psg2_words_mask)\n\n cur_psg1_ems_adj_mat = np.zeros((psg1_max_ems_cnt, psg1_max_ems_cnt), dtype=np.float32)\n cur_psg1_ems_adj_mat[:len(sample.Psg1EMs), :len(sample.Psg1EMs)] = sample.Psg1EMsAdjMat\n psg1_ems_adj_mat_list.append(cur_psg1_ems_adj_mat)\n\n cur_psg2_ems_adj_mat = np.zeros((psg2_max_ems_cnt, psg2_max_ems_cnt), dtype=np.float32)\n cur_psg2_ems_adj_mat[:len(sample.Psg2EMs), :len(sample.Psg2EMs)] = sample.Psg2EMsAdjMat\n psg2_ems_adj_mat_list.append(cur_psg2_ems_adj_mat)\n\n cur_joint_ents = []\n cur_linked_entities = []\n\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\n for idx in sample.Arg1EMs:\n cur_joint_ent[idx] = 1\n cur_joint_ents.append(cur_joint_ent)\n\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\n for idx in sample.Arg2EMs:\n cur_joint_ent[psg1_max_ems_cnt + idx] = 1\n cur_joint_ents.append(cur_joint_ent)\n\n for i in range(0, len(sample.BothPsgEntEMs), 2):\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\n for idx in sample.BothPsgEntEMs[i]:\n cur_joint_ent[idx] = 1\n for idx in sample.BothPsgEntEMs[i + 1]:\n cur_joint_ent[psg1_max_ems_cnt + idx] = 1\n cur_joint_ents.append(cur_joint_ent)\n cur_linked_entities.append(2 + i)\n\n cur_ems_paths = []\n for ems_e1_idx in sample.Arg1EMs:\n for ems_e2_idx in sample.Arg2EMs:\n for ems_link_idx in range(0, len(sample.BothPsgEntEMs), 2):\n for ems_c1_idx in sample.BothPsgEntEMs[ems_link_idx]:\n for ems_c2_idx in sample.BothPsgEntEMs[ems_link_idx + 1]:\n cur_ems_paths.append((ems_e1_idx, ems_c1_idx, ems_c2_idx, ems_e2_idx))\n cur_ems_paths = cur_ems_paths[:min(batch_max_path_cnt, len(cur_ems_paths))]\n ems_path_mask_len = batch_max_path_cnt - len(cur_ems_paths)\n cur_ems_path_mask = [0 for i in range(len(cur_ems_paths))] + [1 for i in range(ems_path_mask_len)]\n ems_path_mask.append(cur_ems_path_mask)\n cur_ems_path_e1 = [sample_idx * psg1_max_ems_cnt + tup[0] for tup in cur_ems_paths] + \\\n [sample_idx * psg1_max_ems_cnt for i in range(ems_path_mask_len)]\n ems_path_e1.append(cur_ems_path_e1)\n cur_ems_path_c1 = [sample_idx * psg1_max_ems_cnt + tup[1] for tup in cur_ems_paths] + \\\n [sample_idx * psg1_max_ems_cnt for i in range(ems_path_mask_len)]\n ems_path_c1.append(cur_ems_path_c1)\n cur_ems_path_c2 = [sample_idx * psg2_max_ems_cnt + tup[2] for tup in cur_ems_paths] + \\\n [sample_idx * psg2_max_ems_cnt for i in range(ems_path_mask_len)]\n ems_path_c2.append(cur_ems_path_c2)\n cur_ems_path_e2 = [sample_idx * psg2_max_ems_cnt + tup[3] for tup in cur_ems_paths] + \\\n [sample_idx * psg2_max_ems_cnt for i in range(ems_path_mask_len)]\n ems_path_e2.append(cur_ems_path_e2)\n\n for i in range(len(sample.OnlyPsg1EntEMs)):\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\n for idx in sample.OnlyPsg1EntEMs[i]:\n cur_joint_ent[idx] = 1\n cur_joint_ents.append(cur_joint_ent)\n\n for i in range(len(sample.OnlyPsg2EntEMs)):\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\n for idx in sample.OnlyPsg2EntEMs[i]:\n cur_joint_ent[psg1_max_ems_cnt + idx] = 1\n cur_joint_ents.append(cur_joint_ent)\n\n cur_linked_ents = [sample_idx * joint_max_ent_cnt + idx for idx in cur_linked_entities] + \\\n [sample_idx * joint_max_ent_cnt for idx in range(joint_max_ent_cnt - len(cur_linked_entities))]\n linked_entities_list.append(cur_linked_ents)\n cur_linked_ents_mask = [0 for i in range(len(cur_linked_entities))] + \\\n [1 for i in range(joint_max_ent_cnt-len(cur_linked_entities))]\n linked_entities_mask.append(cur_linked_ents_mask)\n\n cur_joint_ents_sents_mask = []\n for lst in sample.EntsSents:\n cur_joint_ent_sents_mask = [1 for idx in range(psg1_max_sent + psg2_max_sent)]\n for tup in lst:\n if tup[0] == 1:\n cur_joint_ent_sents_mask[tup[1]] = 0\n else:\n cur_joint_ent_sents_mask[psg1_max_sent + tup[1]] = 0\n cur_joint_ents_sents_mask.append(cur_joint_ent_sents_mask)\n\n for i in range(joint_max_ent_cnt - len(cur_joint_ents)):\n cur_joint_ent_sents_mask = [0 for idx in range(psg1_max_sent + psg2_max_sent)]\n cur_joint_ents_sents_mask.append(cur_joint_ent_sents_mask)\n\n joint_ent_sent_mask_list.append(cur_joint_ents_sents_mask)\n\n cur_joint_ent_mask = [0 for i in range(len(cur_joint_ents))] + \\\n [1 for i in range(joint_max_ent_cnt - len(cur_joint_ents))]\n joint_ent_mask_list.append(cur_joint_ent_mask)\n\n cur_joint_ent_adj_mat = np.zeros((joint_max_ent_cnt, joint_max_ent_cnt), dtype=np.float32)\n cur_joint_ent_adj_mat[:len(cur_joint_ents), :len(cur_joint_ents)] = sample.MergedAdjMat\n\n joint_ent_adj_mat_list.append(cur_joint_ent_adj_mat)\n\n for i in range(joint_max_ent_cnt - len(cur_joint_ents)):\n cur_joint_ents.append([1 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)])\n\n joint_ent_list.append(cur_joint_ents)\n\n sample_idx += 1\n\n if is_training:\n rel_labels_list.append(rel_name_to_idx[sample.Relation])\n\n return {'psg1_words': np.array(psg1_words_list, dtype=np.float32),\n 'psg1_mask': np.array(psg1_mask_list),\n 'psg1_coref': np.array(psg1_coref_list),\n 'psg2_words': np.array(psg2_words_list, dtype=np.float32),\n 'psg2_mask': np.array(psg2_mask_list),\n 'psg2_coref': np.array(psg2_coref_list),\n 'arg1_start': np.array(arg1_s_list),\n 'arg1_end': np.array(arg1_e_list),\n 'arg2_start': np.array(arg2_s_list),\n 'arg2_end': np.array(arg2_e_list),\n 'arg1_mask': np.array(arg1_mask),\n 'arg2_mask': np.array(arg2_mask),\n 'path_e1_s': np.array(path_e1_s),\n 'path_e1_e': np.array(path_e1_e),\n 'path_c1_s': np.array(path_c1_s),\n 'path_c1_e': np.array(path_c1_e),\n 'path_c2_s': np.array(path_c2_s),\n 'path_c2_e': np.array(path_c2_e),\n 'path_e2_s': np.array(path_e2_s),\n 'path_e2_e': np.array(path_e2_e),\n 'path_mask': np.array(path_mask),\n 'psg1_ems_start': np.array(psg1_ems_start_list),\n 'psg1_ems_end': np.array(psg1_ems_end_list),\n 'psg1_ems_mask': np.array(psg1_ems_mask_list),\n 'psg1_ems_adj_mat': np.array(psg1_ems_adj_mat_list),\n 'psg1_ems_words_mask': np.array(psg1_ems_words_mask_list),\n 'psg2_ems_start': np.array(psg2_ems_start_list),\n 'psg2_ems_end': np.array(psg2_ems_end_list),\n 'psg2_ems_mask': np.array(psg2_ems_mask_list),\n 'psg2_ems_adj_mat': np.array(psg2_ems_adj_mat_list),\n 'psg2_ems_words_mask': np.array(psg2_ems_words_mask_list),\n 'joint_ent': np.array(joint_ent_list),\n 'joint_ent_mask': np.array(joint_ent_mask_list),\n 'joint_ent_adj_mat': np.array(joint_ent_adj_mat_list),\n 'linked_entities_map': np.array(linked_entities_list),\n 'linked_entities_mask':np.array(linked_entities_mask),\n 'ems_path_e1': np.array(ems_path_e1),\n 'ems_path_c1': np.array(ems_path_c1),\n 'ems_path_c2': np.array(ems_path_c2),\n 'ems_path_e2': np.array(ems_path_e2),\n 'ems_path_mask': np.array(ems_path_mask)}, \\\n {'target': np.array(rel_labels_list, dtype=np.int32)}","def build(self, fasta_files, subtype_file):\n\n # Check subtype file\n subtypes = {}\n\n for row in csv.reader(open(subtype_file,'r'),delimiter='\\t'):\n name = row[0]\n subt = row[1]\n\n subtypes[name] = subt.lower()\n \n if isinstance(fasta_files, str):\n # Create list\n fasta_files = [fasta_files]\n\n if len(fasta_files) != self._nloci:\n raise Exception(\"Missing fasta file. {} fasta files provided for {} number of loci.\".format(len(fasta_files), self._nloci))\n\n concat = LociConcat()\n sequences = concat.collect(fasta_files)\n \n for name,seqslist in sequences.iteritems():\n this_subt = subtypes[name]\n\n for seqs in seqslist:\n self.add(seqs, name, this_subt)","def _main_extract_CDS(args = None, stdout = None, stderr = None,\n gb_record_fmtdict = None,\n gb_cds_fmtdict = None) :\n if stdout is None :\n stdout = sys.stdout\n if stderr is None :\n stderr = sys.stderr\n if gb_record_fmtdict is None :\n gb_record_fmtdict = _GB_RECORD_FMTDICT\n if gb_cds_fmtdict is None :\n gb_cds_fmtdict = _GB_CDS_FMTDICT\n # Process arguments\n if args is None :\n parser = _makeParser_extract_CDS()\n args = parser.parse_args()\n args = _processArgsToLogic_extract_CDS(args, stdout, stderr,\n gb_record_fmtdict, gb_cds_fmtdict)\n # Go through the input files\n uniqueSeq = dict()\n i_file = 0\n for fi in args.genbank_records :\n i_file += 1\n if args.verbose :\n stderr.write(time.asctime() + \" - \" +\n \"Processing file \" + str(i_file) + \" : \" +\n os.path.basename(fi) + \" - \" +\n \"N unique seq : \" + str(len(uniqueSeq.keys())) + \"\\n\")\n record = SeqIO.parse(fi, \"genbank\")\n for r in record :\n if not args.actionFlags.get(\"DoCount\", False) :\n (summaryString, uniqueSeq, newSeq) = (\n _summarizeRecord(r, args.outfmt, args.hash, uniqueSeq))\n stdout.write(summaryString)\n else :\n count = len([x for x in r.features if x.type == \"CDS\"])\n stdout.write(r.annotations[\"gi\"] + \"\\t\" + str(count) + \"\\n\")\n # Write unique sequences\n if args.actionFlags.get(\"DoUniqueSequences\", False) :\n with open(args.unique, \"w\") as fo :\n for (k, v) in uniqueSeq.items() :\n fo.write(\">\" + k + \"\\n\")\n fo.write(v + \"\\n\")","def generate_batches(source,n=20):\n blist=[]\n with open(source) as f_source:\n start=next_n_lines(f_source, n); string=gen_string(start); blist.append(string)\n while start!=[]: start=next_n_lines(f_source, n); string=gen_string(start); blist.append(string)\n print('2. Generation of batches completed!')\n return blist","def add_batch(self,ligand_file_path,receptor_file_path,batch_predictions,batch_labels):\n\n\n # extract meaningful names from the file path\n def extract_file_from_path(file_path):\n return file_path.split(\"/\")[-1]\n\n ligand_filenames = np.char.array(map(extract_file_from_path,ligand_file_path))\n receptor_filenames = np.char.array(map(extract_file_from_path,receptor_file_path))\n batch_pl_pairs = ligand_filenames + \",\" + receptor_filenames\n\n # sort the batch by protein-ligand pairs\n order = batch_pl_pairs.argsort()\n batch_pl_pairs = batch_pl_pairs[order]\n batch_predictions = batch_predictions[order]\n batch_labels = batch_labels[order]\n\n # check if all of the entries in the batch are unique\n if not np.array_equal(batch_pl_pairs,np.unique(batch_pl_pairs)):\n raise Exception(\"batch has duplicate entries\")\n\n # get binmask with True for each non-unique protein-ligand pair, False for unique protein-ligand pair\n binmask_self = (np.searchsorted(batch_pl_pairs,self.pl_pairs, 'right') - np.searchsorted(batch_pl_pairs,self.pl_pairs,'left')) == 1\n binmask_batch = (np.searchsorted(self.pl_pairs,batch_pl_pairs, 'right') - np.searchsorted(self.pl_pairs,batch_pl_pairs,'left')) == 1\n\n # check if the entries appended to each other have similar names\n if not np.array_equal(batch_pl_pairs[binmask_batch],self.pl_pairs[binmask_self]):\n raise Exception('Error while merging arrays. Names do not match')\n\n # check if labels are similar\n if not np.array_equal(batch_labels[binmask_batch],self.labels[binmask_self]):\n raise Exception('Error while merging arrays. Labels for the same example should be similar')\n\n # split into overlapping and not overlapping entries\n overlap_pl_pairs = batch_pl_pairs[binmask_batch]\n overlap_predictions = np.char.array(self.predictions[binmask_self])\n batch_overlap_predictions = self.usd_format(batch_predictions[binmask_batch])\n\n # for known entries join all of the predictions together\n overlap_predictions = overlap_predictions + \",\" + batch_overlap_predictions\n overlap_labels = batch_labels[binmask_batch]\n\n # merge unique and not unique predictions\n self.pl_pairs = np.hstack((self.pl_pairs[-binmask_self],batch_pl_pairs[-binmask_batch],overlap_pl_pairs))\n self.predictions = np.hstack((self.predictions[-binmask_self],self.usd_format(batch_predictions[-binmask_batch]),overlap_predictions))\n self.labels = np.hstack((self.labels[-binmask_self],batch_labels[-binmask_batch],overlap_labels))\n\n # now sort everything by the first column\n order = self.pl_pairs.argsort()\n self.pl_pairs = self.pl_pairs[order]\n self.predictions = self.predictions[order]\n self.labels = self.labels[order]","def get_batch(self):\n self._next_minibatch()\n scans = np.empty((self.batch_size, 2, RANGE_BINS, HEADING_BINS), dtype=np.float32)\n targets = np.empty((self.batch_size, 1, RANGE_BINS, HEADING_BINS), dtype=np.float32)\n features = np.empty((self.batch_size, 2048, 3, 12), dtype=np.float32)\n\n long_ids = []\n assert len(self.batch) == self.batch_size\n for n,item in enumerate(self.batch):\n long_ids.append(item['scan'] + \"_\" + item['image_id'])\n # Select one feature if there are multiple versions\n selected_features = random.choice(item['features'])\n\n if self.augment:\n # random rotation by a 30 degree increment\n rotation = random.randint(0,12)\n ix = int(len(item['laser'])/12*rotation)\n laser = np.roll(item['laser'], ix) # end rolls around to start\n tgt_heading = np.array([normalize_angle(h + (math.pi/6)*rotation) for h in item['target_heading']])\n feat = np.roll(selected_features, rotation, axis=1)\n else:\n laser = np.array(item['laser'], copy=True)\n tgt_heading = item['target_heading']\n feat = selected_features\n\n # missing part of scan\n length = len(laser)\n miss_start = random.randint(0, length)\n miss_end = miss_start + int((360-self.laser_fov_deg)/360 * length)\n laser[miss_start:miss_end] = -1\n if miss_end >= length:\n laser[:miss_end-length] = -1\n\n # dropout. Unlike conventional dropout, this occurs at both train and test time and is \n # considered to represent missing return values in the laser scan.\n drop = np.random.random_sample((len(laser),))\n laser[drop < self.dropout] = -1 # Indicates missing return.\n\n scans[n, 1, :, :] = radial_occupancy(laser).transpose((2,0,1))\n # add a range indicating channel\n r = np.linspace(-0.5, 0.5, num=RANGE_BINS)\n scans[:,0,:,:] = np.expand_dims(np.expand_dims(r, axis=0), axis=2)\n targets[n, :, :, :] = radial_target(tgt_heading, item['target_range']).transpose((2,0,1))\n features[n, :, :, :] = feat.transpose((2,0,1))\n # features = np.zeros_like(features) # How does it work without image features?\n # scans = np.zeros_like(scans) # How does it work with only image features?\n # Normalize targets into a probability dist\n targets /= targets.reshape(targets.shape[0], -1).sum(axis=1).reshape(-1, 1, 1, 1)\n return scans, features, targets, long_ids","def build_training_data():\r\n for i in range(len(FILE_NAMES)):\r\n input_text = read_file(FILE_NAMES[i])\r\n list_of_word_lines = limiting_sentence_length(input_text)\r\n data = create_training_data_file(list_of_word_lines, LANGUAGE[i])\r\n write_training_data(data, LANGUAGE[i])\r\n merge_training_data()","def __divide_into_batches(self):\n print('Creating batches for parallel execution')\n num_suites = len(self.execution_file_json['suites'])\n full_batches = num_suites // self.max_suites\n print('- Full batches=%s' % full_batches)\n if num_suites % self.max_suites > 0:\n has_partial = True\n else:\n has_partial = False\n print('- Partial batch at end: %s' % has_partial)\n if has_partial:\n total_batches = full_batches + 1\n else:\n total_batches = full_batches\n print('- %s suites will be divided into %s container batches using max suites %s' % (\n num_suites, total_batches, self.max_suites))\n self.suite_batches = []\n # split full batches\n for batch_counter in range(0, full_batches):\n start_index = batch_counter * self.max_suites\n batch = []\n for counter in range(start_index, start_index + self.max_suites):\n batch.append(self.execution_file_json['suites'][counter])\n self.suite_batches.append(batch)\n print('- full batches created', self.suite_batches)\n # add partial batch\n if has_partial:\n start_index = full_batches * self.max_suites\n batch = []\n for counter in range(start_index, num_suites):\n batch.append(self.execution_file_json['suites'][counter])\n self.suite_batches.append(batch)\n print('- partial batch created', self.suite_batches)","def main():\n # Verbosity: 1=Selection Results, >1 is various debugging information\n verbose = 0\n print \"build_all.py running with verbose=%s\"%(str(verbose))\n if verbose:\n print \"Fiducial Cut: \",fid_cut_hex,\"(apethum, z_min, z_max)\"\n print \"Max Drift Distance = %.4f us\"%(max_drift_time)\n\n tree = get_data_tree(list='All') # Golden All\n \n # We use the EXOFitting processed tree to get high-level physical quantities\n # like the anticorrelated energy, etc. \n #ptree_file = ROOT.TFile(preprocessed_tree)\n #ptree = ROOT.Get(\"dataTree\")\n #if verbose: print \"Indexing EXOFitting PreProcessed tree\"\n #ptree.BuildIndex(\"runNum\", \"eventNum\")\n #if verbose: print \" ...done\"\n\n cuts = \"\"\n\n #There must be at least 1 scintillation cluster:\n #cuts = \"@fScintClusters.size()>=1\"\n #cuts = \"(fScintClusters.GetCountsOnAPDPlane(0)+fScintClusters.GetCountsOnAPDPlane(1))>20000\"\n\n # The minimum scintinlation counts must be > 20000 and <70000\n # I observe that three peaks presumable alphas are at 38500, 42200, and 55000\n # So Rn222=5.4MeV, Po218=6MeV, Po214=7.7MeV\n # calibrate:: y=mx+b, m=6167, b=5198\n #cuts = \"fScintClusters.fRawEnergy>20000 && fScintClusters.fRawEnergy<70000\"\n #cuts += \"&& fScintClusters.fRawEnergy>22000 && fScintClusters.fRawEnergy<80000\"\n #cuts += \" && Sum$(fAPDs.fRawCounts) > 8000\"\n\n # Ignore Noise and Muon tagged events\n cuts +=\"fEventHeader.fTaggedAsNoise==0 && fEventHeader.fTaggedAsMuon==0\" \n\n # That's the last of the cuts, lets show the user what the cut looks like\n print \"Applying Cuts to data: \\n%s\"%cuts\n\n #Draw is the fastest method to apply cuts, in the end what we want is a reduced data list\n # to perform a more targeted analysis...\n tree.Draw(\">>+elist_alpha_canidates\",cuts,\"goff\")\n elist_alpha_canidates = ROOT.gDirectory.Get(\"elist_alpha_canidates\")\n print \"There are %d events passing the initial cuts\"%elist_alpha_canidates.GetN()\n\n #Now we have to look at events passing the cuts individually\n tf = ROOT.TFile(\"all.root\",\"RECREATE\")\n Rntree = tree.CloneTree(0)\n \n for i in range(elist_alpha_canidates.GetN()):\n # Print Progress\n if i%int(elist_alpha_canidates.GetN()/20) == 0:\n print \"%d of %d\"%(i,elist_alpha_canidates.GetN())\n\n #Grab the event data\n tree.GetEntry(elist_alpha_canidates.GetEntry(i))\n #ed = tree.EventBranch\n #if verbose>1: print_event_data(ed,verbose)\n\n #is_alphaish = check_alpha_like(ed,verbose)\n \n #is the event a fully reconstructed BiPo?\n #is_bipo = check_full_BiPo(ed,verbose)\n\n # Case1 (position matched Bi-Po)\n #is_case1 = check_case1(ed,verbose)\n #print \"BiPo=%s, Case1=%s\"%(is_bipo, is_case1) \n #raw_input('<hit any key to continue>')\n #if is_bipo or is_alphaish:\n # Write the EventData of events which pass any of our selection criteria\n # to ROOT file\n Rntree.Fill()\n\n Rntree.AutoSave()","def process_batch(self, batch):\n # extend with current batch\n self._extend(batch)\n\n # unpack and compute bounds\n length = len(self.obs)\n c = self.c\n\n # normally we cannot compute samples for the last c elements, but\n # in the terminal case, we halluciante values where necessary\n end = length if batch.terminal else length - c\n\n # collect samples to return in a FeudalBatch\n feudal_batch = FeudalBatch()\n for t in range(c, end):\n\n # state difference\n s_diff = self.s[t + c] - self.s[t]\n\n # intrinsic reward\n ri = 0\n # note that this for loop considers s and g values\n # 1 timestep to c timesteps (inclusively) ago\n for i in range(1, c + 1):\n ri_s_diff = self.s[t] - self.s[t - i]\n if np.linalg.norm(ri_s_diff) != 0:\n ri += cosine_similarity(ri_s_diff, self.g[t - i])\n ri /= c\n\n # sum of g values used to derive w, input to the linear transform\n gsum = np.zeros_like(self.g[t - c])\n for i in range(t - c, t + 1):\n gsum += self.g[i]\n\n # add to the batch\n feudal_batch.add(self.obs[t], self.a[t], self.returns[t], s_diff,\n ri, gsum, self.features[t])\n\n # in the terminal case, set reset flag\n if batch.terminal:\n self.last_terminal = True\n # in the general case, forget all but the last 2 * c elements\n # reason being that the first c of those we have already computed\n # a batch for, and the second c need those first c\n else:\n twoc = 2 * self.c\n self.obs = self.obs[-twoc:]\n self.a = self.a[-twoc:]\n self.returns = self.returns[-twoc:]\n self.s = self.s[-twoc:]\n self.g = self.g[-twoc:]\n self.features = self.features[-twoc:]\n\n return feudal_batch.get_batch()","def __concatonate_files(self, new_file_name, parent_folder):\n\n # make the output directory\n output_file = self.save_directory + \"/\" + new_file_name\n\n # check if save_directory exists\n if not os.path.exists(self.save_directory):\n try:\n # make the directory\n os.makedirs(self.save_directory)\n except PermissionError:\n # if the user is unable to write to this directory, we should not continue\n print(\"You do not have the correct permissions for creating a directory here. Please try again.\")\n exit(-1)\n\n barcode_files = []\n for root, directory, files in os.walk(parent_folder):\n # we need to know where each file is in the barcode folder so we can read data from it\n for name in files:\n barcode_files.append( os.path.join(root, name) )\n\n with open(output_file, 'w') as writer:\n for name in barcode_files:\n with open(name, 'r') as reader:\n for line in reader:\n writer.write(line)","def generate_sample_sheet(self):\n pool = self.pool\n bcl2fastq_sample_ids = []\n i7_names = []\n i7_sequences = []\n i5_names = []\n i5_sequences = []\n wells = []\n plate = pool.container.external_id\n sample_ids = []\n sequencer_type = self.sequencer.equipment_type\n\n for component in pool.components:\n lp_composition = component['composition']\n # Get the well information\n wells.append(lp_composition.container.well_id)\n # Get the i7 index information\n i7_comp = lp_composition.i7_composition.primer_set_composition\n i7_names.append(i7_comp.external_id)\n i7_sequences.append(i7_comp.barcode)\n # Get the i5 index information\n i5_comp = lp_composition.i5_composition.primer_set_composition\n i5_names.append(i5_comp.external_id)\n i5_sequences.append(i5_comp.barcode)\n # Get the sample id\n sample_id = lp_composition.normalized_gdna_composition.\\\n gdna_composition.sample_composition.content\n sample_ids.append(sample_id)\n\n # Transform te sample ids to be bcl2fastq-compatible\n bcl2fastq_sample_ids = [\n SequencingProcess._bcl_scrub_name(sid) for sid in sample_ids]\n # Reverse the i5 sequences if needed based on the sequencer\n i5_sequences = SequencingProcess._sequencer_i5_index(\n sequencer_type, i5_sequences)\n\n data = SequencingProcess._format_sample_sheet_data(\n bcl2fastq_sample_ids, i7_names, i7_sequences, i5_names,\n i5_sequences, wells=wells, sample_plate=plate,\n description=sample_ids, sample_proj=self.run_name,\n lanes=self.lanes, sep=',')\n\n contacts = {c.name: c.email for c in self.contacts}\n pi = self.principal_investigator\n principal_investigator = {pi.name: pi.email}\n sample_sheet_dict = {\n 'comments': SequencingProcess._format_sample_sheet_comments(\n principal_investigator=principal_investigator,\n contacts=contacts),\n 'IEMFileVersion': '4',\n 'Investigator Name': pi.name,\n 'Experiment Name': self.experiment,\n 'Date': str(self.date),\n 'Workflow': 'GenerateFASTQ',\n 'Application': 'FASTQ Only',\n 'Assay': self.assay,\n 'Description': '',\n 'Chemistry': 'Default',\n 'read1': self.fwd_cycles,\n 'read2': self.rev_cycles,\n 'ReverseComplement': '0',\n 'data': data}\n return SequencingProcess._format_sample_sheet(sample_sheet_dict)","def build(self):\n cursor = self.cnx.cursor()\n query = \"\"\"\n CREATE TABLE IF NOT EXISTS `FileNameFilter_scores` (\n id BIGINT unsigned NOT NULL,\n score double DEFAULT NULL,\n PRIMARY KEY(id),\n CONSTRAINT `FNF_unique_file1_id` FOREIGN KEY (`id`)\n REFERENCES `unique_file` (`id`) ON DELETE NO ACTION ON UPDATE NO ACTION\n ) ENGINE=InnoDB\n \"\"\"\n cursor.execute(query)\n self.cnx.commit()\n\n query = \"\"\"\n CREATE TABLE IF NOT EXISTS FileNameFilter_unique_name (\n id BIGINT UNSIGNED NOT NULL AUTO_INCREMENT,\n file_name VARCHAR(255) CHARACTER SET utf8 COLLATE utf8_bin NOT NULL,\n unique_path_id INT(10) NOT NULL,\n count INT DEFAULT 1,\n PRIMARY KEY (id),\n UNIQUE INDEX file_path_idx USING BTREE (file_name ASC, unique_path_id),\n INDEX file_name_idx USING BTREE (file_name ASC)\n ) ENGINE=InnoDB;\n \"\"\"\n cursor.execute(query)\n self.cnx.commit()\n cursor.close()","def start_queryResult_generator(inFile, fDic, work_sheet):\n \"\"\" http://biopython.org/DIST/docs/api/Bio.SearchIO.BlastIO-module.html\"\"\"\n qGenerator = SearchIO.parse(inFile, 'blast-xml')\n max_hits = 0\n query_count = 1\n # Step through all the records in the lump xml data file and write out\n # each separate hit to file. Also write the summary information to the\n # work sheet.\n for query_result in qGenerator:\n print('Processing Query BLAST return ' + str(query_count))\n number_hits = int(len(query_result.hits))\n # Extend header out right if new MAXHITS\n if number_hits > max_hits:\n max_hits = number_hits \n if number_hits == 0:\n # Construct path plus file name for no hit query\n filename = str(fDic['topDir'] + fDic['noHit'] + 'Query_' \n + str(query_count) + '_H_none.xml')\n # Write out any Queries that had to hits to a no Hit subfolder\n SearchIO.write(query_result, filename, 'blast-xml')\n write_qr_to_ws(query_count, query_result, work_sheet)\n else :\n # Now set up a counter of 'hits' in the QueryResult so hit's\n # can be sliced away into their own record cleanly.\n hit_count = 0;\n for hit in query_result.hits:\n total_hsps = len (hit.hsps)\n lowest_eval = hit.hsps[0].evalue\n best_hsp = hit.hsps[0]\n for hsp in hit.hsps:\n if hsp.evalue < lowest_eval:\n lowest_eval = hsp.evalue\n best_hsp = hsp\n filename = str(fDic['topDir'] + outputFileName(query_count, hit, best_hsp))\n SearchIO.write(query_result[hit_count:(hit_count + 1)], filename , 'blast-xml')\n hit_count += 1\n # Write out query_result to worksheet \n write_qr_to_ws(query_count, query_result, work_sheet)\n query_count += 1\n # break is debugging code\n # if query_count == 20:\n # break\n build_ws_header(work_sheet, max_hits)\n return qGenerator","def test_split_fasta_on_sample_ids_to_files(self):\r\n temp_output_dir = mkdtemp()\r\n self.dirs_to_remove.append(temp_output_dir)\r\n\r\n split_fasta_on_sample_ids_to_files(\r\n parse_fasta(self.fasta2),\r\n output_dir=temp_output_dir,\r\n per_sample_buffer_size=2)\r\n self.files_to_remove.extend(glob('%s/*fasta' % temp_output_dir))\r\n\r\n # confirm that all files are as expected\r\n self.assertEqual(open('%s/Samp1.fasta' % temp_output_dir).read(),\r\n \">Samp1_42\\nACCGGTT\\n>Samp1_43 some comme_nt\\nAACCG\\n>Samp1_44\\nA\\n\")\r\n self.assertEqual(open('%s/s2_a.fasta' % temp_output_dir).read(),\r\n \">s2_a_50\\nGGGCCC\\n\")\r\n self.assertEqual(open('%s/s3.fasta' % temp_output_dir).read(),\r\n \">s3_25\\nAAACCC\\n\")\r\n # confirm number of files is as expected\r\n self.assertEqual(len(glob('%s/*' % temp_output_dir)), 3)","def merge_walkupseq_files(latest_tsca_id):\n paths = glob.glob('walkupseq_files/*sample_info*')\n\n dfs = []\n for f in paths:\n tmp = pd.read_table(f, encoding='latin1')\n dfs.append(tmp)\n\n df = pd.concat(dfs, axis=0)\n df.to_csv('walkupseq_files/walkupseq_all_combined_%s.txt'%latest_tsca_id, sep=\"\\t\", index=None)\n return df","def make_all(self):\n # General matrices #\n self.tsv_seq_to_concepts()\n self.tsv_seq_to_names()\n self.list_sequence_concept()\n # Only in the with 'samples' case #\n if self.a.abundances: self.tsv_samples_to_names()\n if self.a.abundances: self.biom_output()\n # Graphical outputs #\n self.per_seq_dot_files()\n if self.a.abundances: self.per_sample_dot_files()","def get_result_files(self):\n name_pattern = \"{mapper}.{ngs_library.name}\"\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"out\", name_pattern + \"{ext}\"), ext=EXT_VALUES\n )\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"log\", \"{mapper}.{ngs_library.name}.{ext}\"),\n ext=(\n \"log\",\n \"conda_info.txt\",\n \"conda_list.txt\",\n \"log.md5\",\n \"conda_info.txt.md5\",\n \"conda_list.txt.md5\",\n ),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.{report}.txt\"\n ),\n report=(\"bamstats\", \"flagstats\", \"idxstats\"),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.{report}.txt.md5\"\n ),\n report=(\"bamstats\", \"flagstats\", \"idxstats\"),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.bamstats.html\"\n )\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.bamstats.html.md5\"\n )\n )\n\n for sheet in self.shortcut_sheets:\n for ngs_library in sheet.all_ngs_libraries:\n if ngs_library.name in self.ngs_library_to_kit:\n extraction_type = ngs_library.test_sample.extra_infos[\"extractionType\"]\n suffix = (\n \"_long\"\n if ngs_library.extra_infos[\"seqPlatform\"] in (\"PacBio\", \"ONP\")\n else \"\"\n )\n # Per-sample target coverage report.\n yield from expand(\n os.path.join(\n \"output\", name_pattern, \"report\", \"cov_qc\", name_pattern + \".{ext}\"\n ),\n mapper=self.config[\"tools\"][extraction_type.lower() + suffix],\n ngs_library=[ngs_library],\n ext=[\"txt\", \"txt.md5\"],\n )\n yield \"output/target_cov_report/out/target_cov_report.txt\"\n yield \"output/target_cov_report/out/target_cov_report.txt.md5\"\n if (\n self.config[\"picard_hs_metrics\"][\"path_targets_interval_list\"]\n and self.config[\"picard_hs_metrics\"][\"path_baits_interval_list\"]\n ):\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"picard_hs_metrics\", name_pattern + \".txt\"\n )\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"picard_hs_metrics\", name_pattern + \".txt.md5\"\n )\n )\n if self.config[\"compute_coverage_bed\"]:\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"report\", \"coverage\", name_pattern + \"{ext}\"),\n ext=(\".bed.gz\", \".bed.gz.tbi\"),\n )\n else:\n print(\n \"Genome-wide coverage BED generation disabled\", file=sys.stderr\n ) # pragma: no cover"],"string":"[\n \"def GenotypeGVCFs():\\n #creates sbatch files to merge batches of batch_size genomics vcf\\n cwd = os.getcwd()\\n sbatch_files = []\\n if not os.path.isdir(os.path.join(cwd, \\\"01_CombineGVCFs\\\")):\\n sys.exit(\\\"Directory 01_CombineGVCFs does not exits exists, something went wrong here.\\\")\\n if os.path.isdir(os.path.join(cwd, \\\"02_GenotypeGVCFs\\\")):\\n print \\\"WARNING: 02_GenotypeGVCFs already present, assuming this step has been completed with success.\\\"\\n return sbatch_files\\n else:\\n #create the folder structure\\n os.mkdir(os.path.join(cwd, \\\"02_GenotypeGVCFs\\\"))\\n os.mkdir(os.path.join(cwd, \\\"02_GenotypeGVCFs\\\", \\\"sbatch\\\"))\\n os.mkdir(os.path.join(cwd, \\\"02_GenotypeGVCFs\\\", \\\"std_err\\\"))\\n os.mkdir(os.path.join(cwd, \\\"02_GenotypeGVCFs\\\", \\\"std_out\\\"))\\n os.mkdir(os.path.join(cwd, \\\"02_GenotypeGVCFs\\\", \\\"VCF\\\"))\\n #Build the sbatch files for the join calling step\\n working_dir = os.path.join(cwd, \\\"02_GenotypeGVCFs\\\")\\n #now retrive the VCF stored in 01_CombineGVCFs/VCF/\\n combined_gvcfs_to_process = []\\n if len(CONFIG[\\\"intervals_list\\\"]) == 0:\\n #no intervals, I have one file for each batch\\n combined_gvcf_files = []\\n for current_batch in range(1, CONFIG[\\\"batch_number\\\"] +1):\\n # for each batch create the vcf file that need to be created by combine step\\n combined_gvcf_name = \\\"{}_batch{}.g.vcf.gz\\\".format(CONFIG[\\\"output_header\\\"], current_batch)\\n combined_gvcf_full_path = os.path.join(cwd, \\\"01_CombineGVCFs\\\", \\\"VCF\\\", combined_gvcf_name)\\n combined_gvcf_files.append(combined_gvcf_full_path)\\n combined_gvcfs_to_process.append(combined_gvcf_files)\\n else:\\n for interval in CONFIG[\\\"intervals_list\\\"]:\\n interval_name = os.path.basename(interval).split(\\\".\\\")[0]\\n combined_gvcf_files = []\\n for current_batch in range(1, CONFIG[\\\"batch_number\\\"] +1):\\n # for each batch create the vcf file that need to be created by combine step\\n combined_gvcf_name = \\\"{}_batch{}_{}.g.vcf.gz\\\".format(CONFIG[\\\"output_header\\\"], current_batch, interval_name)\\n combined_gvcf_full_path = os.path.join(cwd, \\\"01_CombineGVCFs\\\", \\\"VCF\\\", combined_gvcf_name)\\n combined_gvcf_files.append(combined_gvcf_full_path)\\n #now ceate a list with interval file and all gvcf to be combines\\n interval_plus_gvcfs = [interval ,combined_gvcf_files]\\n combined_gvcfs_to_process.append(interval_plus_gvcfs)\\n for interval_plus_gvcfs in combined_gvcfs_to_process:\\n interval = interval_plus_gvcfs[0]\\n combined_gvcf_files = interval_plus_gvcfs[1]\\n sbatch_file = build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, CONFIG[\\\"scratch\\\"], interval)\\n sbatch_files.append(sbatch_file)\\n return sbatch_files\",\n \"def build_batches(self):\\n\\n # get directories for all batches and logs\\n batches_dir = join(self.path, 'batches')\\n logs_dir = join(self.path, 'log')\\n\\n # create index file for batches\\n index_path = join(batches_dir, 'index.txt')\\n index = open(index_path, 'w')\\n\\n # write file containing simulation paths for each batch\\n for i, simulation_path in self.simulation_paths.items():\\n\\n # determine batch ID\\n batch_id = i // self.batch_size\\n\\n # process new batch\\n if i % self.batch_size == 0:\\n\\n # open batch file and append to index\\n batch_path = join(batches_dir, '{:d}.txt'.format(batch_id))\\n index.write('{:s}\\\\n'.format(relpath(batch_path, self.path)))\\n batch_file = open(batch_path, 'w')\\n\\n # create log directory for batch\\n mkdir(join(logs_dir, '{:d}'.format(batch_id)))\\n\\n # write paths to batch file\\n batch_file.write('{:s}\\\\n'.format(simulation_path))\\n\\n # close batch file\\n if i % self.batch_size == (self.batch_size - 1):\\n batch_file.close()\\n chmod(batch_path, 0o755)\\n\\n index.close()\\n\\n chmod(index_path, 0o755)\",\n \"def build(self):\\n writer = None\\n out_complete = ''\\n\\n reader = self.module_loader.get_reader()()\\n reader.set_configs(self.configs)\\n reader.verify_parameters()\\n input_path = self.configs.get_resolved('parameters', 'input', 'path')\\n output_path = self.configs.get_resolved('parameters', 'output', 'path')\\n pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)\\n \\n files = reader.fetch_input_files(input_path)\\n\\n for i, group in enumerate(files):\\n\\n obs = self.params('output_block_size') if self.configs.exists('parameters', 'output_block_size') else 1\\n if obs is None:\\n obs = len(files)\\n\\n first_of_batch = (i % obs == 0)\\n\\n if first_of_batch:\\n output_name = reader.output_filename(group['id'])\\n writer = self.module_loader.get_writer()(output_path, output_name)\\n out_complete = writer.file_path()\\n\\n Logger.log('started_r_files', group['files'])\\n\\n with writer.appending(not first_of_batch) as dataset:\\n Logger.log('writing_file', out_complete, '' if first_of_batch else '(appending)')\\n \\n self.read_attributes(dataset)\\n self.read_variables(dataset)\\n \\n if reader.data_grouping:\\n complete_path = tuple([path.join(input_path, f) for f in group['files']])\\n else:\\n complete_path = path.join(input_path, group['files'])\\n reader.read_to(dataset, complete_path, self.configs, not first_of_batch)\\n\\n Logger.info('done')\",\n \"def _fillBatches(self):\\n\\n batchRE = r\\\"\\\"\\\"\\n B\\n (?P<observebatch>\\\\d+?)\\n (?P<startend>[SE])\\n (?P<sequence>\\\\d+?)\\n _SR\\n (?:_(?P<extraInjections>\\\\d+?|\\\\w+?))?\\n $\\n \\\"\\\"\\\"\\n batchRE = re.compile(batchRE, re.VERBOSE)\\n # We canot infer batches unless we have runorder\\n if 'Run Order' in self.sampleMetadata.keys():\\n currentBatch = 0\\n # Loop over samples in run order\\n for index, row in self.sampleMetadata.sort_values(by='Run Order').iterrows():\\n nameComponents = batchRE.search(row['Sample File Name'])\\n if nameComponents:\\n # Batch start\\n if nameComponents.group('startend') == 'S':\\n # New batch - increment batch no\\n if nameComponents.group('sequence') == '1':\\n currentBatch = currentBatch + 1\\n\\n # Don't include the dilution series or blanks\\n if not ((row['AssayRole'] == AssayRole.LinearityReference) or (row['SampleType'] == SampleType.ProceduralBlank)):\\n self.sampleMetadata.loc[index, 'Batch'] = currentBatch\\n self.sampleMetadata.loc[index, 'Correction Batch'] = currentBatch\\n\\n else:\\n warnings.warn('Unable to infer batches without run order, skipping.')\\n return\",\n \"def run(self):\\n lineage_csv_gz = self.input_files_local[0][0]\\n output_db = self.output_files_local()[0]\\n log.write(f\\\"input: {lineage_csv_gz} output: {output_db}\\\")\\n\\n with IdSeqDictForUpdate(output_db, IdSeqDictValue.VALUE_TYPE_ARRAY) as lineage_dict:\\n batch_list = {}\\n with gzip.open(lineage_csv_gz, \\\"rt\\\") as gzf:\\n for line in gzf:\\n fields = line.rstrip().split(\\\",\\\")\\n taxid = fields[0]\\n species, genus, family = fields[-1:-4:-1]\\n batch_list[taxid] = [species, genus, family]\\n if len(batch_list) >= BATCH_INSERT_SIZE:\\n lineage_dict.batch_inserts(batch_list.items())\\n batch_list = {}\\n lineage_dict.batch_inserts(batch_list.items())\",\n \"def createScript_sbatch(self):\\n tools_createScript_sbatch(\\n sbatch_script_file_name = self.sbatchFile_addMEM,\\n executable = self.executable_addMEM,\\n command_line_parameters = self.cfgFiles_addMEM_modified,\\n input_file_names = self.inputFiles,\\n output_file_names = self.outputFiles,\\n script_file_names = self.shFiles_addMEM_modified,\\n log_file_names = self.logFiles_addMEM,\\n keep_logs = False,\\n working_dir = self.workingDir,\\n max_num_jobs = 100000000, # it's really silly to limit the number of jobs; use an enormous number as the ,,fix''\\n cvmfs_error_log = self.cvmfs_error_log,\\n pool_id = self.pool_id,\\n use_home = self.use_home,\\n validate_outputs = self.check_output_files,\\n max_num_submittedJobs = 2000,\\n )\",\n \"def gen_vars(input_fqs):\\n k_sizes = range(*CONFIG['abyss_bloom']['k_mer_sizes'])\\n sr = re.search(PATH_RE, input_fqs[0])\\n sr2 = re.search(PATH_RE, input_fqs[1])\\n # should be of conventional directory hierarchy\\n try:\\n assert sr.groups() == sr2.groups()\\n except AssertionError:\\n print '{0} != {1}'.format(sr.groups(), sr2.groups())\\n raise\\n\\n bfs, bf_flags, fas, fa_flags = [], [], [], []\\n for k_size in k_sizes:\\n # for abyss_bloom\\n # bn: basename\\n bf_bn = '{0}_k{1}.bf.gz'.format(sr.group('celltype'), k_size)\\n bf_flag_bn = '{0}.SUCCESS'.format(bf_bn)\\n bf_dir = os.path.join(sr.group('prefix'), 'kon', sr.group('chr'), 'bf')\\n bf = os.path.join(bf_dir, bf_bn)\\n bf_flag = os.path.join(bf_dir, bf_flag_bn)\\n bfs.append(bf)\\n bf_flags.append(bf_flag)\\n\\n # for konnector\\n fa_all_bn = '{0}_k{1}_allpaths.fa.gz'.format(sr.group('celltype'), k_size)\\n fa_mer_bn = '{0}_k{1}_merged.fa.gz'.format(sr.group('celltype'), k_size)\\n fa_flag_bn = '{0}_k{1}.SUCCESS'.format(sr.group('celltype'), k_size)\\n fa_dir = os.path.join(sr.group('prefix'), 'kon', sr.group('chr'), 'fafq')\\n fa_all = os.path.join(fa_dir, fa_all_bn)\\n fa_mer = os.path.join(fa_dir, fa_mer_bn)\\n fa_flag = os.path.join(fa_dir, fa_flag_bn)\\n fas.extend([fa_all, fa_mer])\\n fa_flags.append(fa_flag)\\n\\n return k_sizes, bfs, bf_flags, fas, fa_flags\",\n \"def batch_process(in_batch, in_ref, merge_stats=True, dir_counts='', dir_stats='',\\r\\n in_counts=None, in_stats=None, save='all', out_folder='',\\r\\n out_prefix='', return_df=None):\\r\\n\\r\\n # import ref files and define variables/paths\\r\\n path = Path.cwd()\\r\\n df_ref = pd.read_csv(in_ref)\\r\\n if 'sgRNA_seq' not in df_ref.columns.tolist():\\r\\n raise Exception('in_ref is missing column: sgRNA_seq')\\r\\n df_batch = pd.read_csv(in_batch)\\r\\n list_reqcols = ['sample_id', 'fastq_file', 'condition']\\r\\n list_batchcols = df_batch.columns.tolist()\\r\\n if not all(col in list_batchcols for col in list_reqcols):\\r\\n list_miss = [col for col in list_reqcols if col not in list_batchcols]\\r\\n raise Exception('Error! in_batch is missing column(s): ' + str(list_miss))\\r\\n if 't0' not in df_batch['condition'].tolist():\\r\\n raise Exception('t0 condition not found in the in_batch file')\\r\\n # defaults to cwd if subdir == ''\\r\\n counts_path = path / dir_counts\\r\\n stats_path = path / dir_stats\\r\\n if in_counts is None:\\r\\n df_batch['counts_files'] = df_batch['sample_id'] + '_counts.csv'\\r\\n else:\\r\\n df_temp = pd.DataFrame(in_counts, columns=['sample_id', 'counts_files'])\\r\\n df_batch = df_batch.merge(df_temp, on='sample_id', how='left')\\r\\n if in_stats is None:\\r\\n df_batch['stats_files'] = df_batch['sample_id'] + '_stats.txt'\\r\\n else:\\r\\n df_temp = pd.DataFrame(in_stats, columns=['sample_id', 'stats_files'])\\r\\n df_batch = df_batch.merge(df_temp, on='sample_id', how='left')\\r\\n\\r\\n # import csv files and generate dfs for raw reads and log2 norm\\r\\n df_reads, df_log2 = df_ref.copy(), df_ref.copy()\\r\\n for row in df_batch.itertuples():\\r\\n file = counts_path / row.counts_files\\r\\n df_temp = pd.read_csv(file, names=['sgRNA_seq', row.sample_id])\\r\\n # merge on sgRNA_seq to aggregate columns\\r\\n df_reads = pd.merge(df_reads, df_temp, on='sgRNA_seq')\\r\\n # perform log2 normalization (brian/broad method)\\r\\n total_reads = df_reads[row.sample_id].sum()\\r\\n df_log2[row.sample_id] = df_reads[row.sample_id].apply(lambda x: np.log2((x * 1000000 / total_reads) + 1))\\r\\n\\r\\n # perform t0 normalization\\r\\n df_t0 = df_ref.copy()\\r\\n t0 = df_batch.loc[df_batch['condition'] == 't0']['sample_id']\\r\\n if t0.shape[0] != 1:\\r\\n raise Exception('Only a single t0 sample is allowed')\\r\\n t0 = t0[0]\\r\\n for row in df_batch.itertuples():\\r\\n df_t0[row.sample_id] = df_log2[row.sample_id].sub(df_log2[t0])\\r\\n df_t0.drop(columns=t0, inplace=True) # drop the t0 col\\r\\n\\r\\n # average replicates by condition\\r\\n list_conds = df_batch['condition'].unique().tolist()\\r\\n list_conds.remove('t0')\\r\\n df_conds = df_ref.copy()\\r\\n for cond in list_conds:\\r\\n reps = df_batch.loc[df_batch['condition'] == cond]['sample_id'].tolist()\\r\\n if len(reps) > 1:\\r\\n df_conds[cond] = df_t0[reps].mean(axis=1)\\r\\n elif len(reps) == 1:\\r\\n df_conds[cond] = df_t0[reps]\\r\\n else:\\r\\n raise Exception('Error! Invalid number of replicates')\\r\\n\\r\\n # merge statistics files\\r\\n if merge_stats:\\r\\n df_stats = pd.DataFrame(columns=['parameters'])\\r\\n for row in df_batch.itertuples():\\r\\n file = stats_path / row.stats_files\\r\\n df_temp = pd.read_csv(file, sep=': ', engine='python', names=['parameters', row.sample_id])\\r\\n df_stats = pd.merge(df_stats, df_temp, on='parameters', how='outer')\\r\\n\\r\\n # export files and return dataframes if necessary\\r\\n outpath = path / out_folder\\r\\n Path.mkdir(outpath, exist_ok=True)\\r\\n # dictionary to map kws to dfs and output file names\\r\\n dict_df = {'reads': (df_reads, out_prefix + 'reads.csv'),\\r\\n 'log2': (df_log2, out_prefix + 'log2.csv'),\\r\\n 't0': (df_t0, out_prefix + 't0_reps.csv'),\\r\\n 'conds': (df_conds, out_prefix + 't0_conds.csv')}\\r\\n if merge_stats:\\r\\n dict_df.update({'stats': (df_stats, out_prefix + 'stats.csv')})\\r\\n # determine which files to export\\r\\n if save == 'all':\\r\\n save = ['reads','log2','t0', 'conds', 'stats']\\r\\n if isinstance(save, list):\\r\\n for key in save:\\r\\n dict_df[key][0].to_csv(outpath / dict_df[key][1], index=False)\\r\\n elif save is None:\\r\\n pass\\r\\n else:\\r\\n warnings.warn('Invalid value for save. No files exported')\\r\\n # determine df to return\\r\\n print('Batch processing completed')\\r\\n if return_df in dict_df.keys():\\r\\n return dict_df[return_df][0]\\r\\n elif return_df is None:\\r\\n return\\r\\n else:\\r\\n print('Invalid value for return_df. No dataframe returned')\\r\\n return\",\n \"def run_concat_vcfs(job, context, vcf_ids, tbi_ids):\\n\\n work_dir = job.fileStore.getLocalTempDir()\\n\\n vcf_names = ['chrom_{}.vcf.gz'.format(i) for i in range(len(vcf_ids))]\\n out_name = 'genome.vcf.gz'\\n\\n for vcf_id, tbi_id, vcf_name in zip(vcf_ids, tbi_ids, vcf_names):\\n job.fileStore.readGlobalFile(vcf_id, os.path.join(work_dir, vcf_name))\\n job.fileStore.readGlobalFile(tbi_id, os.path.join(work_dir, vcf_name + '.tbi'))\\n\\n cmd = ['bcftools', 'concat'] + [vcf_name for vcf_name in vcf_names] + ['-O', 'z']\\n \\n with open(os.path.join(work_dir, out_name), 'wb') as out_file:\\n context.runner.call(job, cmd, work_dir=work_dir, outfile = out_file)\\n\\n cmd = ['tabix', '-f', '-p', 'vcf', out_name]\\n context.runner.call(job, cmd, work_dir=work_dir)\\n\\n out_vcf_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name))\\n out_tbi_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name + '.tbi'))\\n\\n return out_vcf_id, out_tbi_id\",\n \"def build_index(self):\\n # format output and input\\n ref_file = f'{self.genome_database}BSB_ref.fa'\\n # collect external command\\n index_command = [f'{self.bwa_path}', 'index', '-a', 'bwtsw', '-b', f'{self.block_size}', ref_file]\\n # run external command\\n subprocess.run(args=index_command)\",\n \"def add_batch(batch_index, pCS, orphans, fasta_d, cpus, dun_use_partial):\\n cur_file = \\\"batch{0}.fasta\\\".format(batch_index)\\n seqids = set([r.id for r in SeqIO.parse(open(cur_file), 'fasta')])\\n o = ar.run_minimap(cur_file, \\\"seed{0}.S.fasta\\\".format(batch_index), cpus=cpus)\\n print >> sys.stderr, \\\"processing\\\", o\\n pCS, remains = sp.process_align_to_pCS(o, seqids, pCS, MiniReader, dun_use_partial=dun_use_partial)\\n print >> sys.stderr, \\\"pCS: {0}, tucked: {1}, orphans: {2}, remains: {3}\\\".format( \\\\\\n len(pCS.S), sum(v == 'T' for v in pCS.seq_stat.itervalues()), len(orphans), len(remains))\\n # write batch<i>.remains.fasta\\n cur_file = \\\"batch{0}.remains.fasta\\\".format(batch_index)\\n FileIO.write_seqids_to_fasta(remains, cur_file, fasta_d)\\n o = ar.run_minimap(cur_file, \\\"seed{0}.orphans.fasta\\\".format(batch_index), cpus=cpus)\\n print >> sys.stderr, \\\"processing\\\", o\\n pCS, orphans, remains = sp.process_align_to_orphan(o, remains, orphans, pCS, MiniReader, dun_use_partial=dun_use_partial)\\n print >> sys.stderr, \\\"pCS: {0}, tucked: {1}, orphans: {2}, remains: {3}\\\".format( \\\\\\n len(pCS.S), sum(v == 'T' for v in pCS.seq_stat.itervalues()), len(orphans), len(remains))\\n # write batch<i>.remains2.fasta and self align\\n cur_file = \\\"batch{0}.remains2.fasta\\\".format(batch_index)\\n FileIO.write_seqids_to_fasta(remains, cur_file, fasta_d)\\n o = ar.run_minimap(cur_file, cur_file, cpus=cpus)\\n print >> sys.stderr, \\\"processing\\\", o\\n pCS, remains = sp.process_self_align_into_seed(o, remains, MiniReader, pCS, dun_use_partial=dun_use_partial)\\n print >> sys.stderr, \\\"pCS: {0}, tucked: {1}, orphans: {2}, remains: {3}\\\".format( \\\\\\n len(pCS.S), sum(v == 'T' for v in pCS.seq_stat.itervalues()), len(orphans), len(remains))\\n # combine remains+orphans to new orphans\\n orphans = orphans.union(remains)\\n FileIO.write_preClusterSet_to_fasta(pCS, \\\"seed{0}.S.fasta\\\".format(batch_index+1), fasta_d)\\n FileIO.write_seqids_to_fasta(orphans, \\\"seed{0}.orphans.fasta\\\".format(batch_index+1), fasta_d)\\n\\n return pCS, orphans\",\n \"def build(self, ref_path, reffile_template):\\n SMALL_INDEX_MAX_SIZE = 4 * 1024**3 - 200 # From bowtie2-build wrapper\\n assert os.stat(ref_path).st_size <= SMALL_INDEX_MAX_SIZE\\n self.check_logger()\\n for line in self.yield_output(['--wrapper',\\n 'micall-0',\\n '--quiet',\\n '-f',\\n ref_path,\\n reffile_template],\\n stderr=subprocess.STDOUT):\\n if line != 'Building a SMALL index\\\\n':\\n self.logger.debug(line)\",\n \"def prepareBatchFile(self,detector,exposureTime):\\n \\n inp = open(ini.Ini().getParTestFile(\\\"BEST\\\",\\\"best_batch_file_template\\\"), 'r')\\n t = Template(inp.read())\\n s = t.substitute(besthome=ini.Ini().getPar(\\\"BEST\\\",\\\"besthome\\\"),\\n bestbin=ini.Ini().getPar(\\\"BEST\\\",\\\"best_bin\\\"),\\n detector=detector,exposure_time=exposureTime,folder=self.runFolder)\\n \\n self.completePath = os.path.join(self.runFolder,ini.Ini().getPar(\\\"BEST\\\",\\\"best_batch_file\\\") )\\n outp = open(self.completePath, 'w')\\n outp.write(s)\\n outp.close()\\n # give execute permissions\\n #os.chmod(self.completePath, 0755)\\n self.make_exe(self.completePath) \\n self.log.logger.debug(\\\"Batch best file created: \\\" + self.completePath)\\n \\n return self.completePath\",\n \"def cuffmerge(job, config, name, samples, manifest):\\n\\n stats_root = \\\"{}_cuffmerge_stats\\\".format(config['run_id'])\\n logfile = \\\"{}.cuffmerge.log\\\".format(config['run_id'])\\n\\n command = [\\\"{}\\\".format(config['cuffmerge']['bin']),\\n \\\"-g {}\\\".format(config['transcript_reference']),\\n \\\"-s {}\\\".format(config['reference']),\\n \\\"-p {}\\\".format(config['cuffmerge']['num_cores']),\\n \\\"{}\\\".format(manifest)]\\n\\n job.fileStore.logToMaster(\\\"Cuffmerge Command: {}\\\\n\\\".format(command))\\n pipeline.run_and_log_command(\\\" \\\".join(command), logfile)\\n\\n pwd = os.getcwd()\\n config['merged_transcript_reference'] = os.path.join(pwd, \\\"merged.gtf\\\")\\n\\n return stats_root\",\n \"def batch(self, coeff_count=13, db=False):\\n mfccs, _ = self.mfcc(coeff_count)\\n if db:\\n mfccs = utils.dbspec(mfccs)\\n delta1, delta2 = self.delta_coeffs(mfccs)\\n self._annotate(mfccs)\\n\\n mfccs_len = mfccs.shape[1]\\n batch_x = np.concatenate((mfccs, delta1, delta2), axis=0).transpose()\\n batch_y = np.array(self.annotated_samples)\\n print(\\\"AudioClip--Generated Batch\\\")\\n return (batch_x, batch_y)\",\n \"def generate_batch(self):\\n\\n # sbatch = list()\\n # tbatch = list()\\n # for i in range(self.dict_paras['batch_size']):\\n # sbatch.append(self.lst_triplet_train_map[self.data_index])\\n # self.data_index = (self.data_index + 1) % self.triplet_train_size\\n\\n sbatch = random.sample(self.lst_triplet_train_map, self.dict_paras['batch_size'])\\n tbatch = list()\\n\\n for ele in sbatch:\\n corrupted1, corrupted2 = self.get_corrupted_triplet(ele)\\n tbatch.append((ele, corrupted1))\\n tbatch.append((ele, corrupted2))\\n return tbatch\",\n \"def run(self, dataset_size=4, n_jobs=-1, starting_block=0):\\n data_files = sorted(self.input_directory.glob(\\\"**/*.txt\\\"))\\n log.info(f\\\"Creating shape file based on {len(data_files)} samples.\\\")\\n\\n n_blocks = int(len(data_files) / dataset_size)\\n data_file_blocks = split(data_files, n_blocks)\\n dataset_blocks_ids = np.arange(len(data_file_blocks))\\n\\n if starting_block != 0:\\n data_file_blocks = data_file_blocks[starting_block:]\\n dataset_blocks_ids = dataset_blocks_ids[starting_block:]\\n log.info(f\\\"Starting at a different block number: {starting_block}.\\\")\\n n_blocks = int(len(data_file_blocks))\\n\\n log.info(f\\\"Going through {n_blocks} blocks in parallel.\\\")\\n Parallel(n_jobs=n_jobs)(\\n delayed(self.generate_single_block)(data_file_block, dataset_block_id)\\n for (data_file_block, dataset_block_id) in tqdm(\\n zip(data_file_blocks, dataset_blocks_ids)\\n )\\n )\\n\\n log.info(\\\"Combining the separate index files..\\\")\\n index_floorplan = sorted(self.output_directory.glob(\\\"index_floorplans_*.csv\\\"))\\n log.info(f\\\"Found {len(index_floorplan)} index block files.\\\")\\n index_files = pd.concat([pd.read_csv(_file) for _file in index_floorplan])\\n index_files = index_files.fillna(0)\\n index_files.to_csv(self.output_directory / \\\"index_floorplans.csv\\\", index=False)\",\n \"def _cmd_batch(args):\\n logging.info(\\\"CNVkit %s\\\", __version__)\\n # Validate/restrict options, beyond what argparse mutual exclusion can do\\n bad_args_msg = \\\"\\\"\\n if args.reference:\\n bad_flags = [\\n flag\\n for is_used, flag in (\\n (args.normal is not None, \\\"-n/--normal\\\"),\\n (args.fasta, \\\"-f/--fasta\\\"),\\n (args.targets, \\\"-t/--targets\\\"),\\n (args.antitargets, \\\"-a/--antitargets\\\"),\\n (args.access, \\\"-g/--access\\\"),\\n (args.annotate, \\\"--annotate\\\"),\\n (args.short_names, \\\"--short-names\\\"),\\n (args.target_avg_size, \\\"--target-avg-size\\\"),\\n (args.antitarget_avg_size, \\\"--antitarget-avg-size\\\"),\\n (args.antitarget_min_size, \\\"--antitarget-min-size\\\"),\\n )\\n if is_used\\n ]\\n if bad_flags:\\n bad_args_msg = (\\n \\\"If -r/--reference is given, options to construct \\\"\\n \\\"a new reference (%s) should not be used.\\\" % \\\", \\\".join(bad_flags)\\n )\\n elif args.normal is None:\\n bad_args_msg = (\\n \\\"Option -n/--normal must be given to build a new \\\"\\n \\\"reference if -r/--reference is not used.\\\"\\n )\\n elif args.seq_method in (\\\"hybrid\\\", \\\"amplicon\\\") and not args.targets:\\n bad_args_msg = (\\n \\\"For the '%r' sequencing method, option -t/--targets \\\"\\n \\\"(at least) must be given to build a new reference if \\\"\\n \\\"-r/--reference is not used.\\\" % args.seq_method\\n )\\n if bad_args_msg:\\n sys.exit(bad_args_msg + \\\"\\\\n(See: cnvkit.py batch -h)\\\")\\n\\n # Ensure sample IDs are unique to avoid overwriting outputs\\n seen_sids = {}\\n for fname in (args.bam_files or []) + (args.normal or []):\\n sid = core.fbase(fname)\\n if sid in seen_sids:\\n sys.exit(f\\\"Duplicate sample ID {sid!r} (from {fname} and {seen_sids[sid]})\\\")\\n seen_sids[sid] = fname\\n\\n if args.processes < 1:\\n args.processes = multiprocessing.cpu_count()\\n\\n if not args.reference:\\n # Build a copy number reference; update (anti)targets upon request\\n args.reference, args.targets, args.antitargets = batch.batch_make_reference(\\n args.normal,\\n args.targets,\\n args.antitargets,\\n args.male_reference,\\n args.diploid_parx_genome,\\n args.fasta,\\n args.annotate,\\n args.short_names,\\n args.target_avg_size,\\n args.access,\\n args.antitarget_avg_size,\\n args.antitarget_min_size,\\n args.output_reference,\\n args.output_dir,\\n args.processes,\\n args.count_reads,\\n args.seq_method,\\n args.cluster,\\n )\\n elif args.targets is None and args.antitargets is None:\\n # Extract (anti)target BEDs from the given, existing CN reference\\n ref_arr = read_cna(args.reference)\\n targets, antitargets = reference.reference2regions(ref_arr)\\n ref_pfx = os.path.join(args.output_dir, core.fbase(args.reference))\\n args.targets = ref_pfx + \\\".target-tmp.bed\\\"\\n args.antitargets = ref_pfx + \\\".antitarget-tmp.bed\\\"\\n tabio.write(targets, args.targets, \\\"bed4\\\")\\n tabio.write(antitargets, args.antitargets, \\\"bed4\\\")\\n\\n if args.bam_files:\\n if args.processes == 1:\\n procs_per_bam = 1\\n logging.info(\\\"Running %d samples in serial\\\", len(args.bam_files))\\n else:\\n procs_per_bam = max(1, args.processes // len(args.bam_files))\\n logging.info(\\n \\\"Running %d samples in %d processes (that's %d processes per bam)\\\",\\n len(args.bam_files),\\n args.processes,\\n procs_per_bam,\\n )\\n\\n with parallel.pick_pool(args.processes) as pool:\\n for bam in args.bam_files:\\n pool.submit(\\n batch.batch_run_sample,\\n bam,\\n args.targets,\\n args.antitargets,\\n args.reference,\\n args.output_dir,\\n args.male_reference,\\n args.diploid_parx_genome,\\n args.scatter,\\n args.diagram,\\n args.rscript_path,\\n args.count_reads,\\n args.drop_low_coverage,\\n args.seq_method,\\n args.segment_method,\\n procs_per_bam,\\n args.cluster,\\n args.fasta,\\n args.diploid_parx_genome,\\n )\\n else:\\n logging.info(\\n \\\"No tumor/test samples (but %d normal/control samples) \\\"\\n \\\"specified on the command line.\\\",\\n len(args.normal),\\n )\",\n \"def skesa_assemble(self):\\n with progressbar(self.metadata) as bar:\\n for sample in bar:\\n # Initialise the assembly command\\n sample.commands.assemble = str()\\n try:\\n if sample.general.trimmedcorrectedfastqfiles:\\n # If the sample is a pure isolate, assemble it. Otherwise, run the pre-metagenome pipeline\\n try:\\n status = sample.run.Description\\n except AttributeError:\\n status = 'unknown'\\n if status == 'metagenome':\\n self.merge(sample)\\n else:\\n # Set the output directory\\n sample.general.assembly_output = os.path.join(sample.general.outputdirectory,\\n 'assembly_output')\\n make_path(sample.general.assembly_output)\\n sample.general.assemblyfile = os.path.join(sample.general.assembly_output,\\n '{name}_unfiltered.fasta'\\n .format(name=sample.name))\\n sample.general.bestassemblyfile = os.path.join(sample.general.assembly_output,\\n '{name}.fasta'\\n .format(name=sample.name))\\n fastqfiles = sample.general.trimmedcorrectedfastqfiles\\n\\n # Set the the forward fastq files\\n sample.general.assemblyfastq = fastqfiles\\n forward = fastqfiles[0]\\n gz = True if '.gz' in forward else False\\n # If there are two fastq files\\n if len(fastqfiles) == 2:\\n # Set the reverse fastq name https://github.com/ncbi/SKESA/issues/7\\n sample.commands.assemble = 'skesa --fastq {fastqfiles} --cores {threads} ' \\\\\\n '--use_paired_ends --vector_percent 1 ' \\\\\\n '--contigs_out {contigs}'\\\\\\n .format(fastqfiles=','.join(fastqfiles),\\n threads=self.cpus,\\n contigs=sample.general.assemblyfile)\\n # Same as above, but use single read settings for the assembler\\n else:\\n sample.commands.assemble = 'skesa --fastq {fastqfiles} --cores {threads} ' \\\\\\n '--vector_percent 1 --contigs_out {contigs}'\\\\\\n .format(fastqfiles=','.join(fastqfiles),\\n threads=self.cpus,\\n contigs=sample.general.assemblyfile)\\n # Specify that the files are gzipped\\n if gz:\\n sample.commands.assemble += ' --gz'\\n # If there are no fastq files, populate the metadata appropriately\\n else:\\n sample.general.assembly_output = 'NA'\\n sample.general.assemblyfastq = 'NA'\\n sample.general.bestassemblyfile = 'NA'\\n except AttributeError:\\n sample.general.assembly_output = 'NA'\\n sample.general.assemblyfastq = 'NA'\\n sample.general.trimmedcorrectedfastqfiles = 'NA'\\n sample.general.bestassemblyfile = 'NA'\\n if sample.commands.assemble and not os.path.isfile(sample.general.assemblyfile):\\n # Run the assembly\\n out, err = run_subprocess(sample.commands.assemble)\\n write_to_logfile(sample.commands.assemble,\\n sample.commands.assemble,\\n self.logfile,\\n sample.general.logout,\\n sample.general.logerr,\\n None,\\n None)\\n write_to_logfile(out,\\n err,\\n self.logfile,\\n sample.general.logout,\\n sample.general.logerr,\\n None,\\n None)\",\n \"def prepare_subset_vcf_files_by_population():\\n if not os.path.exists(VCF_BY_POPULATION_PATH):\\n print(\\\"preparing subset vcf by population\\\")\\n os.makedirs(VCF_BY_POPULATION_PATH)\\n sleep(10)\\n vcf_tools_runner = VCFToolsDockerRunner()\\n samples = glob(f\\\"{SAMPLES_FOLDER}/*.csv\\\")\\n\\n with tqdm(total=len(samples)) as pbar:\\n for sample in samples:\\n sample = sample.replace('\\\\\\\\', '/')\\n sample_name = get_filename_from_path(sample)\\n sample_path = \\\"/\\\".join([IMAGE_SHARE_FOLDER_PATH] + sample.split('/')[1:])\\n pbar.set_description(f\\\"Processing {sample_name}\\\")\\n vcf_tools_runner(\\n f\\\"vcf-subset -c {sample_path} \\\"\\n f\\\"{IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_VCF_FILE_NAME} | fill-an-ac > \\\"\\n f\\\"{IMAGE_SHARE_FOLDER_PATH}/{VCF_BY_POPULATION_FOLDER}/{sample_name}.vcf\\\")\\n pbar.update(1)\\n else:\\n print(f\\\"Subset VCF files by population already exist in: {VCF_BY_POPULATION_PATH}\\\")\",\n \"def seqff(self):\\r\\n\\r\\n start = time.time()\\r\\n\\r\\n # load bininfo\\r\\n bininfo = load_bininfo(self.bininfodata_loc)\\r\\n\\r\\n # load input files\\r\\n if os.path.isdir(self.input_loc):\\r\\n input_list = [self.input_loc + x for x in os.listdir(self.input_loc)]\\r\\n\\r\\n elif os.path.isfile(self.input_loc):\\r\\n input_list = [self.input_loc]\\r\\n\\r\\n else:\\r\\n raise FileNotFoundError(\\\"error occurred : inputData is not a Directory or File\\\")\\r\\n\\r\\n for i, file in enumerate(input_list):\\r\\n filetype = file.split(\\\".\\\")[-1]\\r\\n # filetype : 'sam' or 'bam' or 'newtemp'\\r\\n if 'sam' in filetype:\\r\\n bincount = load_sam(file)\\r\\n\\r\\n elif 'newtemp' in filetype:\\r\\n bincount = load_counts(file)\\r\\n file = file.replace(\\\".newtemp\\\", \\\"\\\") # TEMP .newtemp -> .bam\\r\\n\\r\\n elif 'bam' in filetype:\\r\\n bincount = load_bam(file)\\r\\n\\r\\n else:\\r\\n continue\\r\\n\\r\\n #CREATE newtemp file in \\\"output_loc\\\"/newtemp/\\r\\n create_newtemp(bincount, file, self.newtemp_loc)\\r\\n\\r\\n newtemp = pd.DataFrame.from_dict(bincount, orient='index')\\r\\n newtemp.reset_index(level=0, inplace=True)\\r\\n newtemp.rename(columns={'index': 'binName', 0: 'counts'}, inplace=True)\\r\\n\\r\\n temp_bininfo = bininfo.copy(deep=True)\\r\\n temp_bininfo = temp_bininfo.merge(newtemp, on='binName',\\r\\n how='left') # missing value : NaN, not NA in pandas\\r\\n temp_bininfo['counts'] = temp_bininfo['counts'].fillna(0)\\r\\n\\r\\n temp_bininfo.sort_values(by='binorder', inplace=True)\\r\\n temp_bininfo.reset_index(drop=True)\\r\\n\\r\\n ####DATA PROCESSING #######################\\r\\n autosomebinsonly = []\\r\\n for index in range(61927):\\r\\n boolean = (temp_bininfo['FRS'][index] != 'NA') and \\\\\\r\\n (float(temp_bininfo['GC'][index]) > 0.316) and \\\\\\r\\n (temp_bininfo['CHR'][index] != 'chrX') and \\\\\\r\\n (temp_bininfo['CHR'][index] != 'chrY')\\r\\n autosomebinsonly.append(boolean)\\r\\n autosomebinsonly = pd.Series(autosomebinsonly)\\r\\n\\r\\n alluseablebins = []\\r\\n for index in range(61927):\\r\\n boolean = (temp_bininfo['FRS'][index] != \\\"NA\\\") and (float(temp_bininfo['GC'][index]) > 0.316)\\r\\n alluseablebins.append(boolean)\\r\\n alluseablebins = pd.Series(alluseablebins)\\r\\n\\r\\n #CREATE alluseablebins file in \\\"output_loc\\\"/alluseablebins\\r\\n #create_alluseablebins(alluseablebins, file, self.alluseablebins_loc)\\r\\n\\r\\n sum_counts = pd.Series(temp_bininfo['counts'])\\r\\n sum_counts = sum_counts[autosomebinsonly].sum(skipna=True)\\r\\n\\r\\n autoscaledtemp = pd.Series(temp_bininfo['counts'].loc[(autosomebinsonly)],\\r\\n copy=True) / sum_counts # NA-related code removed\\r\\n allscaledtemp = pd.Series(temp_bininfo['counts'].loc[(alluseablebins)], copy=True) / sum_counts\\r\\n\\r\\n gc_index = {}\\r\\n cnt = 0\\r\\n for index, isauto in enumerate(autosomebinsonly):\\r\\n if isauto:\\r\\n if temp_bininfo['GC'].iat[index] in gc_index:\\r\\n gc_index[temp_bininfo['GC'].iat[index]].append(float(autoscaledtemp.iat[cnt]))\\r\\n cnt += 1\\r\\n\\r\\n else:\\r\\n gc_index[temp_bininfo['GC'].iat[index]] = [float(autoscaledtemp.iat[cnt])]\\r\\n cnt += 1\\r\\n\\r\\n key_list = []\\r\\n val_list = []\\r\\n for key, val in gc_index.items():\\r\\n key_list.append(key)\\r\\n val_list.append(np.median(val))\\r\\n\\r\\n loess_var = loess(key_list, val_list) # default span : 0.75\\r\\n loess_var.fit()\\r\\n # y = loess.loess_prediction(newData, loessVar)\\r\\n # temp_loessPredict.loess_debugging(loessVar)\\r\\n\\r\\n ###prediction###\\r\\n loess_x = [float(gc) for index, gc in enumerate(temp_bininfo['GC']) if (alluseablebins[index])]\\r\\n # print(temp_bininfo['GC'])\\r\\n loess_fitted = loess_var.predict(loess_x)\\r\\n loess_fitted = list(loess_fitted.values)\\r\\n # print(loess_fitted)\\r\\n\\r\\n median_autoscaledtemp = np.median(autoscaledtemp)\\r\\n median_autoscaledtemp = float(median_autoscaledtemp) # for fixed constant\\r\\n\\r\\n normalizedbincount = [(x + (median_autoscaledtemp - loess_fitted[index])) for index, x in\\r\\n enumerate(allscaledtemp)]\\r\\n\\r\\n #CREATE normalizedbincount in \\\"output_loc\\\"/normalizedbincount\\r\\n create_normalizedbincount(normalizedbincount, file, self.normalizedbincount_loc)\\r\\n\\r\\n bincounts = pd.Series(data=np.repeat(a=0.0, repeats=61927), index=temp_bininfo['binName'], dtype=np.float64)\\r\\n\\r\\n sum_normalizedbincount = sum([val for val in normalizedbincount if not math.isnan(val)])\\r\\n sum_normalizedbincount = float(sum_normalizedbincount) # deep copy temporarily\\r\\n\\r\\n cnt = 0\\r\\n for index, x in enumerate(alluseablebins):\\r\\n if x == True:\\r\\n data = (normalizedbincount[cnt] / sum_normalizedbincount) * len(normalizedbincount)\\r\\n bincounts.iat[index] = data\\r\\n cnt += 1\\r\\n\\r\\n #CREATE bincounts in \\\"output_loc\\\"/bincounts\\r\\n create_bincounts(bincounts, file, self.bincounts_loc)\\r\\n\\r\\n wrsc = self.prediction(bincounts, self.B, self.mu, self.parameter_1, self.parameter_2)\\r\\n enet = np.dot(bincounts, (self.elnetbeta)) + (self.elnetintercept)\\r\\n ff = (wrsc+enet) / 2\\r\\n\\r\\n result_lines = list()\\r\\n result_lines.append(\\\"SeqFF\\\\tEnet\\\\tWRSC\\\")\\r\\n result_lines.append(\\\"{}\\\\t{}\\\\t{}\\\".format(ff, enet, wrsc))\\r\\n\\r\\n #CREATE results of seqff (seqff paper result covered) in \\\"output_loc\\\"/results\\r\\n create_results(result_lines, file, self.results_loc)\\r\\n\\r\\n end = time.time()\\r\\n elapsed = end - start\\r\\n h = int(elapsed) // 3600\\r\\n m = (int(elapsed) - (h * 3600)) // 60\\r\\n s = (int(elapsed) % 60)\\r\\n print(\\\"elapsed time: %d hr %d min %d sec\\\" % (h, m, s))\\r\\n print(\\\"elapsed :\\\", elapsed)\\r\\n print(\\\"progress : {} / {}\\\".format(i + 1, self.progress))\",\n \"def __iter__(self):\\n\\n batch_sp = []\\n batch_noise = []\\n batch_mix = []\\n batch_count = 0\\n\\n while True:\\n\\n # Randomizing wav lists\\n random.shuffle(self._lst_spk_files)\\n random.shuffle(self._lst_noise_files)\\n\\n for spk_file, noise_file in zip(self._lst_spk_files, self._lst_noise_files):\\n\\n # Read wav files\\n sig_spk = self.__read_wav_file(spk_file)\\n sig_noise = self.__read_wav_file(noise_file)\\n\\n # Align signal\\n min_length = min(sig_spk.shape[0], sig_noise.shape[0])\\n\\n if min_length < self._fftsize:\\n raise Exception(\\\"ERROR: Too short signals in dataset\\\")\\n\\n sig_spk = sig_spk[:min_length]\\n sig_noise = sig_noise[:min_length]\\n\\n # Generate need SNR\\n need_snr = random.uniform(self._min_snr, self._max_snr)\\n\\n # Calc scaled signals\\n sig_spk, sig_noise = self.__mix_with_snr(sig_spk, sig_noise, need_snr)\\n\\n # Calc STFT\\n stft_spk = stft(sig_spk, fftsize=self._fftsize, overlap=self._overlap)\\n stft_noise = stft(sig_noise, fftsize=self._fftsize, overlap=self._overlap)\\n stft_mix = stft_spk + stft_noise\\n\\n # Skip small segments\\n frames, bin = stft_mix.shape\\n if frames <= self._context_size:\\n continue\\n\\n # Collect batch\\n i = 0\\n while i + self._context_size < frames:\\n\\n batch_sp.append(stft_spk[i:i + self._context_size, :])\\n batch_noise.append(stft_noise[i:i + self._context_size, :])\\n batch_mix.append(stft_mix[i:i + self._context_size, :])\\n\\n i += self._context_size // 2\\n batch_count += 1\\n\\n if batch_count == self._batch_size:\\n sp = np.array(batch_sp).reshape((self._batch_size,\\n self._context_size, -1))\\n noise = np.array(batch_noise).reshape((self._batch_size,\\n self._context_size, -1))\\n mix = np.array(batch_mix).reshape((self._batch_size,\\n self._context_size, -1))\\n yield sp, noise, mix\\n\\n batch_sp = []\\n batch_noise = []\\n batch_mix = []\\n batch_count = 0\",\n \"def addVCFSubsetJobs(self, workflow=None, inputData=None, db_vervet=None, sampleIDFile=None, transferOutput=True,\\\\\\n\\t\\t\\t\\t\\t\\trefFastaFList=None, GenomeAnalysisTKJar=None,\\\\\\n\\t\\t\\t\\t\\t\\tmaxContigID=None, outputDirPrefix=\\\"\\\"):\\n\\t\\tif workflow is None:\\n\\t\\t\\tworkflow = self\\n\\t\\tif GenomeAnalysisTKJar is None:\\n\\t\\t\\tGenomeAnalysisTKJar = workflow.GenomeAnalysisTKJar\\n\\t\\tif refFastaFList is None:\\n\\t\\t\\trefFastaFList = self.refFastaFList\\n\\t\\t\\n\\t\\tsys.stderr.write(\\\"Adding vcf-subset jobs for %s vcf files ... \\\"%(len(inputData.jobDataLs)))\\n\\t\\tno_of_jobs= 0\\n\\t\\t\\n\\t\\t\\n\\t\\ttopOutputDir = \\\"%sVCFSubset\\\"%(outputDirPrefix)\\n\\t\\ttopOutputDirJob = self.addMkDirJob(outputDir=topOutputDir)\\n\\t\\tno_of_jobs += 1\\n\\t\\t\\n\\t\\treturnData = PassingData()\\n\\t\\treturnData.jobDataLs = []\\n\\t\\tfor jobData in inputData.jobDataLs:\\n\\t\\t\\tinputF = jobData.vcfFile\\n\\t\\t\\tchr = self.getChrFromFname(inputF.name)\\n\\t\\t\\tif maxContigID:\\n\\t\\t\\t\\tcontig_id = self.getContigIDFromFname(inputF.name)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tcontig_id = int(contig_id)\\n\\t\\t\\t\\t\\tif contig_id>maxContigID:\\t#skip the small contigs\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tsys.stderr.write('Except type: %s\\\\n'%repr(sys.exc_info()))\\n\\t\\t\\t\\t\\timport traceback\\n\\t\\t\\t\\t\\ttraceback.print_exc()\\n\\t\\t\\tinputFBaseName = os.path.basename(inputF.name)\\n\\t\\t\\tcommonPrefix = inputFBaseName.split('.')[0]\\n\\t\\t\\toutputVCF = File(os.path.join(topOutputDir, '%s.subset.vcf'%(commonPrefix)))\\n\\t\\t\\tvcfSubsetJob = self.addVCFSubsetJob(workflow, executable=workflow.vcfSubset, vcfSubsetPath=workflow.vcfSubsetPath, \\\\\\n\\t\\t\\t\\t\\t\\tsampleIDFile=sampleIDFile,\\\\\\n\\t\\t\\t\\t\\t\\tinputVCF=inputF, outputF=outputVCF, \\\\\\n\\t\\t\\t\\t\\t\\tparentJobLs=[topOutputDirJob]+jobData.jobLs, transferOutput=False, job_max_memory=200,\\\\\\n\\t\\t\\t\\t\\t\\textraArguments=None, extraDependentInputLs=None)\\n\\t\\t\\t\\n\\t\\t\\t#2012.10.5\\n\\t\\t\\t#selectVariants would generate AC, AF so that TrioCaller could read it.\\n\\t\\t\\t#samtools uses 'AC1' instead of AC, 'AF1' instead of AF.\\n\\t\\t\\tVCF4OutputF = File(os.path.join(topOutputDir, '%s.niceformat.vcf'%commonPrefix))\\n\\t\\t\\tvcfConvertJob = self.addSelectVariantsJob(workflow, SelectVariantsJava=workflow.SelectVariantsJava, \\\\\\n\\t\\t\\t\\t\\tinputF=vcfSubsetJob.output, outputF=VCF4OutputF, \\\\\\n\\t\\t\\t\\t\\trefFastaFList=refFastaFList, parentJobLs=[vcfSubsetJob], \\\\\\n\\t\\t\\t\\t\\textraDependentInputLs=[], transferOutput=False, \\\\\\n\\t\\t\\t\\t\\textraArguments=None, job_max_memory=2000, interval=chr)\\n\\t\\t\\t\\n\\t\\t\\tVCFGzipOutputF = File(\\\"%s.gz\\\"%VCF4OutputF.name)\\n\\t\\t\\tVCFGzipOutput_tbi_F = File(\\\"%s.gz.tbi\\\"%VCF4OutputF.name)\\n\\t\\t\\tbgzip_tabix_VCF_job = self.addBGZIP_tabix_Job(workflow, bgzip_tabix=workflow.bgzip_tabix, \\\\\\n\\t\\t\\t\\t\\tparentJobLs=[vcfConvertJob], inputF=vcfConvertJob.output, outputF=VCFGzipOutputF, \\\\\\n\\t\\t\\t\\t\\ttransferOutput=transferOutput)\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\treturnData.jobDataLs.append(PassingData(jobLs=[bgzip_tabix_VCF_job], vcfFile=VCFGzipOutputF, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ttbi_F=VCFGzipOutput_tbi_F, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfileLs=[VCFGzipOutputF, VCFGzipOutput_tbi_F]))\\n\\t\\t\\t\\n\\t\\tsys.stderr.write(\\\"%s jobs.\\\\n\\\"%(self.no_of_jobs))\\n\\t\\treturn returnData\",\n \"def generate_flatbuffer_binaries():\\n for element in FLATBUFFERS_CONVERSION_DATA:\\n schema = element.schema\\n output_path = element.output_path\\n if not os.path.exists(output_path):\\n os.makedirs(output_path)\\n for json in element.input_files:\\n target = processed_json_path(json)\\n if needs_rebuild(json, target) or needs_rebuild(schema, target):\\n convert_json_to_flatbuffer_binary(\\n json, schema, output_path)\",\n \"def filesToBlender(context, prefix, max_blocks=200):\\n # Get reference matrix\\n refMatrix = None\\n if context.scene.maps_models_importer_is_ref_matrix_valid:\\n values = context.scene.maps_models_importer_ref_matrix\\n refMatrix = Matrix((values[0:4], values[4:8], values[8:12], values[12:16]))\\n\\n drawcallId = 0\\n while max_blocks <= 0 or drawcallId < max_blocks:\\n if not os.path.isfile(\\\"{}{:05d}-indices.bin\\\".format(prefix, drawcallId)):\\n break\\n\\n try:\\n indices, positions, uvs, img, constants = loadData(prefix, drawcallId)\\n except FileNotFoundError as err:\\n print(\\\"Skipping ({})\\\".format(err))\\n continue\\n\\n uvOffsetScale, matrix, refMatrix = extractUniforms(constants, refMatrix)\\n\\n # Make triangles from triangle strip index buffer\\n n = len(indices)\\n tris = [ [ indices[i+j] for j in [[0,1,2],[0,2,1]][i%2] ] for i in range(n - 3)]\\n tris = [ t for t in tris if t[0] != t[1] and t[0] != t[2] and t[1] != t[2] ]\\n verts = [ [ p[0], p[1], p[2] ] for p in positions ]\\n\\n [ou, ov, su, sv] = uvOffsetScale\\n uvs = [ [ (floor(u * 65535.0 + 0.5) + ou) * su, (floor(v * 65535.0 + 0.5) + ov) * sv ] for u, v in uvs ]\\n \\n if len(indices) == 0:\\n continue\\n\\n mesh_name = \\\"BuildingMesh-{:05d}\\\".format(drawcallId)\\n obj = addMesh(context, mesh_name, verts, tris, uvs)\\n obj.matrix_world = matrix\\n\\n mat_name = \\\"BuildingMat-{:05d}\\\".format(drawcallId)\\n addImageMaterial(mat_name, obj, img)\\n\\n drawcallId += 1\\n\\n # Save reference matrix\\n if refMatrix:\\n values = sum([list(v) for v in refMatrix], [])\\n context.scene.maps_models_importer_ref_matrix = values\\n context.scene.maps_models_importer_is_ref_matrix_valid = True\",\n \"def generate_batch(self) -> Tuple[np.ndarray, np.ndarray, List[str]]:\\n batch = []\\n labels = []\\n filelist = []\\n for i in range(self.batch_size):\\n filename = self.filelist[self.data_index]\\n filelist.append(self.filelist[self.data_index].split(\\\"\\\\\\\\\\\")[-1].split(\\\"/\\\")[-1])\\n greyimg, colorimg = read_img(filename)\\n batch.append(greyimg)\\n labels.append(colorimg)\\n self.data_index = (self.data_index + 1) % self.size\\n batch = np.asarray(batch) / 255\\n labels = np.asarray(labels) / 255\\n return batch, labels, filelist\",\n \"def make_batch(self, batch_size):\\n filenames = self.get_filenames()\\n\\n if os.path.isdir(filenames):\\n num_records = len(os.listdir(filenames))\\n print(\\\"Loading from directory. \\\" + str(num_records) + \\\" tfRecords found.\\\")\\n files = tf.data.Dataset.list_files(filenames + \\\"/\\\" + \\\"*.tfrecord\\\").shuffle(num_records)\\n dataset = files.apply(\\n tf.contrib.data.parallel_interleave(\\n lambda x: tf.data.TFRecordDataset(x, num_parallel_reads=256, buffer_size=8*1024*1024),\\n cycle_length=32, sloppy=True)\\n )\\n else:\\n print(\\\"Loading from single tfRecord...\\\")\\n dataset = tf.data.TFRecordDataset(filenames + \\\".tfrecord\\\").repeat()\\n \\n dataset = dataset.map(self.parser, num_parallel_calls=128)\\n \\n if self.subset == 'train':\\n min_queue_examples = int(\\n Cifar10DataSet.num_examples_per_epoch(self.subset) * 0.4)\\n # Ensure that the capacity is sufficiently large to provide good random\\n # shuffling.\\n dataset = dataset.shuffle(buffer_size=min_queue_examples + 3 * batch_size)\\n \\n dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))\\n dataset = dataset.prefetch(10)\\n \\n iterator = dataset.make_one_shot_iterator()\\n seq_batch, input_batch, map_batch, transformation_batch = iterator.get_next()\\n\\n return seq_batch, input_batch, map_batch, transformation_batch\",\n \"def write_batch_file(self, dot_input, dot_aln, hyphy_batch_file=\\\"\\\", hyphy_result_file=\\\"\\\"):\\r\\n gene_id = os.path.basename(dot_input).split(os.path.extsep)[0]\\r\\n path_main = os.path.splitext(dot_input)[0]\\r\\n\\r\\n if \\\"\\\" == hyphy_batch_file:\\r\\n hyphy_batch_file = path_main + \\\".bf\\\"\\r\\n\\r\\n if \\\"\\\" == hyphy_result_file:\\r\\n hyphy_result_file = path_main + \\\".result\\\"\\r\\n\\r\\n if \\\"\\\" == self.batch_content:\\r\\n raise BFError\\r\\n\\r\\n # replace begins here\\r\\n batch_content, num_hits = re.subn(self.f_input, dot_input, self.batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n # partition is optional\\r\\n if (0, 0) == self.partition:\\r\\n if self.f_partition in batch_content:\\r\\n batch_content, num_hits = re.subn(self.f_partition, \\\"\\\", batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n else:\\r\\n batch_content, num_hits = re.subn(self.f_partition, \\\"%d-%d\\\" % self.partition, batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n batch_content, num_hits = re.subn(self.f_mdl, self.mdl_file, batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n # only support 1 matrix now :2014-5-26\\r\\n batch_content, num_hits = re.subn(self.f_matrix_name, self.matrix_name[0], batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n if self.use_given_tree:\\r\\n tree_newick_string = self.tree_definition_external\\r\\n else:\\r\\n genes_share_aln = pHdata.aln_reader(dot_aln)\\r\\n tree_newick_string = self.build_tree(genes_share_aln)\\r\\n\\r\\n batch_content, num_hits = re.subn(self.f_tree, tree_newick_string, batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n batch_content, num_hits = re.subn(self.f_output, hyphy_result_file, batch_content)\\r\\n self._error_no_hit(num_hits)\\r\\n\\r\\n self.check_whether_incomplete(batch_content)\\r\\n\\r\\n with open(name=hyphy_batch_file, mode=\\\"w\\\") as bf_writer:\\r\\n bf_writer.write(batch_content)\",\n \"def save_batch(self):\\n self._batch_counter += 1\\n write_to_disk(\\n self._batch_cases,\\n os.path.join(\\n self.crop.location,\\n \\\"batches\\\",\\n BTCH_NM.format(self._batch_counter),\\n ),\\n )\\n self._batch_cases = []\\n self._counter = 0\",\n \"async def addToFingerPrint(samples, sampleset=, allsampleset=\\\"all\\\", workspace=WORKSPACE, sid=, vcf_list=None, \\nvcf_list_dir=, working_dir, crosscheck_batch_size, recreate_batch, bamcolname,\\ntaiga_dataset, taiga_filename):\\n bams = samples[bamcolname]\\n bams[sid] = bams.index\\n print('adding '+str(len(bams))+' new samples to the fingerprint')\\n wm = dm.WorkspaceManager(workspace).disable_hound()\\n \\n # Create batch files listing all vcfs in fingerprints dir and upload to bucket\\n # (NEW VERSION ONLY) will only needed if need to recreate batches\\n if recreate_batch:\\n if not vcf_list:\\n vcf_list = gcp.lsFiles([vcf_list_dir])\\n vcf_list = wm.get_samples()[\\\"fingerprint_vcf\\\"].tolist()\\n batches = []\\n for i, l in enumerate(range(0, len(vcf_list), crosscheck_batch_size)):\\n f = open(working_dir + \\\"vcf_batch_\\\"+str(i), 'w')\\n f.write(\\\"\\\\n\\\".join(vcf_list[l:l + crosscheck_batch_size]))\\n f.close()\\n batches.append(working_dir+\\\"vcf_batch_\\\"+str(i))\\n gcp.cpFiles(batches, vcf_list_dir)\\n\\n # Upload sample sheet\\n samples_df = pd.DataFrame()\\n samples_df[[\\\"bam_filepath\\\", \\\"bai_filepath\\\", \\\"sample_id\\\",\\n \\\"participant_id\\\"]] = bams[bamcolname + [sid, sid]].values\\n samples_df = samples_df.set_index('sample_id')\\n wm.upload_samples(samples_df, add_participant_samples=True)\\n wm.update_sample_set(sampleset, samples_df.index)\\n\\n # Submit jobs \\n submission_id = wm.create_submission(\\\"fingerprint_bam_with_liftover\\\", sampleset, \\n 'sample_set', expression='this.samples')\\n await terra.waitForSubmission(workspace, submission_id)\\n\\n #1.2 Crosscheck Fingerprint VCFs\\n #Here we use Dalmation to run the crosscheck_vcfs workflow on Terra. \\n # This workflow calls Picard CrosscheckFingerprints to compare the new \\n # fingerprint vcfs to batches of existing fingerprint vcfs in fingerprints_dir\\n # Create list with new vcfs and upload to bucket\\n f = open(working_dir + sampleset, 'w')\\n f.write(('\\\\n').join(wm.get_samples().loc[samples_df.index, 'fingerprints'].tolist()))\\n f.close()\\n gcp.cpFiles(working_dir + sampleset, vcf_list_dir)\\n os.system('rm '+working_dir + sampleset)\\n\\n # Upload sample sheet\\n if recreate_batch:\\n sample_group_df = pd.DataFrame(data={\\\"entity:sample_group_id\\\" : batches, \\\"vcf_group\\\" : [vcf_list_dir + x for x in batches]}).set_index('entity:sample_group_id')\\n else:\\n sample_group_df = pd.DataFrame(data={\\\"entity:sample_group_id\\\" : [sampleset], \\\"vcf_group\\\" : [vcf_list_dir+sampleset]}).set_index('entity:sample_group_id')\\n \\n print(wm.get_entities('sample_group').index.tolist())\\n wm.upload_entities(\\\"sample_group\\\", sample_group_df)\\n try:\\n wm.update_entity_set(\\\"sample_group\\\", set_id=allsampleset,\\n entity_ids=wm.get_entities('sample_group').index)\\n except:\\n print(\\\"still can't update entitis, please upload directly from the file in ../temp.tsv\\\")\\n #in case it does not work\\n sample_group_df.to_csv(\\\"../temp.tsv\\\", sep='\\\\t')\\n\\n # Submit jobs\\n conf = wm.get_config(\\\"crosscheck_vcfs\\\")\\n conf['inputs']['crosscheck.run_crosscheck.vcf_second_input_file'] = '\\\"'+vcf_list_dir+sampleset+'\\\"'\\n wm.update_config(conf)\\n submission_id = wm.create_submission(\\\"crosscheck_vcfs\\\", allsampleset, \\n 'sample_set',expression='this.samples')\\n await terra.waitForSubmission(workspace, submission_id)\\n\\n #1.3 Update LOD matrix\\n #Here we update the fingerprint LOD matrix on taiga with the new fingerprints\\n # Generate matrix with LOD score for new fingerprint vcfs\\n new_lod_list = []\\n samples_df = wm.get_entities(\\\"sample_group\\\")['cross_checks_out'].tolist()\\n for batch in samples_df:\\n # could be pd concat\\n df = pd.read_csv(batch, sep='\\\\t', comment='#')\\n lod_mat = df.pivot(index=\\\"LEFT_SAMPLE\\\",\\n columns=\\\"RIGHT_SAMPLE\\\", values=\\\"LOD_SCORE\\\")\\n new_lod_list.append(lod_mat)\\n new_lod_mat = pd.concat(new_lod_list)\\n new_lod_mat.index.name = None\\n new_lod_mat = new_lod_mat.T\\n\\n # Update LOD matrix ( have to update (A+a)*(B+b) = (AB)+(aB)+(Ab)+(ab))\\n prev_lod_mat = tc.get(name=taiga_dataset,file=taiga_filename)\\n new_ids = set(new_lod_mat.index)\\n old_ids = set(prev_lod_mat.index) - set(new_ids)\\n updated_lod_mat = pd.concat((prev_lod_mat.loc[old_ids,old_ids],\\n new_lod_mat.loc[new_ids,old_ids]), axis=0)\\n updated_lod_mat = pd.concat((updated_lod_mat.loc[new_ids.union(old_ids), old_ids], \\n new_lod_mat.transpose().loc[new_ids.union(old_ids, new_ids)]), axis=1)\\n updated_lod_mat.to_csv(working_dir+taiga_filename+'.csv')\\n \\n # Upload updated LOD matrix to Tiaga\\n tc.update_dataset(dataset_permaname=taiga_dataset,\\n changes_description=\\\"New bam fingerprints added for \\\"+sampleset,\\n upload_files=[\\n {\\n \\\"path\\\": working_dir+taiga_filename+'.csv',\\n \\\"name\\\": taiga_filename,\\n \\\"format\\\": \\\"NumericMatrixCSV\\\",\\n \\\"encoding\\\": \\\"utf-8\\\"\\n }\\n ],\\n add_all_existing_files=True)\\n\\n # finding issues with the dataset\\n v = updated_lod_mat.loc[new_ids]\\n ref = tracker.getTracker()\\n ref = ref.append(samples)\\n should = {}\\n print(\\\"\\\\n\\\\nsamples that should match but don't:\\\")\\n for u in set(fbams.arxspan_id):\\n res = v.loc[fbams[fbams.arxspan_id == u].index,\\n ref[ref.arxspan_id == u].index.tolist()]\\n for i, j in [(res.index[x], res.columns[y]) for x, y in np.argwhere(res.values < 100)]:\\n print('__________________________')\\n print(res.loc[i, j])\\n print(i, ':', tuple(ref.loc[i, ['arxspan_id', 'version', 'datatype', 'participant_id']].values), j, ':', tuple(\\n ref.loc[j, ['arxspan_id', 'version', 'datatype', 'participant_id', 'blacklist']]))\\n \\n print(\\\"\\\\n\\\\nsamples that shouldn't match but do\\\")\\n previ = ''\\n shouldnt = {}\\n for i, j in [(v.index[x], v.columns[y]) for x, y in np.argwhere(v.values > 500)]:\\n if i == j:\\n continue\\n if ref.loc[i]['participant_id'] == ref.loc[j]['participant_id']:\\n continue\\n if i != previ:\\n if previ != '':\\n shouldnt.update({'_'.join(ref.loc[previ, ['arxspan_id', 'version', 'datatype',\\n 'participant_id', \\n 'stripped_cell_line_name']].astype(str).values.tolist()): n})\\n n = [tuple(ref.loc[j, ['arxspan_id', 'version', 'datatype',\\n 'participant_id', 'stripped_cell_line_name']].values)]\\n else:\\n n.append(tuple(ref.loc[j, ['arxspan_id', 'version', 'datatype',\\n 'participant_id', 'stripped_cell_line_name']].values))\\n previ = i\\n return updated_lod_mat, should, shouldnt\",\n \"def test_no_batches(self):\\n\\n gas = {\\n \\\"class\\\": \\\"PASGas\\\",\\n \\\"path\\\": \\\"C:\\\\\\\\Python\\\\\\\\SensorTools\\\\\\\\Tests\\\\\\\\PAS_gas_testdata\\\",\\n \\\"filename\\\": \\\"NW1017.ASD\\\", \\n \\\"temperature_channel\\\": 0,\\n \\\"audio_channel\\\": 2,\\n \\\"pd_channel\\\": 1,\\n \\\"temperature_inverted\\\": True,\\n \\\"audio_inverted\\\": True,\\n }\\n liquid = {\\n \\\"class\\\": \\\"PASLiquid\\\",\\n \\\"path\\\": \\\"C:\\\\\\\\Python\\\\\\\\SensorTools\\\\\\\\Tests\\\\\\\\PAS_liquid_testdata\\\",\\n \\\"filename\\\": \\\"NW1006.ASD\\\",\\n \\\"temperature_channel\\\": 0,\\n \\\"audio_channel\\\": 1,\\n \\\"temperature_inverted\\\": True, \\n }\\n mess = [gas.copy(), gas.copy(), gas.copy(), gas.copy(), liquid.copy(), liquid.copy(), liquid.copy(), liquid.copy(), liquid.copy()]\\n mess[0][\\\"filename\\\"] = \\\"NW1017.ASD\\\"\\n mess[1][\\\"filename\\\"] = \\\"NW1018.ASD\\\"\\n mess[2][\\\"filename\\\"] = \\\"NW1019.ASD\\\"\\n mess[3][\\\"filename\\\"] = \\\"NW1020.ASD\\\"\\n \\n mess[4][\\\"filename\\\"] = \\\"NW1001.ASD\\\"\\n mess[5][\\\"filename\\\"] = \\\"NW1002.ASD\\\"\\n mess[6][\\\"filename\\\"] = \\\"NW1003.ASD\\\"\\n \\n mess[7][\\\"filename\\\"] = \\\"NW1004.ASD\\\"\\n mess[8][\\\"filename\\\"] = \\\"NW1008.ASD\\\"\\n \\n self.P = MLS.MultiLinearSpectra(verbose = self.verbose, mess = mess)\\n self.P.import_data(filetype = \\\"ASD\\\")\\n self.P.select_single_slope()\\n min_x, max_x = self.P.get_min_max_x() \\n self.P.bin_data(x_resolution = 0.01, min_x = min_x, max_x = max_x)\\n self.P.calculate_signal() \\n\\n batches = []\\n self.P.make_batches(batches)\",\n \"def _generate_and_load_initial_batch(self, working_directory: Path):\\n\\n template_dir = Path(working_directory) / \\\"template_1\\\"\\n template_dir.mkdir()\\n # changes here should often be reflected in\\n # data_generator_opts and data_loader_opts\\n\\n channel_decl = self.channel_configs[0]\\n\\n plugin_options = {\\n \\\"pid\\\": \\\"0\\\",\\n \\\"big_ids\\\": \\\"True\\\",\\n }\\n # if it's efficient to do the whole load in one go, let's just do that.\\n if self.run_until.gap < MIN_PORTION_SIZE:\\n num_records = self.run_until.gap\\n else:\\n num_records = 1 # smallest possible batch to get to parallelizing fast\\n results = self._generate_and_load_batch(\\n template_dir,\\n channel_decl.org_config,\\n {\\n \\\"generator_yaml\\\": self.options.get(\\\"recipe\\\"),\\n \\\"num_records\\\": num_records,\\n \\\"num_records_tablename\\\": self.run_until.sobject_name or COUNT_REPS,\\n \\\"loading_rules\\\": self.loading_rules,\\n \\\"vars\\\": channel_decl.merge_recipe_options(self.recipe_options),\\n \\\"plugin_options\\\": plugin_options,\\n \\\"bulk_mode\\\": self.bulk_mode,\\n },\\n )\\n self.update_running_totals_from_load_step_results(results)\\n\\n # rename directory to reflect real number of sets created.\\n wd = SnowfakeryWorkingDirectory(template_dir)\\n if self.run_until.sobject_name:\\n self.sets_finished_while_generating_template = wd.get_record_counts()[\\n self.run_until.sobject_name\\n ]\\n else:\\n self.sets_finished_while_generating_template = num_records\\n\\n new_template_dir = data_loader_new_directory_name(template_dir, self.run_until)\\n shutil.move(template_dir, new_template_dir)\\n template_dir = new_template_dir\\n\\n # don't send data tables to child processes. All they\\n # care about are ID->OID mappings\\n wd = SnowfakeryWorkingDirectory(template_dir)\\n self._cleanup_object_tables(*wd.setup_engine())\\n\\n return template_dir, wd.relevant_sobjects()\",\n \"def combine_gvcf(self, reference, gvcf_list, output, input_is_sorted=False, extension_list=[\\\"g.vcf\\\",],\\n tmp_dir=\\\"./tmp_combine_gvcf/\\\", max_files_per_merging=50, iteration=0, threads=None,\\n remove_intermediate_files=False):\\n\\n filtered_gvcf_list = []\\n for filename in gvcf_list:\\n for extension in extension_list:\\n if extension == filename[-len(extension):]:\\n filtered_gvcf_list.append(filename)\\n break\\n \\n if len(filtered_gvcf_list) <= max_files_per_merging:\\n options = self.parse_options(reference, filtered_gvcf_list, output, input_is_sorted, extension_list=extension_list)\\n self.execute(options, runtype=\\\"cp\\\")\\n if remove_intermediate_files:\\n shutil.rmtree(tmp_dir, ignore_errors=True)\\n\\n else:\\n self.safe_mkdir(tmp_dir)\\n iteration_dir = \\\"%s/iteration_%i/\\\" % (tmp_dir, iteration)\\n self.safe_mkdir(iteration_dir)\\n\\n number_of_files = len(filtered_gvcf_list)\\n\\n bins = np.arange(0, number_of_files, max_files_per_merging)\\n #print(bins)\\n if bins[-1] != number_of_files:\\n if number_of_files - bins[-1] < 2:\\n bins[-1] = number_of_files\\n else:\\n bins = np.append(bins, number_of_files)\\n\\n output_file_list = []\\n options_list = []\\n\\n merged_files = 0\\n for i in range(0, len(bins)-1):\\n output_file = \\\"%s/%i.g.vcf\\\" % (iteration_dir, i)\\n output_file_list.append(output_file)\\n #print(bins[i], bins[i+1])\\n\\n merged_files += bins[i+1] - bins[i]\\n options_list.append(self.parse_options(reference,\\n filtered_gvcf_list[bins[i]:bins[i+1]],\\n output_file,\\n input_is_sorted, extension_list=extension_list))\\n print(\\\"%i/%i files will be merged\\\" % (merged_files, number_of_files))\\n\\n self.parallel_execute(options_list, threads=threads, runtype=\\\"cp\\\")\\n\\n self.combine_gvcf(reference, output_file_list, output, input_is_sorted=input_is_sorted,\\n extension_list=extension_list,\\n tmp_dir=tmp_dir,\\n max_files_per_merging=max_files_per_merging, iteration=iteration+1)\",\n \"def wrt_gau_input(self):\\n fp = open(\\\"myfiles.dat\\\", \\\"w\\\") \\n nmol = self.model['nmol']\\n prefix = self.config['job_prefix']\\n incr = self.config['incr']\\n for i in xrange(0, nmol, incr):\\n self.wrt_gau_input_once(i)\\n print >>fp, \\\"%sx%s.gjf\\\" % (prefix, i)\\n fp.close()\\n \\n # extra jobs\\n fp = open(\\\"link.sh\\\", \\\"w\\\")\\n print >>fp, \\\"#! /bin/bash\\\"\\n print >>fp, \\\"# sampling shell input\\\"\\n print >>fp, \\\"rm linking.gjf\\\"\\n print >>fp, \\\"myfiles=`more myfiles.dat`\\\"\\n print >>fp, \\\"\\\"\\\"\\n for onefile in $myfiles;\\n do cat $onefile >> linking.gjf;\\n echo -e '\\\\\\\\n--Link1--\\\\\\\\n' >> linking.gjf;\\n done\\n \\\"\\\"\\\"\\n fp.close()\\n return\",\n \"def build(self):\\r\\n self.dirty = 0\\r\\n \\r\\n # Files first\\r\\n for output in self.files.keys():\\r\\n params = self.files[output]\\r\\n if (params[1] != -1):\\r\\n filename = params[0]\\r\\n freq = params[1]\\r\\n if (output == 'energies'):\\r\\n self.myOutputs.append(OutputEnergies.OutputEnergies(filename, freq, 1,0,1.0,0))\\r\\n elif (output == 'dcdtrajpos'):\\r\\n if (os.path.exists(filename)): # Continue\\r\\n self.myOutputs.append(OutputDCDTrajectory.OutputDCDTrajectory(filename, freq, 1, 1))\\r\\n else: # Overwrite\\r\\n self.myOutputs.append(OutputDCDTrajectory.OutputDCDTrajectory(filename, freq, 1, 0))\\r\\n elif (output == 'dcdtrajvel'):\\r\\n if (os.path.exists(filename)):\\r\\n self.myOutputs.append(OutputDCDTrajectoryVel.OutputDCDTrajectoryVel(filename, freq, 1, 1))\\r\\n else:\\r\\n self.myOutputs.append(OutputDCDTrajectoryVel.OutputDCDTrajectoryVel(filename, freq, 1, 0))\\r\\n elif (output == 'xyztrajforce'):\\r\\n self.myOutputs.append(OutputXYZTrajectoryForce.OutputXYZTrajectoryForce(filename, freq))\\r\\n elif (output == 'xyztrajpos'):\\r\\n self.myOutputs.append(OutputXYZTrajectoryPos.OutputXYZTrajectoryPos(filename, freq, 1))\\r\\n elif (output == 'xyztrajvel'):\\r\\n self.myOutputs.append(OutputXYZTrajectoryVel.OutputXYZTrajectoryVel(filename, freq))\\r\\n elif (output == 'gui'):\\r\\n self.myOutputs.append(OutputFAHGUI.OutputFAHGUI(filename, freq, 52753, 1, \\\"MDL_3.0\\\", 0.0, 0))\\r\\n\\r\\n if (self.screen != -1):\\r\\n self.myOutputs.append(OutputScreen.OutputScreen(self.screen))\\r\\n\\r\\n\\r\\n # Now plots\\r\\n for plot in self.plots.keys():\\r\\n freq = self.plots[plot]\\r\\n if (freq != -1):\\r\\n\\r\\n # Initialize a plot\\r\\n if (not self.doMPL): # Gnuplot\\r\\n self.xyData[plot] = []\\r\\n self.graphs[plot] = Gnuplot(debug=0)\\r\\n else: # Matplotlib\\r\\n self.xData[plot] = []\\r\\n self.yData[plot] = []\\r\\n self.figures[plot] = 0\\r\\n\\r\\n # Add the function to plot the data,\\r\\n # and the frequency at which to execute it\\r\\n self.myPlots.append([self.plotFunctions[plot], freq])\",\n \"def build(self, file_number, data):\\n pass\",\n \"def create(self):\\n\\n for key in self.dirs.keys():\\n if type(self.dirs[key]) == dict:\\n for dir_type in self.dirs[key].keys():\\n create_if_not_exists(self.dirs[key][dir_type])\\n else:\\n create_if_not_exists(self.dirs[key])\\n\\n self.inputFileIds = {}\\n for sample_name, sample_info in self.samples.items():\\n if not sample_info['use_it']:\\n continue\\n\\n process_name = sample_info[\\\"process_name_specific\\\"]\\n is_mc = (sample_info[\\\"type\\\"] == \\\"mc\\\")\\n\\n if not is_mc:\\n continue\\n\\n logging.info(\\\"Creating configuration files to run '%s' for sample %s\\\" % (self.executable, process_name))\\n\\n inputFileList = generateInputFileList(sample_info, self.max_files_per_job)\\n key_dir = getKey(process_name)\\n\\n outputFile = os.path.join(\\n self.dirs[key_dir][DKEY_HISTO], \\\"%s.root\\\" % process_name\\n )\\n self.outputFiles[process_name] = {\\n 'inputFiles' : [],\\n 'outputFile' : outputFile,\\n }\\n if os.path.isfile(outputFile) and tools_is_file_ok(outputFile, min_file_size = 2000):\\n logging.info('File {} already exists --> skipping job'.format(outputFile))\\n continue\\n\\n for jobId in inputFileList.keys():\\n\\n key_file = getKey(sample_name, jobId)\\n\\n self.inputFiles[key_file] = inputFileList[jobId]\\n if len(self.inputFiles[key_file]) == 0:\\n logging.warning(\\n \\\"'%s' = %s --> skipping job !!\\\" % (key_file, self.inputFiles[key_file])\\n )\\n continue\\n\\n self.cfgFiles_projection[key_file] = os.path.join(\\n self.dirs[key_dir][DKEY_CFGS], \\\"project_%s_%i_cfg.txt\\\" % (process_name, jobId)\\n )\\n self.outputFiles_tmp[key_file] = os.path.join(\\n self.dirs[key_dir][DKEY_HISTO_TMP], \\\"histogram_%i.root\\\" % jobId\\n )\\n self.logFiles_projection[key_file] = os.path.join(\\n self.dirs[key_dir][DKEY_LOGS], \\\"project_%s_%i.log\\\" % (process_name, jobId)\\n )\\n self.scriptFiles_projection[key_file] = os.path.join(\\n self.dirs[key_dir][DKEY_CFGS], \\\"project_%s_%i_cfg.sh\\\" % (process_name, jobId)\\n )\\n projection_module = self.projection_module\\n if projection_module == \\\"count\\\":\\n projection_module = \\\"countHistogramAll\\\"\\n if sample_name.startswith('/TTTo'):\\n projection_module += \\\"CompTopRwgt\\\"\\n elif sample_info['sample_category'].startswith('ttH'):\\n projection_module += \\\"CompHTXS\\\"\\n elif isSplitByNlheJet(process_name):\\n projection_module += \\\"SplitByLHENjet\\\"\\n elif isSplitByNlheHT(process_name):\\n projection_module += \\\"SplitByLHEHT\\\"\\n elif isSplitByNlheJetHT(process_name, sample_name):\\n projection_module += \\\"SplitByLHENjetHT\\\"\\n self.jobOptions_sbatch[key_file] = {\\n 'histName' : process_name,\\n 'inputFiles' : self.inputFiles[key_file],\\n 'cfgFile_path' : self.cfgFiles_projection[key_file],\\n 'outputFile' : self.outputFiles_tmp[key_file],\\n 'logFile' : self.logFiles_projection[key_file],\\n 'scriptFile' : self.scriptFiles_projection[key_file],\\n 'projection_module' : projection_module,\\n }\\n if self.projection_module != 'puHist':\\n self.jobOptions_sbatch[key_file]['ref_genWeight'] = self.ref_genWeights[process_name]\\n if process_name not in self.ref_genWeights:\\n raise RuntimeError(\\\"Unable to find reference LHE weight for process %s\\\" % process_name)\\n self.createCfg_project(self.jobOptions_sbatch[key_file])\\n self.outputFiles[process_name]['inputFiles'].append(self.outputFiles_tmp[key_file])\\n\\n if self.is_sbatch:\\n logging.info(\\\"Creating script for submitting '%s' jobs to batch system\\\" % self.executable)\\n self.num_jobs['project'] += self.createScript_sbatch(\\n self.executable, self.sbatchFile_projection, self.jobOptions_sbatch\\n )\\n\\n logging.info(\\\"Creating Makefile\\\")\\n lines_makefile = []\\n self.addToMakefile_project(lines_makefile)\\n self.addToMakefile_hadd(lines_makefile)\\n if self.plot:\\n self.addToMakefile_plot(lines_makefile)\\n self.addToMakefile_finalHadd(lines_makefile)\\n self.createMakefile(lines_makefile)\\n logging.info(\\\"Done\\\")\\n\\n return self.num_jobs\",\n \"def __iter__(self):\\n\\n batch_sp = []\\n batch_noise = []\\n batch_mix = []\\n batch_count = 0\\n\\n while True:\\n\\n # Randomizing wav lists\\n random.shuffle(self._lst_spk_files)\\n random.shuffle(self._lst_noise_files)\\n\\n for spk_file, noise_file in zip(self._lst_spk_files, self._lst_noise_files):\\n\\n # Read wav files\\n sig_spk, rate = self.__read_wav_file(spk_file)\\n sig_noise, _ = self.__read_wav_file(noise_file)\\n\\n # Skip silence file\\n if np.mean(sig_spk ** 2) < self.energy_silence_threshold or \\\\\\n np.mean(sig_noise ** 2) < self.energy_silence_threshold:\\n continue\\n\\n # Apply reverberations\\n if self._enable_rir:\\n rev_prob = np.random.uniform(0, 1) < self._rir_prob\\n if rev_prob:\\n filter_num = random.randint(0, self._rir_filters_num - 1)\\n\\n filter_sp_name = self.RIR_PREF + str(filter_num) + self.RIR_SP_SUF\\n filter_n_name = self.RIR_PREF + str(filter_num) + self.RIR_N_SUF\\n\\n sig_spk = reverb_util.reverb_matlab(sig_spk, rate, os.path.join(self._rir_dir, filter_sp_name))\\n sig_noise = reverb_util.reverb_matlab(sig_noise, rate, os.path.join(self._rir_dir, filter_n_name))\\n\\n # Align signal\\n min_length = min(sig_spk.shape[0], sig_noise.shape[0])\\n spk_length = sig_spk.shape[0]\\n noise_length = sig_noise.shape[0]\\n\\n if min_length < self._fftsize:\\n raise Exception(\\\"ERROR: Too short signals in dataset\\\")\\n\\n if spk_length > min_length:\\n start_ind = random.randint(0, spk_length - min_length)\\n sig_spk = sig_spk[start_ind:start_ind + min_length]\\n elif noise_length > min_length:\\n start_ind = random.randint(0, noise_length - min_length)\\n sig_noise = sig_noise[start_ind:start_ind + min_length]\\n\\n # Generate need SNR\\n need_snr = random.uniform(self._min_snr, self._max_snr)\\n\\n # Calc scaled signals\\n sig_spk, sig_noise = self.__mix_with_snr(sig_spk, sig_noise, need_snr)\\n\\n # Normalization\\n norm_const = np.max([np.max(np.abs(sig_spk)), np.max(np.abs(sig_noise))])\\n sig_spk /= norm_const\\n sig_noise /= norm_const\\n\\n # Calc STFT\\n stft_spk = stft(sig_spk, fftsize=self._fftsize, overlap=self._overlap)\\n stft_noise = stft(sig_noise, fftsize=self._fftsize, overlap=self._overlap)\\n stft_mix = stft_spk + stft_noise\\n\\n # Skip small segments\\n frames, bin = stft_mix.shape\\n if frames <= self._context_size:\\n continue\\n\\n # Collect batch\\n i = 0\\n while i + self._context_size < frames:\\n\\n batch_sp.append(stft_spk[i:i + self._context_size, :])\\n batch_noise.append(stft_noise[i:i + self._context_size, :])\\n batch_mix.append(stft_mix[i:i + self._context_size, :])\\n\\n i += self._context_size // 2\\n batch_count += 1\\n\\n if batch_count == self._batch_size:\\n sp = np.array(batch_sp).reshape((self._batch_size,\\n self._context_size, -1))\\n noise = np.array(batch_noise).reshape((self._batch_size,\\n self._context_size, -1))\\n mix = np.array(batch_mix).reshape((self._batch_size,\\n self._context_size, -1))\\n yield sp, noise, mix\\n\\n batch_sp = []\\n batch_noise = []\\n batch_mix = []\\n batch_count = 0\",\n \"def build(self, is_easy=False) -> None:\\n allocation = ['train', 'dev', 'test']\\n\\n bm25_helper = self.__build_bm25_helper(is_easy)\\n\\n for entry in allocation:\\n with open(self.__json_location + '/merged_' + entry + '.json', 'r') as f:\\n json_data = json.load(f)\\n\\n output_file_name = 'data_' + entry\\n if is_easy:\\n json2training_converter = Json2EasyTraining(json_data, bm25_helper)\\n output_file_name += '_easy'\\n else:\\n json2training_converter = JSON2Training(json_data, bm25_helper)\\n\\n training_set = json2training_converter.convert()\\n dialog_lookup_table = json2training_converter.get_dialog_lookup_table()\\n\\n self.__write_tsv(output_file_name + '.tsv', training_set)\\n self.__write_array(output_file_name + '_lookup' '.txt', dialog_lookup_table)\",\n \"def bowtie_build(self):\\n if self.params[\\\"data_type\\\"] != \\\"barcode\\\":\\n command = [\\n \\\"bowtie2-build\\\",\\n \\\"-f\\\",\\n \\\"--threads\\\", str(self.proc),\\n os.path.join(self.params[\\\"index_path\\\"], \\\"ref.fa\\\"),\\n os.path.join(self.params[\\\"index_path\\\"], \\\"reference\\\")\\n ]\\n\\n self.run_subprocess(command)\",\n \"def GatherVcfs(\\n b: hb.Batch,\\n input_vcfs: List,\\n disk_size: int,\\n output_vcf_path: str = None,\\n) -> Job:\\n j = b.new_job('VQSR: FinalGatherVcf')\\n j.image(utils.GATK_IMAGE)\\n j.memory(f'16G')\\n j.storage(f'{disk_size}G')\\n j.declare_resource_group(\\n output_vcf={'vcf.gz': f'{NAME}_gathered.vcf.gz', 'vcf.gz.tbi': f'{NAME}_gathered.vcf.gz.tbi'}\\n )\\n\\n input_cmdl = ' '.join([f'--input {v}' for v in input_vcfs])\\n j.command(\\n f\\\"\\\"\\\"set -euo pipefail\\n # --ignore-safety-checks makes a big performance difference so we include it in \\n # our invocation. This argument disables expensive checks that the file headers \\n # contain the same set of genotyped samples and that files are in order \\n # by position of first record.\\n gatk --java-options -Xms6g \\\\\\\\\\n GatherVcfsCloud \\\\\\\\\\n --gather-type BLOCK \\\\\\\\\\n {input_cmdl} \\\\\\\\\\n --output {j.output_vcf['vcf.gz']}\\n tabix {j.output_vcf['vcf.gz']}\\\"\\\"\\\"\\n )\\n if output_vcf_path:\\n b.write_output(j.output_vcf, f'{output_vcf_path}{NAME}_gathered{LABEL}')\\n return j\",\n \"def generate_megafile():\\n\\n print(\\\"\\\\nFetching testing dataset…\\\")\\n testing = get_testing()\\n\\n print(\\\"\\\\nFetching ECDC dataset…\\\")\\n ecdc = get_ecdc()\\n\\n location_mismatch = set(testing.location).difference(set(ecdc.location))\\n for loc in location_mismatch:\\n print(f\\\"<!> Location '{loc}' has testing data but is absent from ECDC data\\\")\\n\\n print(\\\"\\\\nFetching OxCGRT dataset…\\\")\\n cgrt = get_cgrt()\\n\\n all_covid = (\\n ecdc\\n .merge(testing, on=[\\\"date\\\", \\\"location\\\"], how=\\\"outer\\\")\\n .merge(cgrt, on=[\\\"date\\\", \\\"location\\\"], how=\\\"left\\\")\\n .sort_values([\\\"location\\\", \\\"date\\\"])\\n )\\n\\n # Add ISO codes\\n print(\\\"Adding ISO codes…\\\")\\n iso_codes = pd.read_csv(os.path.join(INPUT_DIR, \\\"iso/iso3166_1_alpha_3_codes.csv\\\"))\\n\\n missing_iso = set(all_covid.location).difference(set(iso_codes.location))\\n if len(missing_iso) > 0:\\n print(missing_iso)\\n raise Exception(\\\"Missing ISO code for some locations\\\")\\n\\n all_covid = iso_codes.merge(all_covid, on=\\\"location\\\")\\n\\n # Add continents\\n print(\\\"Adding continents…\\\")\\n continents = pd.read_csv(\\n os.path.join(INPUT_DIR, \\\"owid/continents.csv\\\"),\\n names=[\\\"_1\\\", \\\"iso_code\\\", \\\"_2\\\", \\\"continent\\\"],\\n usecols=[\\\"iso_code\\\", \\\"continent\\\"],\\n header=0\\n )\\n\\n all_covid = continents.merge(all_covid, on=\\\"iso_code\\\", how=\\\"right\\\")\\n\\n # Add macro variables\\n # - the key is the name of the variable of interest\\n # - the value is the path to the corresponding file\\n macro_variables = {\\n \\\"population\\\": \\\"un/population_2020.csv\\\",\\n \\\"population_density\\\": \\\"wb/population_density.csv\\\",\\n \\\"median_age\\\": \\\"un/median_age.csv\\\",\\n \\\"aged_65_older\\\": \\\"wb/aged_65_older.csv\\\",\\n \\\"aged_70_older\\\": \\\"un/aged_70_older.csv\\\",\\n \\\"gdp_per_capita\\\": \\\"wb/gdp_per_capita.csv\\\",\\n \\\"extreme_poverty\\\": \\\"wb/extreme_poverty.csv\\\",\\n \\\"cardiovasc_death_rate\\\": \\\"gbd/cardiovasc_death_rate.csv\\\",\\n \\\"diabetes_prevalence\\\": \\\"wb/diabetes_prevalence.csv\\\",\\n \\\"female_smokers\\\": \\\"wb/female_smokers.csv\\\",\\n \\\"male_smokers\\\": \\\"wb/male_smokers.csv\\\",\\n \\\"handwashing_facilities\\\": \\\"un/handwashing_facilities.csv\\\",\\n \\\"hospital_beds_per_thousand\\\": \\\"owid/hospital_beds.csv\\\",\\n \\\"life_expectancy\\\": \\\"owid/life_expectancy.csv\\\",\\n \\\"human_development_index\\\": \\\"un/human_development_index.csv\\\",\\n }\\n all_covid = add_macro_variables(all_covid, macro_variables)\\n\\n print(\\\"Writing to CSV…\\\")\\n all_covid.to_csv(os.path.join(DATA_DIR, \\\"owid-covid-data.csv\\\"), index=False)\\n\\n print(\\\"Writing to XLSX…\\\")\\n all_covid.to_excel(os.path.join(DATA_DIR, \\\"owid-covid-data.xlsx\\\"), index=False)\\n\\n print(\\\"Writing to JSON…\\\")\\n df_to_json(all_covid, os.path.join(DATA_DIR, \\\"owid-covid-data.json\\\"), macro_variables.keys())\\n\\n # Store the last updated time\\n timestamp_filename = os.path.join(DATA_DIR, \\\"owid-covid-data-last-updated-timestamp.txt\\\")\\n with open(timestamp_filename, \\\"w\\\") as timestamp_file:\\n timestamp_file.write(datetime.utcnow().replace(microsecond=0).isoformat())\\n\\n print(\\\"All done!\\\")\",\n \"def upload_data_new_batch(tsca_id, latest_tsca_id, paths_to_batches_info, namespace, workspace, google_bucket_id):\\n paths_to_batches_info_df = pd.read_excel(paths_to_batches_info, index_col=0)\\n path_to_samples_info = paths_to_batches_info_df.loc[tsca_id, 'path_to_samples_info']\\n\\n # DF of remote [sample < > sample set ]\\n remote_sample_sets = pd.read_table('remote_files/sample_set_membership_%s.tsv'%latest_tsca_id)\\n # DF of remote [pair < > pair set]\\n remote_pair_sets = pd.read_table('remote_files/pair_set_membership_%s.tsv'%latest_tsca_id)\\n\\n all_samples = get_samples(paths_to_batches_info, google_bucket_id)\\n # Add cohorts for older batches\\n all_samples = add_cohort_to_old_batches(all_samples)\\n\\n ##### Remove blacklisted samples ##\\n # Blacklisted samples\\n blacklisted = pd.read_table(\\\"samples_blacklist.txt\\\", header=None, names=[\\\"entity:sample_id\\\"])\\n blacklisted_sample_ids = blacklisted[\\\"entity:sample_id\\\"].values.tolist()\\n all_samples = all_samples[~all_samples[\\\"entity:sample_id\\\"].isin(blacklisted_sample_ids)]\\n\\n ########## Participants ##########\\n print(\\\"Uploading participants...\\\") \\n participants = prepare_participants_for_metadata_export(path_to_samples_info, tsca_id)\\n r1 = save_and_upload_participants(participants, namespace, workspace, tsca_id)\\n ##################################\\n\\n ########## Samples ############\\n print(\\\"Uploading samples...\\\")\\n batch_samples = prepare_batch_samples_for_metadata_export(path_to_samples_info, tsca_id, google_bucket_id)\\n r2 = save_and_upload_samples(batch_samples, namespace, workspace, tsca_id)\\n #################################\\n\\n ########## Pairs #############\\n print(\\\"Uploading pairs...\\\")\\n pairs = create_pairs_list(all_samples)\\n r3 = save_and_upload_pairs(namespace, workspace, pairs)\\n #################################\\n\\n ########## Sample Sets #########\\n print(\\\"Uploading sample sets...\\\")\\n batch_sample_set, batch_tumor_set, batch_normal_set = prepare_batch_sample_set_for_metadata_export(path_to_samples_info, tsca_id)\\n # Remove the samples that have already been uploaded \\n uploaded_sample_ids = remote_sample_sets['sample'].tolist()\\n batch_sample_set_clean = batch_sample_set[~batch_sample_set['sample_id'].isin(uploaded_sample_ids)]\\n batch_tumor_set_clean = batch_tumor_set[~batch_tumor_set['sample_id'].isin(uploaded_sample_ids)]\\n batch_normal_set_clean = batch_normal_set[~batch_normal_set['sample_id'].isin(uploaded_sample_ids)]\\n r4a, r4b, r4c = save_and_upload_batch_sample_sets(batch_sample_set_clean, batch_tumor_set_clean, batch_normal_set_clean, tsca_id, namespace, workspace)\\n #################################\\n\\n ########## PoNs ###############\\n print(\\\"Uploading PoNs...\\\")\\n \\n # Number of latest tsca id\\n latest_tsca_id_int = int(re.findall('\\\\d+', latest_tsca_id )[0])\\n # Array with list of all previous TSCA ids\\n previous_tsca_ids = [\\\"TSCA%s\\\"%i for i in np.arange(14, latest_tsca_id_int+1)]\\n previous_tsca_ids.insert(0, \\\"TSCA1213\\\")\\n\\n pon, name = create_panel_of_normals_advanced(tsca_id, all_samples,\\\\\\n num_normals_per_cohort_involved = 3, \\\\\\n batches_to_pick_from = previous_tsca_ids)\\n\\n # Only upload PoN if it hasn't been uploaded already\\n if not name in remote_sample_sets['membership:sample_set_id'].unique().tolist():\\n r5 = upload_pon(pon, name, namespace, workspace) \\n else: \\n print(\\\"PoN already exists...\\\")\\n r5 = {}\\n #################################\\n \\n ########## Pair Set ###########\\n print(\\\"Uploading pair sets...\\\")\\n # Upload cumulative pair sets\\n tn_cum_pairsets, tp_cum_pairsets = prepare_cumulative_pairsets_for_metadata_export(pairs, tsca_id)\\n r6 = upload_pairsets(namespace, workspace, tn_cum_pairsets, \\\"TN\\\")\\n r7 = upload_pairsets(namespace, workspace, tp_cum_pairsets, \\\"TP\\\")\\n\\n # Batch pair sets\\n tn_pairsets, tp_pairsets = prepare_batch_pairsets_for_metadata_export(all_samples, pairs, tsca_id)\\n uploaded_pair_ids = remote_pair_sets['pair'].tolist()\\n tn_pairsets_clean = tn_pairsets[~tn_pairsets['pair_id'].isin(uploaded_pair_ids)]\\n tp_pairsets_clean = tp_pairsets[~tp_pairsets['pair_id'].isin(uploaded_pair_ids)]\\n\\n r8 = upload_pairsets(namespace, workspace, tn_pairsets_clean, \\\"TN\\\")\\n r9 = upload_pairsets(namespace, workspace, tp_pairsets_clean, \\\"TP\\\")\\n #################################\\n\\n return (r1, r2, r3, r4a, r4b, r4c, r5, r6, r7, r8, r9)\",\n \"def concatenate_sft_files(self):\\n\\n SFTFilename = (\\n f\\\"{self.detectors[0]}-{self.nsfts}_{self.detectors}_{self.Tsft}SFT_mfdv4\\\"\\n )\\n # We don't try to reproduce the NB filename convention exactly,\\n # as there could be always rounding offsets with the number of bins,\\n # instead we use wildcards there.\\n outfreq = int(np.floor(self.fmin))\\n outwidth = int(np.floor(self.Band))\\n SFTFilename += f\\\"_NBF{outfreq:04d}Hz*W{outwidth:04d}Hz*\\\"\\n SFTFilename += f\\\"-{self.tstart}-{self.duration}.sft\\\"\\n SFTFile_fullpath = os.path.join(self.outdir, SFTFilename)\\n if os.path.isfile(SFTFile_fullpath):\\n logger.info(\\n f\\\"Removing previous file(s) {SFTFile_fullpath} (no caching implemented).\\\"\\n )\\n os.remove(SFTFile_fullpath)\\n\\n inpattern = os.path.join(self.tmp_outdir, \\\"*sft\\\")\\n cl_splitSFTS = \\\"lalpulsar_splitSFTs\\\"\\n cl_splitSFTS += \\\" -fs {} -fb {} -fe {} -n {} -- {}\\\".format(\\n self.fmin, self.Band, self.fmin + self.Band, self.outdir, inpattern\\n )\\n utils.run_commandline(cl_splitSFTS)\\n utils.run_commandline(f\\\"rm -r {self.tmp_outdir}\\\")\\n outglob = glob.glob(SFTFile_fullpath)\\n if len(outglob) != 1:\\n raise IOError(\\n \\\"Expected to produce exactly 1 merged file\\\"\\n f\\\" matching pattern '{SFTFile_fullpath}',\\\"\\n f\\\" but got {len(outglob)} matches: {outglob}\\\"\\n \\\" Something went wrong!\\\"\\n )\\n self.sftfilepath = outglob[0]\\n logger.info(f\\\"Successfully wrote SFTs to: {self.sftfilepath}\\\")\",\n \"def makeJob(kallisto, index, meta, bootstraps, files, single, s=1, l=180): \\n cmd = \\\"%(kallisto)s quant -i %(index)s -o %(meta)s \\\" % locals()\\n for file in files: \\n cmd += \\\" ../%s\\\" % file \\n if single: \\n cmd += \\\" --single -l %(l)i -s %(s)i\\\" % locals()\\n cmd += \\\" &> %s.log.txt\\\" % meta\\n return cmd\",\n \"def buildClusters(self):\\n oldLatFile = 'needed_files/lat.in'\\n oldFile = open(oldLatFile, 'r')\\n oldLines = [line for line in oldFile]\\n oldFile.close()\\n \\n newFile = open('enum/lat.in','w')\\n for i in xrange(len(oldLines)):\\n if 'Number pairs' in oldLines[i-1] and i>=1: #bch use label on previous line\\n for num in self.clusterNums:\\n newFile.write(str(num) + \\\" \\\")\\n newFile.write(\\\"\\\\n\\\")\\n else:\\n newFile.write(oldLines[i])\\n newFile.close()\\n \\n lastDir = os.getcwd()\\n os.chdir(lastDir + '/enum')\\n if sum(self.clusterNums)<=1500: #the 1500 assumes you are running Main with 16G. \\n subprocess.call([self.uncleExec, '10'], stdout=self.uncleOut)\\n else:\\n subprocess.call(['echo','Warning: BLOCKING CLUSTER JOB to save time'])\\n# clustersjob = ClustersBuild.clustersjob()\\n# clustersjob.clustBuild()\\n# \\n os.chdir(lastDir)\",\n \"def regenerate_gas_strat_lut_files():\\n for record in gas_records.values():\\n record(force_strat_calculation=True, save_strat=True)\",\n \"def quant(input_folder,fastq_dict,species_kmers,output_folder,transcriptome_folder,bsub_out=\\\"bsub_out\\\"):\\n print(\\\"Starting new quantification run for batch of %d samples from %s\\\"%(len(fastq_dict),input_folder))\\n mkdir_p(bsub_out)\\n #no subfolders needed for SRA data\\n print(\\\"bsub logs stored in %s folder\\\"%bsub_out)\\n mkdir_p(output_folder)\\n print(\\\"kallisto output in %s\\\"%output_folder)\\n for i in fastq_dict:\\n print(\\\"===processing fastq files from sample ID: %s===\\\"%i)\\n outdir = path.join(output_folder,i) #separate folder for each fastq, within the output folder\\n mkdir_p(outdir)\\n cmd = kw.CMD_BASE.format(fastq_id=i,bsub_out=bsub_out)\\n cmd = shlex.split(cmd) #convert to list of arguments\\n species = fastq_dict[i][\\\"species\\\"]\\n t_index = path.join(transcriptome_folder,kw.species2transcriptomeindex(species,kmer_size=species_kmers[species]))\\n f1 = [path.join(input_folder,r+\\\"_1.fastq.gz\\\") for r in fastq_dict[i][\\\"fastq_list\\\"]]\\n if fastq_dict[i][\\\"is_paired_end\\\"]:\\n f2 = [path.join(input_folder,r+\\\"_2.fastq.gz\\\") for r in fastq_dict[i][\\\"fastq_list\\\"]]\\n flist = \\\" \\\".join(imap(lambda x,y: x+\\\" \\\"+y,f1,f2))\\n cmd.append(\\\"kallisto quant -i {ti} -o {out} {flist}\\\".format(ti=t_index,out=outdir,flist = flist))\\n else: #case of single end reads\\n flen = median(array(fastq_dict[i][\\\"avgLengths\\\"]))\\n flist = \\\" \\\".join(f1)\\n cmd.append(\\\"kallisto quant --single -i {ti} -o {out} -l {flen} -s {fsd} {flist}\\\".format(ti=t_index,out=outdir,flen=flen,fsd=flen/5.0,flist = flist))\\n #note, fsd is the standard deviation of the fragment length distribution. flen/5 is just a placeholder. We should actually estimate this in the future!\\n #print(cmd)\\n subprocess.call(cmd)\",\n \"def Param_gen_and_write(self):\\n SF_start_params = self.source_df.groupby(\\n \\\"FinalID\\\").apply(self.start_params)\\n SF_start_params.reset_index(level=0, inplace=True)\\n self.source_df = pd.merge(\\n self.source_df,\\n SF_start_params,\\n on=\\\"FinalID\\\")\\n self.source_df.to_csv(\\\"../Data/Biotraits_with_start_params.csv\\\")\",\n \"def build_input_file(self, replica):\\n\\n file_name = self.inp_basename + \\\"_\\\" + \\\\\\n str(replica.id) + \\\"_\\\" + \\\\\\n str(replica.cycle) + \\\".md\\\"\\n\\n fo = open(file_name, \\\"wb\\\")\\n for i in range(1,500):\\n fo.write(str(random.randint(i, 500) + i*2.5) + \\\" \\\");\\n if i % 10 == 0:\\n fo.write(str(\\\"\\\\n\\\"));\\n fo.close()\",\n \"def run(self):\\n first_index, last_index = \\\\\\n self.get_initial_blocks()\\n while last_index - first_index > self.block_size:\\n first_index, last_index = \\\\\\n self.join_blocks(first_index, last_index)\\n self.merge_blocks(self.output_file_name, first_index, last_index)\",\n \"def build_batch_sampler(\\n self,\\n build_batch_sampler: dict,\\n target_dir: str,\\n cache_dir: str,\\n mode: str,\\n data_csv: str,\\n dataset: Dataset,\\n ):\\n\\n def _build_batch_sampler(\\n train: dict = None, valid: dict = None, test: dict = None\\n ):\\n if mode == \\\"train\\\":\\n return FixedBatchSizeBatchSampler(dataset, **train)\\n elif mode == \\\"valid\\\":\\n return FixedBatchSizeBatchSampler(dataset, **valid)\\n elif mode == \\\"test\\\":\\n return FixedBatchSizeBatchSampler(dataset, **test)\\n\\n return _build_batch_sampler(**build_batch_sampler)\",\n \"def gtf_processing(genome=None, prefix='gencov'):\\n all_bed = prefix + \\\".all.bed\\\"\\n\\n if not os.path.exists(all_bed) or os.stat(all_bed).st_size == 0:\\n log.info(\\\"Preprocessing annotation...\\\")\\n features = ('exon', 'gene', 'intron', 'intergenic')\\n merged_exons, merged_genes = map(preprocess, features[:2])\\n ins = {\\n 'intron': [merged_genes, merged_exons],\\n 'intergenic': [merged_genes, genome]\\n }\\n intron_bed, intergenic_bed = map(preprocess, features[2:], [ins, ins])\\n\\n log.info(\\\"Concatenate bed files for all elements...\\\")\\n with open(all_bed, 'w') as out_bed:\\n cat_all(merged_exons, merged_genes, intron_bed, intergenic_bed, out_bed=out_bed)\\n\\n for f in (merged_exons, merged_genes, intron_bed, intergenic_bed):\\n os.remove(f)\\n\\n return all_bed\",\n \"def get_batches(variants, batch_queue, header, vep=False, results_queue=None, \\n annotation_keyword = 'Annotation'):\\n\\n logger.debug(\\\"Set beginning to True\\\")\\n beginning = True\\n logger.debug(\\\"Create first empty batch\\\")\\n # A batch is a ordered dictionary with variants\\n batch = OrderedDict()\\n new_chrom = None\\n current_chrom = None\\n current_features = set()\\n chromosomes = []\\n\\n start_parsing_time = datetime.now()\\n start_chrom_time = start_parsing_time\\n start_twenty_time = start_parsing_time\\n \\n nr_of_variants = 0\\n nr_of_batches = 0\\n \\n header_line = header.header\\n vep_header = header.vep_columns\\n logger.info(\\\"Start parsing the variants\\\")\\n \\n for line in variants:\\n if not line.startswith('#'):\\n \\n variant = get_variant_dict(line, header_line)\\n variant_id = get_variant_id(variant)\\n variant['variant_id'] = variant_id\\n variant['info_dict'] = get_info_dict(variant['INFO'])\\n\\n if vep:\\n variant['vep_info'] = get_vep_dict(\\n vep_string=variant['info_dict']['CSQ'], \\n vep_header=vep_header,\\n allele=variant['ALT'].split(',')[0]\\n )\\n \\n logger.debug(\\\"Checking variant {0}\\\".format(variant_id))\\n\\n nr_of_variants += 1\\n new_chrom = variant['CHROM']\\n if new_chrom.startswith('chr'):\\n new_chrom = new_chrom[3:]\\n\\n logger.debug(\\\"Update new chrom to {0}\\\".format(new_chrom))\\n\\n new_features = get_annotation(\\n variant = variant, \\n vep = vep,\\n annotation_key = annotation_keyword\\n )\\n logger.debug(\\\"Adding {0} to variant {1}\\\".format(\\n ', '.join(new_features), variant_id\\n ))\\n\\n variant['annotation'] = new_features\\n\\n if nr_of_variants % 20000 == 0:\\n logger.info(\\\"{0} variants parsed\\\".format(nr_of_variants))\\n logger.info(\\\"Last 20.000 took {0} to parse.\\\".format(\\n str(datetime.now() - start_twenty_time)))\\n start_twenty_time = datetime.now()\\n\\n if beginning:\\n logger.debug(\\\"First variant.\\\")\\n current_features = new_features\\n\\n logger.debug(\\\"Adding %s to variant batch\\\" % variant_id)\\n batch[variant_id] = variant\\n\\n logger.debug(\\\"Updating current chrom to {0}\\\".format(new_chrom))\\n current_chrom = new_chrom\\n\\n chromosomes.append(current_chrom)\\n logger.debug(\\\"Adding chr {0} to chromosomes\\\".format(new_chrom)) \\n\\n beginning = False\\n logger.debug(\\\"Updating beginning to False\\\")\\n\\n else:\\n # If we should put the batch in the queue:\\n logger.debug(\\\"Updating send to True\\\") \\n send = True\\n \\n # Check if the variant ovelapps any features\\n if len(new_features) != 0:\\n # Check if the features overlap the previous variants features\\n if new_features.intersection(current_features):\\n logger.debug(\\\"Set send to False since variant features overlap\\\") \\n send = False\\n # If we are at a new chromosome we finish the current batch:\\n if new_chrom != current_chrom:\\n if current_chrom not in chromosomes:\\n chromosomes.append(current_chrom)\\n logger.debug(\\\"Adding chr {0} to chromosomes\\\".format(new_chrom)) \\n # New chromosome means new batch\\n send = True\\n logger.info(\\\"Chromosome {0} parsed. Time to parse\\\"\\\\\\n \\\" chromosome: {1}\\\".format(\\n current_chrom, datetime.now()-start_chrom_time))\\n start_chrom_time = datetime.now()\\n current_chrom = new_chrom\\n\\n if send:\\n # Put the job in the queue\\n if len(batch) > 0:\\n logger.debug(\\\"Adding batch in queue\\\")\\n batch_queue.put(batch)\\n nr_of_batches += 1\\n #Reset the variables\\n current_features = new_features\\n logger.debug(\\\"Initializing empty batch\\\") \\n batch = {}\\n else:\\n current_features = current_features.union(new_features)\\n \\n # Add variant to batch\\n batch[variant_id] = variant\\n\\n if current_chrom not in chromosomes:\\n logger.debug(\\\"Adding chr {0} to chromosomes\\\".format(current_chrom))\\n chromosomes.append(current_chrom)\\n\\n logger.info(\\\"Chromosome {0} parsed. Time to parse\\\"\\\\\\n \\\" chromosome: {0}\\\".format(\\n current_chrom, datetime.now()-start_chrom_time))\\n\\n if len(batch) > 0:\\n nr_of_batches += 1\\n batch_queue.put(batch)\\n logger.debug(\\\"Adding batch to queue\\\") \\n\\n logger.info(\\\"Variants parsed. Time to parse variants: {0}\\\".format(\\n str(datetime.now() - start_parsing_time)\\n ))\\n\\n logger.info(\\\"Number of variants in variant file: {0}\\\".format(nr_of_variants))\\n logger.info(\\\"Number of batches created: {0}\\\".format(nr_of_batches))\\n\\n return chromosomes\",\n \"def combine(self):\\n\\n import re\\n \\n print 'Creating file', self.__filename\\n \\n bname = (os.path.split(self.__filename))[1]\\n bname2 = bname\\n \\n # bugfix: if file contains characters like +,.,[]\\n # properly escape them, otherwise re will fail to match.\\n for a, b in zip(['+', '.', '[', ']','$', '(', ')'],\\n ['\\\\+','\\\\.','\\\\[','\\\\]','\\\\$', '\\\\(', '\\\\)']):\\n bname2 = bname2.replace(a, b)\\n \\n chunkre = re.compile(bname2 + '-' + '[0-9]+')\\n \\n chunkfiles = []\\n for f in os.listdir(\\\".\\\"):\\n print f\\n if chunkre.match(f):\\n chunkfiles.append(f)\\n\\n\\n print 'Number of chunks', len(chunkfiles), '\\\\n'\\n chunkfiles.sort(self.sort_index)\\n\\n data=''\\n for f in chunkfiles:\\n\\n try:\\n print 'Appending chunk', os.path.join(\\\".\\\", f)\\n data += open(f, 'rb').read()\\n except (OSError, IOError, EOFError), e:\\n print e\\n continue\\n\\n try:\\n f = open(bname, 'wb')\\n f.write(data)\\n f.close()\\n except (OSError, IOError, EOFError), e:\\n raise FileSplitterException, str(e)\\n\\n print 'Wrote file', bname\",\n \"def go(self):\\n\\n self._write_master()\\n num_fofs = self.fofs['fofid'].max()\\n fof_splits = split.get_splits(num_fofs, self['chunksize'])\\n\\n njobs=0\\n fobj=None\\n\\n icondor=0\\n for isplit,fof_split in enumerate(fof_splits):\\n if njobs % self['jobs_per_sub']==0:\\n if fobj is not None:\\n fobj.close()\\n fobj = self._open_condor_script(icondor)\\n icondor += 1\\n\\n self._write_split(fobj, isplit, fof_split)\\n\\n njobs += 1\",\n \"def summarize(self):\\n self.smalltalk += \\\"\\\\n Data IDs in this bundle: \\\\n\\\"\\n self._files = {}\\n inv_dict = {}\\n # sort IDs to make sure pdfs are printed in same oder as they were\\n # taken\\n for k, v in self.stage_summaries.items():\\n for qc_id in flatten_list(v):\\n inv_dict[qc_id] = k\\n sorted_ids = list(flatten_list(self.stage_summaries.values()))\\n sorted_ids.sort(key=int)\\n # for stage, value in self.stage_summaries.items():\\n for qc_run_id in sorted_ids:\\n # stage = inv_dict[qc_run_id]\\n # if stage[0:7] == 'failed_':\\n # stage = stage[7:]\\n # try:\\n # s = self.comments[qc_run_id]\\n # except KeyError:\\n # s = ''\\n # self.comments[qc_run_id] = 'Classified as poor result.\\\\n' + s\\n ds = Dataset(qc_run_id, self.db_name)\\n device_name = ds.device_name\\n f_folder = os.path.join(self.db_folder, \\\"tuning_results\\\", device_name)\\n # for qc_run_id in flatten_list(value):\\n self.smalltalk += str(qc_run_id) + \\\", \\\"\\n\\n # filename = stage + '_fit_ds'\\n # filename += str(qc_run_id) + '.png'\\n filename = os.path.join(f_folder, str(ds.ds.guid) + \\\".png\\\")\\n\\n self._files[str(qc_run_id)] = filename\",\n \"def stim_build(stim_file, random_prototype_selection):\\n\\n\\t# Open training file and turn each row into a list of integers instead of strings. Count the number of stimulus (rows) there are in the file. \\n\\twith open(stim_file, encoding='utf-8-sig') as sf:\\n\\t\\tsf = csv.reader(sf)\\n\\t\\trows = list(sf)\\n\\t\\tcount = (list(range(len(rows))))\\n\\n\\t# Make a new list that contains a list for each row in the stimlus file as integers instead of strings.\\n\\t\\tstimulus_list = []\\n\\t\\tfor i in count:\\n\\t\\t\\tnew_row = []\\n\\t\\t\\tfor element in rows[i]:\\n\\t\\t\\t\\tnew_row.append(int(element))\\n\\t\\t\\tstimulus_list.append(new_row)\\n\\n\\t# create a list of all 1's to compare to the randomly selected prototype later\\n\\trandpro = random_prototype_selection\\n\\tall1proto = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\\n\\n\\t# Compare the randomly selected prototype to the all 1's. If it is the same, then take the stimulus file as is and add the necessary columns.\\n\\tif random_prototype_selection == all1proto:\\n\\t\\n\\t\\tstim_id = []\\n\\t\\tsums_feat = []\\n\\t\\tcategory = []\\n\\t\\tcorrect_key = []\\n\\t\\tfor item in stimulus_list:\\n\\t\\t\\t# calculate the sum of the number of features each stimulus has in common with the protoype\\n\\t\\t\\tsums = sum(item)\\n\\t\\t\\tsums_feat.append(sums)\\n\\n\\t\\t\\t# define the categories based on sum_feat \\n\\t\\t\\tif sums > 5:\\n\\t\\t\\t\\tcat = 'batmans'\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcat = 'robins'\\n\\n\\t\\t# define which key is correct based on the category it is assigned\\n\\t\\t\\tif cat == 'batmans':\\n\\t\\t\\t\\tkey_corr = 'f'\\n\\t\\t\\telif cat == 'robins':\\n\\t\\t\\t\\tkey_corr = 'j'\\n\\t\\t\\tcorrect_key += [key_corr]\\n\\n\\t\\t# append cat to category. Have to use different operation because .append doesn't work for strings\\n\\t\\t\\tcategory += [cat]\\n\\n\\t\\t\\t# now that all the manipulations are done, we can to concatenate the recoded stimuli to match the file names\\n\\t\\t\\tstring = ''.join([str(item) for item in item])\\n\\t\\t\\t# added quotations so that the leading zeros are not removed when writing to the csv file. Will take them off in psychopy\\n\\t\\t\\tstim_id += [string]\\n\\t\\t\\n\\t# If the randomly selected prototype is not all 1's then we need to recode the stimulus and add the necessary rows. \\n\\telse:\\n\\t\\t# get the number of stimuli in the stim_file and create a list of the range of numbers.\\n\\t\\tcount_stim_list = list(range(len(stimulus_list)))\\n\\t\\t\\n\\t\\t# count the number of items in the prototype and create a list for the range of numbers. Should always be 10 for the 10 features.\\n\\t\\tcount_randpro = (list(range(len(randpro))))\\n\\t\\n\\t\\t# create empty list for each column we want in our csv\\n\\t\\tcomparison = []\\n\\t\\tstim_id = []\\n\\t\\tsums_feat = []\\n\\t\\tcategory = []\\n\\t\\tcorrect_key = []\\n\\n\\t\\t# make a new variable defining the stimulus we are currently working with as \\\"stim\\\"\\n\\t\\tfor i in count_stim_list:\\n\\t\\t\\tstim = stimulus_list[i]\\n\\n\\t\\t\\t# create another empty list to store the comparisons between the prototype and the current stimulus\\n\\t\\t\\tnew_code = []\\n\\t\\t\\tfor n in count_randpro:\\n\\t\\t\\t\\tif stim[n] == randpro[n]:\\n\\t\\t\\t\\t\\toutput = bool('True')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\toutput = bool('')\\n\\n\\t\\t\\t\\t# turn the boolean phrases into into integers for the new code. Append each item to the rest of the items within the stimulus\\n\\t\\t\\t\\tnew_code.append(output * 1)\\n\\n\\t\\t\\t# Add the recoded stimuli into the same list. We only need this list to check that the recode worked. It won't be going in the csv\\n\\t\\t\\tcomparison.append(new_code)\\n\\n\\t\\t\\t# compare the new code to the stimuli to get the number of features that match so we can add them up and determine the category\\n\\t\\t\\tcompare_cat = []\\n\\t\\t\\tfor x in count_randpro:\\n\\t\\t\\t\\tif new_code[x] == randpro[x]:\\n\\t\\t\\t\\t\\toutput = bool('True')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\toutput = bool('')\\n\\t\\t\\t\\tcompare_cat.append(output*1)\\n\\n\\t\\t\\t# find a sum of the number of features each stimulus has in common with the prototype\\n\\t\\t\\tsums = sum(compare_cat)\\n\\t\\t\\tsums_feat.append(sums)\\n\\t\\t\\n\\t\\t\\t# define the categories based on the number of features each stimulus has in common with the prototype\\n\\t\\t\\tif sums > 5:\\n\\t\\t\\t\\tcat = 'batmans'\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcat = 'robins'\\n\\t\\t\\tcategory += [cat]\\n\\n\\t\\t\\t# define which key is correct based on the category it is assigned\\n\\t\\t\\tif cat == 'batmans':\\n\\t\\t\\t\\tkey_corr = 'f'\\n\\t\\t\\telif cat == 'robins':\\n\\t\\t\\t\\tkey_corr = 'j'\\n\\t\\t\\tcorrect_key += [key_corr]\\n\\n\\t\\t\\t# now that all the manipulations are done, we can to concatenate the recoded stimuli to match the file names\\n\\t\\t\\tstring = ''.join([str(item) for item in new_code])\\n\\t\\t\\t# added quotations so that the leading zeros are not removed when writing to the csv file. Will take them off in psychopy\\n\\t\\t\\tstim_id += [string]\\n\\t\\t\\n\\t# write the results to a new csv file. \\n\\twith open('/Volumes/shares/Cabi/exp/joplin/joplin1/data/_stimfile.csv', 'w', newline='') as stim_file:\\n\\t\\twriter = csv.writer(stim_file)\\n\\t\\twriter.writerow(('stim_id', 'sums_feat', 'category', 'correct_key'))\\n\\t\\tfor i in range(len(stim_id)):\\n\\t\\t\\twriter.writerow((stim_id[i], sums_feat[i], category[i], correct_key[i]))\",\n \"def make_master_flats(dc):\\n\\n\\t## Make EXTcheck: is there always the same number of extensions in each file\\n\\tprint \\\"Making master flats\\\"\\n\\t\\n\\t## Choose extensions you are using\\n\\t\\n\\tfor flat_type in ['FFS']: # Currently FFD is unsupported. If you have FFDs, add them to the list but you must have ONLY FFDs or ONLY FFSs in the dir. Otherwise the first element in the list will get overwritten!\\n\\t\\t#~ print \\\"\\\\n\\\", flat_type, \\\"\\\\n\\\"\\n\\t\\tfor i in dc:\\n\\t\\t\\tTRIM, TRIM1, VR, PS, PS1, OS, OS1 = CCD_sections((i[0], i[1]))\\n\\t\\t\\tfilelist = []\\n\\t\\t\\tfor f in glob.glob(RAW+'*'+flat_type+'*fits'):\\n\\t\\t\\t\\tccd_conf = []\\n\\t\\t\\t\\theader0 = fits.getheader(f)\\n\\t\\t\\t\\theader1 = fits.getheader(f, ext=1)\\n\\t\\t\\t\\tif header0['OBSMODE']==flat_type:\\n\\t\\t\\t\\t\\tfor KW in ['BINX', 'BINY']:\\n\\t\\t\\t\\t\\t\\tccd_conf.append(header0[KW])\\n\\t\\t\\t\\t\\tfor KW in ['NAXIS1', 'NAXIS2']:\\n\\t\\t\\t\\t\\t\\tccd_conf.append(header1[KW])\\n\\t\\t\\t\\t\\t\\tif tuple(ccd_conf)==i:\\n\\t\\t\\t\\t\\t\\t\\tfilelist.append(f)\\n\\t\\t\\tlfl = len(filelist)\\n\\t\\t\\tif lfl > 0:\\n\\t\\t\\t\\tBIN=CD+'/'+str(i[0])+'x'+str(i[1])+'/'\\n\\t\\t\\t\\tWD=BIN+str(i[-2])+'x'+str(i[-1])+'/' # Bottom level dir with calibrated and master frames\\n\\t\\t\\t\\tB=check_exist(WD, 'MF.fits', i)\\n\\t\\t\\t\\tif B=='n':\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\thdul = fits.HDUList()\\n\\t\\t\\t\\t\\thdul.append(fits.ImageHDU())\\n\\t\\t\\t\\t\\t#~ MB = fits.open(WD+'MB.fits')\\n\\t\\t\\t\\t\\tx = np.array(range(0,i[-1]))\\n\\t\\t\\t\\t\\tfor EXT in (extensions):\\n\\t\\t\\t\\t\\t\\tprint \\\"##################################################\\\"\\n\\t\\t\\t\\t\\t\\tprint \\\"Stacking \\\"+`lfl`+' '+`i[-2]`+'x'+`i[-1]`+' channel '+`EXT`+' flat frames!'\\n\\t\\t\\t\\t\\t\\tif EXT==1:\\n\\t\\t\\t\\t\\t\\t\\tPSC=PS1\\n\\t\\t\\t\\t\\t\\t\\tOSC=OS1\\n\\t\\t\\t\\t\\t\\t\\tTR=TRIM1\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tPSC=PS1\\n\\t\\t\\t\\t\\t\\t\\tOSC=OS\\n\\t\\t\\t\\t\\t\\t\\tTR=TRIM\\n\\t\\t\\t\\t\\t\\tsc = -1 # counts how many flats have mean>limit\\n\\t\\t\\t\\t\\t\\tfor n, fn in enumerate(filelist):\\n\\t\\t\\t\\t\\t\\t\\tprint \\\"Files left:\\\",`lfl-n`+'/'+`lfl`\\n\\t\\t\\t\\t\\t\\t\\tim = fits.getdata(fn, ext=EXT)\\n\\t\\t\\t\\t\\t\\t\\tmeanval = np.mean(im[VR[0]:VR[1], TR[0]:TR[1]])\\n\\t\\t\\t\\t\\t\\t\\t#~ maxval = np.max(im[VR[0]:VR[1], TR[0]:TR[1]])\\n\\t\\t\\t\\t\\t\\t\\tmaxval = stats.scoreatpercentile(im[VR[0]:VR[1], TR[0]:TR[1]], 90)\\n\\t\\t\\t\\t\\t\\t\\texptime = fits.getheader(fn)['EXPTIME']\\n\\t\\t\\t\\t\\t\\t\\t#~ if meanval > 15000. and meanval < 40000. and maxval < 50000. and exptime>5.:\\n\\t\\t\\t\\t\\t\\t\\tif meanval > 16000. and meanval < 40000. and exptime>=5.:\\n\\t\\t\\t\\t\\t\\t\\t\\tsc+=1\\n\\t\\t\\t\\t\\t\\t\\t\\t#~ im[im<1]=1\\n\\t\\t\\t\\t\\t\\t\\t\\tmscrow, sigmarow = median_row(OSC, PSC, TR, im)\\n\\t\\t\\t\\t\\t\\t\\t\\tsh = np.shape(im)\\n\\t\\t\\t\\t\\t\\t\\t\\tfor y in range(0, sh[0]):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tim[y] = im[y]-mscrow[y]\\n\\t\\t\\t\\t\\t\\t\\t\\tF=im\\n\\t\\t\\t\\t\\t\\t\\t\\tnorm = np.median(F[VR[0]:VR[1], TR[0]:TR[1]])\\n\\t\\t\\t\\t\\t\\t\\t\\tF = F/norm #+np.min(F)+0.0001\\n\\t\\t\\t\\t\\t\\t\\t\\tif sc==0:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tstack_arr = F\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tstack_arr = np.dstack((stack_arr, F))\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"****************************************\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"Rejected\\\", fn, \\\"AVG =\\\", meanval, \\\"EXPTIME =\\\", exptime\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"****************************************\\\"\\n\\t\\t\\t\\t\\t\\tprint 'Will stack a total of', np.shape(stack_arr)[2], 'flats'\\n\\t\\t\\t\\t\\t\\tMF = np.median(stack_arr, axis=2)\\n\\t\\t\\t\\t\\t\\thdul.append(fits.ImageHDU(MF))\\n\\t\\t\\t\\t\\t\\thdul[EXT].header.set(\\\"NAXIS1\\\", np.shape(MF)[1])\\n\\t\\t\\t\\t\\t\\thdul[EXT].header.set(\\\"NAXIS2\\\", np.shape(MF)[0])\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"CALIBR\\\", \\\"T\\\")\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"INSTRUME\\\", \\\"MAIA\\\")\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"BINX\\\", i[0])\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"BINY\\\", i[1])\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"CALMODE\\\", \\\"MASTER FLAT\\\")\\n\\t\\t\\t\\t\\thdul.writeto(WD+\\\"MF.fits\\\", clobber=True)\\n\\t\\t\\t\\t\\tprint \\\"############################################################\\\"\\n\\tprint \\\"Completed master flats\\\"\",\n \"def build_indices(genome_fasta, genome_gtf, rRNA_fasta, transcriptome_fasta):\\n \\n if not os.path.exists(\\\"data/indices\\\"):\\n os.mkdir(\\\"data/indices\\\")\\n\\n \\n # 1. Bowtie index\\n print(\\\"Building Bowtie index\\\")\\n if not os.path.exists(BOWTIE_DIR):\\n os.mkdir(BOWTIE_DIR)\\n cmd_bowtie = 'bowtie-build' + ' ' + genome_fasta + ' ' + BOWTIE_DIR+'/yeast'\\n output = subprocess.run(cmd_bowtie, shell=True)\\n\\n cmd_rRNA = 'bowtie-build' + ' ' + rRNA_fasta + ' ' + BOWTIE_DIR+'/rRNA'\\n output = subprocess.run(cmd_rRNA, shell=True)\\n \\n # 2. STAR index\\n print(\\\"Building STAR index\\\")\\n if not os.path.exists(STAR_DIR):\\n os.mkdir(STAR_DIR)\\n cmd_STAR = 'STAR' + ' ' + '--runThreadN' + ' ' + '4' + ' ' + '--runMode' + ' ' + 'genomeGenerate' + ' ' + '--genomeDir' + ' ' + STAR_DIR + ' ' + '--genomeFastaFiles' + ' ' + genome_fasta + ' ' + '--sjdbGTFfile' + ' ' + genome_gtf #+ ' ' + '--sjdbOverhang' + ' ' + 'max(ReadLength)-1'\\n output = subprocess.run(cmd_STAR, shell=True)\\n\\n\\n# run build transcriptome fasta. \\n if not os.path.exists(STAR_TRANSCRIPTOME_DIR):\\n os.mkdir(STAR_TRANSCRIPTOME_DIR)\\n cmd_STAR = 'STAR' + ' ' + '--runThreadN' + ' ' + '4' + ' ' + '--runMode' + ' ' + 'genomeGenerate' + ' ' + '--genomeDir' + ' ' + STAR_TRANSCRIPTOME_DIR + ' ' + '--genomeFastaFiles' + ' ' + transcriptome_fasta # + ' ' + '--sjdbGTFfile' + ' ' + genome_gtf #+ ' ' + '--sjdbOverhang' + ' ' + 'max(ReadLength)-1'\\n output = subprocess.run(cmd_STAR, shell=True)\",\n \"def batch_apply(self, batch, is_train=False, stats=None, **kwargs):\\n if self.max_context == 0:\\n return batch\\n trf_batch = []\\n doc = {}\\n doc[\\\"src\\\"] = []\\n doc[\\\"tgt\\\"] = []\\n doc[\\\"indices\\\"] = 0\\n\\n for ex, _, cid in batch:\\n if ex[\\\"tgt\\\"] is not None:\\n cur_len = max(len(doc[\\\"src\\\"] + ex[\\\"src\\\"]), len(doc[\\\"tgt\\\"] + ex[\\\"tgt\\\"]))\\n\\n if len(ex[\\\"src\\\"]) == 0 and len(ex[\\\"tgt\\\"]) == 0:\\n # doc break we add it, restart new doc\\n trf_batch.append((doc, self, cid))\\n doc = {}\\n doc[\\\"src\\\"] = []\\n doc[\\\"tgt\\\"] = []\\n doc[\\\"indices\\\"] = ex[\\\"indices\\\"]\\n elif cur_len > self.doc_length:\\n if len(doc[\\\"src\\\"]) == 0:\\n # case 1st ex is already longer\\n trf_batch.append((ex, self, cid))\\n else:\\n # adding cur ex is too long we add cur doc\\n # and reset doc to cur ex\\n trf_batch.append((doc, self, cid))\\n doc = copy.deepcopy(ex)\\n else:\\n if len(doc[\\\"src\\\"]) == 0:\\n # we start the new doc with cur ex\\n doc = copy.deepcopy(ex)\\n else:\\n # we cumulate cur ex to cur doc\\n doc[\\\"src\\\"] += [DefaultTokens.SEP] + ex[\\\"src\\\"]\\n doc[\\\"src_original\\\"] += [DefaultTokens.SEP] + ex[\\\"src_original\\\"]\\n doc[\\\"tgt\\\"] += [DefaultTokens.SEP] + ex[\\\"tgt\\\"]\\n doc[\\\"tgt_original\\\"] += [DefaultTokens.SEP] + ex[\\\"tgt_original\\\"]\\n nb_ctx = doc[\\\"src\\\"].count(DefaultTokens.SEP)\\n if nb_ctx >= self.max_context:\\n trf_batch.append((doc, self, cid))\\n doc = {}\\n doc[\\\"src\\\"] = []\\n doc[\\\"tgt\\\"] = []\\n doc[\\\"indices\\\"] = ex[\\\"indices\\\"]\\n else:\\n cur_len = len(doc[\\\"src\\\"] + ex[\\\"src\\\"])\\n doc[\\\"tgt\\\"] = None\\n if len(ex[\\\"src\\\"]) == 0:\\n trf_batch.append((doc, self, cid))\\n doc = {}\\n doc[\\\"src\\\"] = []\\n doc[\\\"indices\\\"] = ex[\\\"indices\\\"]\\n elif cur_len > self.doc_length:\\n if len(doc[\\\"src\\\"]) == 0:\\n trf_batch.append((ex, self, cid))\\n else:\\n trf_batch.append((doc, self, cid))\\n doc = copy.deepcopy(ex)\\n else:\\n if len(doc[\\\"src\\\"]) == 0:\\n doc = copy.deepcopy(ex)\\n else:\\n doc[\\\"src\\\"] += [DefaultTokens.SEP] + ex[\\\"src\\\"]\\n doc[\\\"src_original\\\"] += [DefaultTokens.SEP] + ex[\\\"src_original\\\"]\\n nb_ctx = doc[\\\"src\\\"].count(DefaultTokens.SEP)\\n if nb_ctx >= self.max_context:\\n trf_batch.append((doc, self, cid))\\n doc = {}\\n doc[\\\"src\\\"] = []\\n doc[\\\"indices\\\"] = ex[\\\"indices\\\"]\\n if len(doc[\\\"src\\\"]) > 0:\\n trf_batch.append((doc, self, cid))\\n return trf_batch\",\n \"def compress_feats(self):\\n mkpath(os.path.join(self.base_update_path,'comp_features'))\\n mkpath(os.path.join(self.base_update_path,'comp_idx'))\\n args = [self.base_update_path+'/', str(self.features_dim), '1', self.master_update_file, str(self.bits_num)]\\n subprocess_command = [self.hashing_execpath+\\\"compress_feats\\\"] + args\\n # this will work only if features to be compressed are present in self.base_update_path/features\\n proc = subprocess.Popen(subprocess_command, stdout=subprocess.PIPE)\\n print \\\"[HasherSwig.compress_feats: log] running command: {}\\\".format(subprocess_command)\\n (out, err) = proc.communicate()\\n print \\\"[HasherSwig.compress_feats: log] program output:\\\", out\\n print \\\"[HasherSwig.compress_feats: log] program error:\\\", err\",\n \"def build_job_scripts(model_list, scenario_list, output_dir, cassandra_config_dir, cassandra_log_dir,\\n cassandra_main_script, sbatch_account, sbatch_partition='slurm', sbatch_walltime='01:00:00',\\n sbatch_ntasks=3, sbatch_nodes=3, sbatch_jobname='cassie', sbatch_logdir='.', template=None):\\n\\n # use default configuration template file if user does not give one\\n if template is None:\\n template = pkg_resources.resource_filename('cassie', 'data/sbatch_template.sh')\\n\\n # existing tags to replace in the template file\\n model_tag = '<model>'\\n scenario_tag = '<scenario>'\\n account_tag = '<account>'\\n partition_tag = '<partition>'\\n ntasks_tag = '<ntasks>'\\n nodes_tag = '<nodes>'\\n time_tag = '<walltime>'\\n jobname_tag = '<jobname>'\\n logdir_tag = '<logdir>'\\n cassandra_configdir_tag = '<cassconfigdir>'\\n cassandra_logdir_tag = '<casslogdir>'\\n cassandra_script_tag = '<cassmainscript>'\\n\\n for model in model_list:\\n for scenario in scenario_list:\\n\\n output_file = os.path.join(output_dir, f'run_{model.lower()}_{scenario}.sh')\\n\\n with open(output_file, 'w') as out:\\n with open(template) as get:\\n\\n f = get.read()\\n\\n # replace tag names with dynamic content\\n fx = f.replace(model_tag, model)\\n fx = fx.replace(scenario_tag, scenario)\\n\\n fx = fx.replace(account_tag, sbatch_account)\\n fx = fx.replace(partition_tag, sbatch_partition)\\n fx = fx.replace(ntasks_tag, str(sbatch_ntasks))\\n fx = fx.replace(nodes_tag, str(sbatch_nodes))\\n fx = fx.replace(time_tag, sbatch_walltime)\\n fx = fx.replace(jobname_tag, sbatch_jobname)\\n fx = fx.replace(logdir_tag, sbatch_logdir)\\n\\n fx = fx.replace(cassandra_configdir_tag, cassandra_config_dir)\\n fx = fx.replace(cassandra_logdir_tag, cassandra_log_dir)\\n fx = fx.replace(cassandra_script_tag, cassandra_main_script)\\n\\n out.write(fx)\",\n \"def build_index(fasta_fp, index_fp):\\n subprocess.call([\\\" \\\".join([\\\"bowtie2-build\\\", fasta_fp, index_fp])],\\n shell=True)\",\n \"def gappyBlocks():\\n path = \\\"./data/\\\"\\n for file in os.listdir(path):\\n if file.endswith(\\\".fa\\\") or file.endswith(\\\".fasta\\\"):\\n alignin = AlignIO.read(path + file, \\\"fasta\\\")\\n try:\\n filecore = file.rstrip(\\\".fa\\\")\\n except:\\n filecore = file.rstrip(\\\".fasta\\\")\\n fileout = path + filecore + \\\".blocks\\\"\\n \\n # constants\\n align = []\\n gap = []\\n border = []\\n blocks = []\\n \\n # specify cut-off of gaps in column (in percentage)\\n cut_min = 0.1\\n cut_max = 0.9\\n \\n # alignment\\n for pos in range(0,(alignin.get_alignment_length())):\\n column=alignin[:,pos]\\n align.append(column)\\n if \\\"-\\\" in column:\\n col=list(column)\\n gaps=col.count(\\\"-\\\")\\n if gaps > (cut_min*len(col)) and gaps < (cut_max*len(col)):\\n gap.append(pos)\\n \\n if gap != []:\\n border.append(gap[0])\\n border.append(gap[len(gap)-1])\\n for i in range(0,(len(gap)-1)):\\n if int(gap[i]+1)!=int(gap[i+1]):\\n border.append(gap[i])\\n \\n for j in range((len(gap)-1), 0, -1):\\n if int(gap[j]-1)!=int(gap[j-1]):\\n border.append(gap[j])\\n # list of positions for the blocks\\n order=sorted(border)\\n \\n # get the blocks and writes to the .blocks file\\n o=open(fileout, \\\"w\\\")\\n \\n for i in range(0,len(order)-1,2):\\n beg=int(order[i])\\n end=int(order[i+1])\\n count = end-beg \\n block=alignin[:,beg:end]\\n \\n # specify the minimum length of a gap\\n if count < 3:\\n pass\\n else: \\n blocks.append(block) \\n o.write('***Block***'+\\\"\\\\n\\\"+\\\"Start:\\\"+str(beg)+\\\\\\n \\\"\\\\n\\\"+\\\"Count:\\\"+str(count)+\\\"\\\\n\\\")\\n for record in block:\\n o.write(str(record.seq)+\\\"\\\\n\\\")\\n o.close()\\n else:\\n o=open(fileout, \\\"w\\\")\\n o.close()\\n pass\\n return\",\n \"def write_pbs(self):\\n fout = open(\\\"runStarCCM.pbs\\\", \\\"w\\\")\\n fout.write(\\\"#PBS -S /bin/csh\\\\n\\\")\\n fout.write(\\\"#PBS -l select=\\\" + str(self.numNodes) + \\\":ncpus=\\\" + str(self.numCPUs) + \\\":mpiprocs=\\\" + str(self.mpiProcs) + \\\":model=has,walltime=\\\" + self.WallTime + \\\"\\\\n\\\\n\\\")\\n fout.write(\\\"#PBS -W group_list=\\\" + self.GroupID + \\\"\\\\n\\\")\\n fout.write(\\\"#PBS -j oe\\\\n\\\")\\n fout.write(\\\"#PBS -q \\\" + self.queue + \\\"\\\\n\\\")\\n fout.write(\\\"#PBS -N \\\" + self.jobName + \\\"\\\\n\\\")\\n fout.write(\\\"#PBS -m e\\\\n\\\")\\n fout.write(\\\"#PBS -W block=true\\\\n\\\\n\\\")\\n fout.write(\\\"cd $PBS_O_WORKDIR\\\\n\\\")\\n\\n if self.runVolGrid == 1:\\n #fout.write(\\\"/bin/rm -f \\\" + self.simMeshFile + \\\".sim\\\\n\\\")\\n fout.write(\\\"/bin/rm -f starccmMeshRun.out\\\\n\\\")\\n fout.write(\\\"chmod u+x \\\" + self.cshBatch1File + \\\".csh\\\\n\\\")\\n # do not use >>& because it will fail in some environment\\n fout.write(\\\"./\\\" + self.cshBatch1File + \\\".csh -powerOnDemand \\\" + self.javaBatch1File + \\\".java >& starccmMeshRun.out\\\\n\\\\n\\\")\\n else:\\n fout.write(\\\"echo 'User chooses not to make a mesh run.'\\\\n\\\")\\n\\n if self.runCFD == 1:\\n fout.write(\\\"chmod u+x \\\" + self.cshBatch2File + \\\".csh\\\\n\\\")\\n fout.write(\\\"/bin/rm -f *.csv *.png starccmFlowRun.out\\\\n\\\")\\n # do not use >>& because it will fail in some environment\\n fout.write(\\\"./\\\" + self.cshBatch2File + \\\".csh -powerOnDemand \\\" + self.javaBatch2File + \\\".java \\\" + self.simMeshFile + \\\" >& starccmFlowRun.out\\\\n\\\\n\\\")\\n fout.write(\\\"# rename the strange file names\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDForceX.csv ForceX.csv\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDForceY.csv ForceY.csv\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDForceZ.csv ForceZ.csv\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDMomentX.csv MomentX.csv\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDMomentY.csv MomentY.csv\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDMomentZ.csv MomentZ.csv\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDResiduals.csv Residuals.csv\\\\n\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDForceX.png ForceX.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDForceY.png ForceY.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDForceZ.png ForceZ.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDMomentX.png MomentX.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDMomentY.png MomentY.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDMomentZ.png MomentZ.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDResiduals.png Residuals.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDUpperCp.png UpperCp.png\\\\n\\\")\\n fout.write(\\\"/bin/mv \\\\$PWDLowerCp.png LowerCp.png\\\\n\\\")\\n fout.write(\\\"/bin/rm -rf null\\\\n\\\")\\n else:\\n fout.write(\\\"echo 'User chooses not to make a CFD run.'\\\\n\\\")\\n\\n fout.close()\",\n \"def get_batch_data(cur_samples, is_training=False):\\n\\tbatch_src_max_len, batch_trg_max_len = get_max_len(cur_samples)\\n\\tbatch_trg_max_len += 1\\n\\tsrc_words_list = list()\\n\\tsrc_words_mask_list = list()\\n\\tsrc_char_seq = list()\\n\\tdecoder_input_list = list()\\n\\tsrc_pos_seq = list()\\n\\tsrc_dep_seq = list()\\n\\tsrc_loc_seq = list()\\n\\targ1sweights = []\\n\\targ1eweights = []\\n\\targ2sweights = []\\n\\targ2eweights = []\\n\\n\\trel_seq = list()\\n\\targ1_start_seq = list()\\n\\targ1_end_seq = list()\\n\\targ2_start_seq = list()\\n\\targ2_end_seq = list()\\n\\ttarget_vec_seq = []\\n\\ttarget_vec_mask_seq = []\\n\\n\\tfor sample in cur_samples:\\n\\t\\tsrc_words_list.append(get_words_index_seq(sample.SrcWords, batch_src_max_len))\\n\\t\\tsrc_words_mask_list.append(get_padded_mask(sample.SrcLen, batch_src_max_len))\\n\\t\\tsrc_char_seq.append(get_char_seq(sample.SrcWords, batch_src_max_len))\\n\\t\\tsrc_pos_seq.append(get_pos_index_seq(sample.SrcWords, batch_src_max_len))\\n\\t\\tsrc_dep_seq.append(get_dep_index_seq(sample.SrcWords, batch_src_max_len))\\n\\t\\tsrc_loc_seq.append([i+1 for i in range(len(sample.SrcWords))] +\\n\\t\\t\\t\\t\\t\\t [0 for i in range(batch_src_max_len - len(sample.SrcWords))])\\n\\n\\t\\tif is_training:\\n\\t\\t\\targ1_start_seq.append(get_padded_pointers(sample.TrgPointers, 0, batch_trg_max_len))\\n\\t\\t\\targ1_end_seq.append(get_padded_pointers(sample.TrgPointers, 1, batch_trg_max_len))\\n\\t\\t\\targ2_start_seq.append(get_padded_pointers(sample.TrgPointers, 2, batch_trg_max_len))\\n\\t\\t\\targ2_end_seq.append(get_padded_pointers(sample.TrgPointers, 3, batch_trg_max_len))\\n\\t\\t\\targ1sweights.append(get_pointer_location(sample.TrgPointers, 0, batch_src_max_len, batch_trg_max_len))\\n\\t\\t\\targ1eweights.append(get_pointer_location(sample.TrgPointers, 1, batch_src_max_len, batch_trg_max_len))\\n\\t\\t\\targ2sweights.append(get_pointer_location(sample.TrgPointers, 2, batch_src_max_len, batch_trg_max_len))\\n\\t\\t\\targ2eweights.append(get_pointer_location(sample.TrgPointers, 3, batch_src_max_len, batch_trg_max_len))\\n\\t\\t\\trel_seq.append(get_relation_index_seq(sample.TrgRels, batch_trg_max_len))\\n\\t\\t\\tdecoder_input_list.append(get_relation_index_seq(sample.TrgRels, batch_trg_max_len))\\n\\t\\t\\ttarget_vec_seq.append(get_target_vec(sample.TrgPointers, sample.TrgRels, batch_src_max_len))\\n\\t\\t\\ttarget_vec_mask_seq.append([0 for i in range(len(sample.TrgRels))] +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t [1 for i in range(batch_trg_max_len + 1 - len(sample.TrgRels))])\\n\\t\\telse:\\n\\t\\t\\tdecoder_input_list.append(get_relation_index_seq([], 1))\\n\\n\\treturn {'src_words': np.array(src_words_list, dtype=np.float32),\\n\\t\\t\\t'src_words_mask': np.array(src_words_mask_list),\\n\\t\\t\\t'src_chars': np.array(src_char_seq),\\n\\t\\t\\t'src_pos_tags': np.array(src_pos_seq),\\n\\t\\t\\t'src_dep_tags': np.array(src_dep_seq),\\n\\t\\t\\t'src_loc': np.array(src_loc_seq),\\n\\t\\t\\t'decoder_input': np.array(decoder_input_list),\\n\\t\\t\\t'arg1sweights': np.array(arg1sweights),\\n\\t\\t\\t'arg1eweights': np.array(arg1eweights),\\n\\t\\t\\t'arg2sweights': np.array(arg2sweights),\\n\\t\\t\\t'arg2eweights': np.array(arg2eweights),\\n\\t\\t\\t'rel': np.array(rel_seq),\\n\\t\\t\\t'arg1_start': np.array(arg1_start_seq),\\n\\t\\t\\t'arg1_end': np.array(arg1_end_seq),\\n\\t\\t\\t'arg2_start': np.array(arg2_start_seq),\\n\\t\\t\\t'arg2_end': np.array(arg2_end_seq),\\n\\t\\t\\t'target_vec': np.array(target_vec_seq),\\n\\t\\t\\t'target_vec_mask': np.array(target_vec_mask_seq)}\",\n \"def makecats(self, onlyn = None, sbe_sample_scale=0.05):\\n\\n\\t\\tfilenames = io.get_filenames(self.sbedatadir)\\n\\t\\tif onlyn != None:\\n\\t\\t\\tfilenames = filenames[:onlyn]\\n\\t\\t\\n\\t\\t\\n\\t\\t# Hardcoded for now:\\n\\t\\tstampsize = 200\\n\\t\\tn = 32\\n\\t\\t\\n\\t\\tlogger.info(\\\"Will make %i cats...\\\" % len(filenames))\\n\\t\\t\\n\\t\\tfor filename in filenames:\\n\\t\\t\\t\\n\\t\\t\\tdatafilepath = io.datafile(filename)\\n\\t\\t\\timagefilepath = io.imagefile(filename)\\n\\t\\t\\tworkname = io.workname(filename)\\n\\t\\t\\tcatfilepath = os.path.join(self.workobsdir, workname + \\\"-inputcat.pkl\\\")\\n\\t\\t\\timageworkdirfilepath = os.path.join(self.workobsdir, workname + \\\"-imageworkdir\\\")\\n\\t\\t\\t\\n\\t\\t\\tif os.path.exists(catfilepath):\\n\\t\\t\\t\\tlogger.info(\\\"Skipping '%s', catalog already exists\\\" % (workname))\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\n\\t\\t\\t# We read the data file and turn it into an astropy table\\n\\t\\t\\tcat = astropy.io.ascii.read(datafilepath)\\n\\t\\t\\t\\n\\t\\t\\t# Let's keep the file identification also in the meta dict:\\n\\t\\t\\tcat.meta[\\\"sbefilename\\\"] = filename # very weird SBE thing, without extension...\\n\\t\\t\\tcat.meta[\\\"workname\\\"] = workname\\n\\t\\t\\t\\n\\t\\t\\t# Let's convert the true shape parameters into more convenient forms:\\n\\t\\t\\t\\n\\t\\t\\tcat[\\\"PSF_e1\\\"] = cat[\\\"PSF_shape_1\\\"] * np.cos(2.0*cat[\\\"PSF_shape_2\\\"]*np.pi/180)\\n\\t\\t\\tcat[\\\"PSF_e2\\\"] = cat[\\\"PSF_shape_1\\\"] * np.sin(2.0*cat[\\\"PSF_shape_2\\\"]*np.pi/180)\\n\\t\\t\\tcat[\\\"Galaxy_e1\\\"] = cat[\\\"Galaxy_shape_1\\\"] * np.cos(2.0*cat[\\\"Galaxy_shape_2\\\"]*np.pi/180)\\n\\t\\t\\tcat[\\\"Galaxy_e2\\\"] = cat[\\\"Galaxy_shape_1\\\"] * np.sin(2.0*cat[\\\"Galaxy_shape_2\\\"]*np.pi/180)\\n\\t\\t\\tcat[\\\"Galaxy_g1\\\"] = cat[\\\"Galaxy_shear_1\\\"] * np.cos(2.0*cat[\\\"Galaxy_shear_2\\\"]*np.pi/180)\\n \\t\\t\\tcat[\\\"Galaxy_g2\\\"] = cat[\\\"Galaxy_shear_1\\\"] * np.sin(2.0*cat[\\\"Galaxy_shear_2\\\"]*np.pi/180)\\n\\n\\t\\t\\t# And for convenience, include some standard MegaLUT names for the PSFs\\n\\t\\t\\tcat[\\\"tru_psf_g1\\\"] = cat[\\\"PSF_e1\\\"]\\n\\t\\t\\tcat[\\\"tru_psf_g2\\\"] = cat[\\\"PSF_e2\\\"]\\n\\t\\t\\tcat[\\\"tru_psf_sigma\\\"] = cat[\\\"PSF_sigma_arcsec\\\"] / sbe_sample_scale\\n \\n\\t\\t\\t# We add the xid, yid, x and y columns, following an explanation by Bryan\\n\\t\\t\\t# on how the data/fits files should be interpreted (\\\"like english text\\\").\\n\\t\\t\\t#cat[\\\"xid\\\"] = np.concatenate([np.arange(n) for i in range(n)])\\n\\t\\t\\t#cat[\\\"yid\\\"] = np.concatenate([np.ones(n, dtype=np.int)*(n - i -1) for i in range(n)])\\n\\t\\t\\t# Well, not exactly. One should start y at the bottom, it seems:\\n\\t\\t\\tcat[\\\"xid\\\"] = np.concatenate([np.arange(n) for i in range(n)])\\n\\t\\t\\tcat[\\\"yid\\\"] = np.concatenate([np.ones(n, dtype=np.int)*i for i in range(n)])\\t\\t\\n\\t\\t\\tcat[\\\"x\\\"] = stampsize/2.0 + cat[\\\"xid\\\"]*(stampsize + 1) + 0.5\\n\\t\\t\\tcat[\\\"y\\\"] = stampsize/2.0 + cat[\\\"yid\\\"]*(stampsize + 1) + 0.5\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t# We create the ImageInfo object\\n\\t\\t\\timg = megalut.tools.imageinfo.ImageInfo(\\n\\t\\t\\t\\tfilepath = imagefilepath,\\n\\t\\t\\t\\txname = \\\"x\\\", yname = \\\"y\\\",\\n\\t\\t\\t\\tstampsize = stampsize,\\n\\t\\t\\t\\tworkdir = imageworkdirfilepath\\n\\t\\t\\t\\t)\\n\\t\\t\\tcat.meta[\\\"img\\\"] = img\\n\\t\\t\\t\\n\\t\\t\\t# And save the catalog\\n\\t\\t\\tmegalut.tools.io.writepickle(cat, catfilepath)\",\n \"def build_command(self, args):\\n self._validate_common(args)\\n self._set_manifests(args)\\n\\n manifest = self._manager._recipes\\n\\n records = self._get_matching_records(args, manifest)\\n\\n if not len(records):\\n sys.exit(\\\"No matching items found.\\\")\\n\\n for record in records:\\n result = self._manager.build(record['_type'], **record)\\n print('The requested asset has been built: {}'.format(result['_path']))\\n\\n if len(records) > 1:\\n print('All files have been successfully built. Thank you.')\",\n \"def go(self):\\n num_fofs = self.fofs['fofid'].max()\\n fof_splits = split.get_splits(num_fofs, self['chunksize'])\\n\\n for isplit,fof_split in enumerate(fof_splits):\\n logger.info('%s %s' % (isplit,fof_split))\\n self._write_split(isplit, fof_split)\",\n \"def main(filein, min_reads=150, n_group=2000):\\n from random import sample\\n assert os.path.exists(filein)\\n hq = filter_reads(filein)\\n logging.info('remove matching reads')\\n no_pol = remove_matching_reads(hq, cont_file)\\n # no_pol = 'clean_reads.fasta'\\n no_pol_reads = list(SeqIO.parse(no_pol, 'fasta'))\\n no_pol_reads = sample(no_pol_reads, k=len(no_pol_reads))\\n covering_reads = set([])\\n logging.info('blast reads in batches until enough are found')\\n total_blasted = 0\\n for i, group in enumerate(grouper(n_group, no_pol_reads)):\\n if i > 2 and len(covering_reads) < 20:\\n sys.exit('not enough reads covering V3 were found')\\n logging.info('blast call %d', i + 1)\\n _ = blast_reads(group)\\n covering_reads.update(_)\\n total_blasted += n_group\\n logging.info('this blast: %d covering out of %d total - %3.2f %%', len(_), n_group,\\n 100 * float(len(_)) / n_group)\\n logging.info('cumulative: %d covering out of %d total - %3.2f %%', len(covering_reads), total_blasted,\\n 100 * float(len(covering_reads)) / total_blasted)\\n if len(covering_reads) >= min_reads:\\n break\\n\\n logging.info('covering_reads used in MSA: %d out of %d blasted (%3.2f %%)', len(covering_reads), total_blasted,\\n 100 * float(len(covering_reads)) / total_blasted)\\n cov_reads, n_fwd, n_rev = extract_reads(covering_reads, no_pol)\\n\\n SeqIO.write(cov_reads, 'v3reads.fasta', 'fasta')\\n logging.info('%d covering reads in forward orientation', n_fwd)\\n logging.info('%d covering reads in reverse orientation', n_rev)\\n if n_fwd + n_rev < min_reads:\\n logging.error('Not enough reads: %d', n_fwd + n_rev)\\n sys.exit('Not enough reads: %d' % (n_fwd + n_rev))\\n\\n no_singleton_reads = [s for s in SeqIO.parse('v3reads.fasta', 'fasta') if int(s.id.split('_')[-1]) > 1]\\n SeqIO.write(no_singleton_reads, 'v3reads_no_singleton.fasta', 'fasta')\\n\\n cml = shlex.split('muscle -in v3reads_no_singleton.fasta -out msa.fasta -quiet')\\n subprocess.call(cml)\\n\\n df, haplotypes, support = msa_2_df('msa.fasta')\\n logging.info('Haplotypes supported by %d reads out of %d: %3.1f%%',\\n support, n_fwd + n_rev, 100.0 * support / (n_fwd + n_rev))\\n cons_seq = df_2_ambiguous_sequence(df)\\n SeqIO.write([SeqRecord(Seq(cons_seq), id='v3_consensus', description='')], 'v3cons.fasta', 'fasta')\\n\\n haps = []\\n hi = 1 # counter for haplotypes, used in fasta file\\n accounted_f = 0.0 # keep track of the cumulative accounted frequency\\n tot_reads = sum(haplotypes.values())\\n for h, support in haplotypes.most_common():\\n f = round(float(support) / tot_reads, 2)\\n accounted_f += f\\n sr = SeqRecord(Seq(h), id='v3_haplotype_%d-support_%3.2f' % (hi, f), description='')\\n haps.append(sr)\\n hi += 1\\n\\n SeqIO.write(haps, 'v3haplotypes.fasta', 'fasta')\\n for f in ['high_quality.fastq', 'clean_reads.fasta']:\\n os.remove(f)\\n logging.info('Haplotypes written to haplotypes.fasta')\",\n \"def main(raw_args=None):\\r\\n args = get_args(raw_args)\\r\\n dataset = DATASETS[args.dataset]\\r\\n dataset.set_work_dir(args.path)\\r\\n\\r\\n dataset_dir = os.path.join(dataset.get_data_path(), consts.RAW_DATA_DIR)\\r\\n input_file = os.path.join(dataset_dir, args.input)\\r\\n if args.output:\\r\\n out_name = args.output\\r\\n else:\\r\\n out_name = OUT_FILE % (args.chr, format_number(args.size))\\r\\n output_file = os.path.join(dataset_dir, out_name)\\r\\n\\r\\n print(\\\"Output will be in: %s\\\" % output_file)\\r\\n\\r\\n entries = set()\\r\\n out = open(output_file, 'w')\\r\\n input = open(input_file, 'r')\\r\\n\\r\\n is_first = True\\r\\n for line in input:\\r\\n if is_first:\\r\\n add_headers(line.strip(), out)\\r\\n is_first = False\\r\\n else:\\r\\n entries.add(line)\\r\\n input.close()\\r\\n print(\\\"Done reading\\\")\\r\\n\\r\\n # Keeps track of spacers that were already included.\\r\\n spacers = set()\\r\\n # Keeps track of the number of samples already obtained.\\r\\n sampled = 0\\r\\n # Keeps track of progress for printing.\\r\\n chunk = 1\\r\\n\\r\\n # While we have not sampled enough...\\r\\n while sampled < args.size:\\r\\n remaining = args.size - sampled\\r\\n # Sample more entries.\\r\\n new_entries = sample(entries, spacers, remaining, dataset)\\r\\n sampled += len(new_entries)\\r\\n for entry in new_entries:\\r\\n out.write(common.to_csv_line(*entry))\\r\\n\\r\\n # If we have passed a progress checkpoint - prints progress.\\r\\n if (args.size / PRINT_NUM) * chunk < sampled:\\r\\n print(\\\"Sampled: %d\\\" % sampled)\\r\\n chunk += 1\\r\\n\\r\\n print(\\\"Done\\\")\\r\\n print(STATS)\",\n \"def process_datasets(self):\\n\\n with open(self.mappings, \\\"r+\\\") as json_file:\\n emsl_to_jgi = json.load(json_file)\\n emsl_to_jgi_copy = copy.deepcopy(emsl_to_jgi)\\n\\n contaminant_file_loc = emsl_to_jgi[\\\"contaminant_file_loc\\\"]\\n # run for each dataset\\n for dataset_id, values in emsl_to_jgi.items():\\n if dataset_id not in [\\n \\\"contaminant_file_loc\\\",\\n \\\"analysis_activity_file_loc\\\",\\n \\\"data_objects_file_loc\\\",\\n \\\"STUDY\\\",\\n \\\"tools_used\\\",\\n ]:\\n raw_file_loc = values[\\\"raw_file_loc\\\"]\\n self.dataset_name = values[\\\"dataset_name\\\"]\\n # dataset search against a fasta file\\n for genome_directory, locations in values[\\n \\\"genome_directory\\\"\\n ].items():\\n # clear object to prepare next job\\n ANALYSIS_JOBS_OBJECT.clear()\\n\\n # create log_dir\\n self.save_job_results = os.path.join(\\n self.result_loc, dataset_id, genome_directory\\n )\\n self.log_collected_at = os.path.join(\\n os.path.abspath(self.save_job_results), \\\"analysis_jobs_logs\\\"\\n )\\n if not os.path.exists(self.log_collected_at):\\n os.makedirs(self.log_collected_at)\\n\\n files = [locations[\\\"faa_file_loc\\\"], contaminant_file_loc]\\n contaminated_faa_file_loc = self.contaminate_fasta(files)\\n\\n self.register_job_in_emsl_to_jgi(\\n dataset_id,\\n genome_directory,\\n \\\"contaminated_faa_file_loc\\\",\\n contaminated_faa_file_loc,\\n emsl_to_jgi_copy,\\n )\\n # convert .faa to .txt\\n faa_txt_file = self.convert_faa2txt(\\n dataset_id, contaminated_faa_file_loc\\n )\\n self.register_job_in_emsl_to_jgi(\\n dataset_id,\\n genome_directory,\\n \\\"txt_faa_file_loc\\\",\\n faa_txt_file,\\n emsl_to_jgi_copy,\\n )\\n\\n # log & run job\\n self.run_n_log_job(\\n dataset_id,\\n genome_directory,\\n contaminated_faa_file_loc,\\n raw_file_loc,\\n emsl_to_jgi_copy,\\n )\\n\\n # merge analysis\\n resultant_file = self.merge_analysis_jobs(\\n dataset_id, genome_directory\\n )\\n self.register_job_in_emsl_to_jgi(\\n dataset_id,\\n genome_directory,\\n \\\"resultant_file_loc\\\",\\n resultant_file,\\n emsl_to_jgi_copy,\\n )\\n\\n # capture the job metadata object\\n logger.info(\\\"Jobrun\\\", extra=LOGGED_ANALYSIS_JOB)\\n\\n # update emsl_to_jgi.json\\n json_file.seek(0) # move back to BOF.\\n json_file.truncate()\\n json_file.write(json.dumps(emsl_to_jgi_copy, default=str, indent=4))\\n pass\",\n \"def FSC2(input_dir, num_reps=50, min_sims=100000, max_ecm=20, calc_CI=False, numcores=1, scratch_mb='200', time_scratch=\\\"01:50:00\\\", mem=\\\"200\\\", print1=False, overwrite=\\\"None\\\", fsc2_path=\\\"/storage/plzen1/home/holcovam/programs/fsc26_linux64/fsc26\\\"):\\n Data_Files = []\\n tpl_files = []\\n est_files = []\\n CI_Data_Files = []\\n shlist = []\\n\\n if input_dir.endswith(\\\"/\\\") is False:\\n input_dir += \\\"/\\\"\\n\\n for path in os.listdir(input_dir):\\n if os.path.isdir(input_dir + path) and path.startswith(\\\"FSC2input\\\"):\\n samp_name = path.split(\\\"_\\\")[1]\\n #folder_name = samp_name\\n if samp_name + \\\"_DSFS.obs\\\" in os.listdir(input_dir + path):\\n for i in range(0, num_reps):\\n new_file = open(input_dir + path + \\\"/\\\" + samp_name + str(i) + \\\"_DSFS.obs\\\", 'w')\\n with open(input_dir + path + \\\"/\\\" + samp_name + \\\"_DSFS.obs\\\") as data_file:\\n for line in data_file:\\n new_file.write(line)\\n new_file.close()\\n Data_Files.append(input_dir + path + \\\"/\\\" + samp_name + str(i) + \\\"_DSFS.obs\\\")\\n else:\\n print(\\\"Did not find input data file for: \\\", samp_name)\\n if calc_CI == \\\"True\\\":\\n num_files = 0\\n for file in os.listdir(input_dir + path):\\n if file.endswith(\\\"_DSFS.obs\\\") and file.split(\\\"_\\\")[-2].split(\\\".\\\")[-1][0:3] == \\\"rep\\\" and file != samp_name + \\\"_DSFS.obs\\\":\\n for i in range(0, num_reps):\\n new_file = open(input_dir + path + \\\"/\\\" + samp_name + file.split(\\\"_\\\")[-2].split(\\\".\\\")[-1].split(\\\"_\\\")[0]+ \\\"_\\\" + str(i) + \\\"_DSFS.obs\\\", 'w')\\n with open(input_dir + path + \\\"/\\\" + file) as data_file:\\n for line in data_file:\\n new_file.write(line)\\n new_file.close()\\n CI_Data_Files.append(input_dir + path + \\\"/\\\" + samp_name + file.split(\\\"_\\\")[-2].split(\\\".\\\")[-1].split(\\\"_\\\")[0]+ \\\"_\\\" + str(i) + \\\"_DSFS.obs\\\")\\n num_files += 1\\n if len(CI_Data_Files) < 1:\\n print(\\\"Did not find bootstrap replicates for: \\\", samp_name)\\n else:\\n print(\\\"Found \\\", num_files, \\\" replicate dsfs files for CI calculation for \\\", samp_name)\\n if path.endswith(\\\".tpl\\\"):\\n tpl_files.append(path)\\n est_files.append(path.split(\\\".\\\")[0])\\n if len(tpl_files) == 0:\\n print(\\\"Did not find any tpl files!! Aborting!!\\\")\\n else:\\n if calc_CI == \\\"True\\\":\\n Data_Files = CI_Data_Files\\n for file in Data_Files:\\n name = file.split(\\\"_DSFS\\\")[0]\\n samp_name = name.split(\\\"/\\\")[-1]\\n folder_name = samp_name [0:11]\\n for tpl in tpl_files:\\n tpl_name = tpl.split(\\\".tpl\\\")[0]\\n if os.path.isdir(name + \\\"_\\\" + tpl_name) is False or overwrite == \\\"hard\\\":\\n new_tpl = open(name + \\\"_\\\" + tpl_name + \\\".tpl\\\", 'w')\\n new_data = open(name + \\\"_\\\" + tpl_name + \\\"_DSFS.obs\\\", 'w')\\n\\n with open(file, 'r') as data:\\n for i, line in enumerate(data):\\n if i == 1:\\n pop_info = line.strip(\\\"\\\\n\\\").strip(\\\"\\\\t\\\").split(\\\"\\\\t\\\")\\n pop_num = int(pop_info[0])\\n samp_nums = pop_info[-pop_num:]\\n new_data.write(line)\\n with open(input_dir + tpl, 'r') as template:\\n samp_num_lines = pop_num + 4\\n for i, line in enumerate(template):\\n if i < samp_num_lines:\\n new_tpl.write(line)\\n elif i == samp_num_lines:\\n for num in samp_nums:\\n new_tpl.write(num + \\\"\\\\n\\\")\\n elif i >= samp_num_lines + len(samp_nums):\\n new_tpl.write(line)\\n new_est = open(name + \\\"_\\\" + tpl_name + \\\".est\\\", 'w')\\n try:\\n with open(input_dir + tpl_name + \\\".est\\\") as est:\\n for line in est:\\n new_est.write(line)\\n except FileNotFoundError:\\n print(\\\"Did not find est file for: \\\", tpl)\\n #folder_name = samp_name ''.join(i for i in s if not i.isdigit())\\n shname = name + \\\"_\\\" + tpl_name + \\\".sh\\\"\\n shfile5 = open(shname, 'w')\\n shfile5.write('#!/bin/bash -e\\\\n' +\\n '#PBS -N '+samp_name+'\\\\n' +\\n '#PBS -l walltime='+str(time_scratch)+'\\\\n' +\\n '#PBS -l select=1:ncpus='+str(numcores)+':mem='+str(mem)+'mb:scratch_local='+str(scratch_mb)+'mb\\\\n' +\\n '#PBS -m abe\\\\n' +\\n '#PBS -j oe\\\\n\\\\n' +\\n 'module add python-3.4.1-gcc\\\\n'+\\n 'module add python34-modules-gcc\\\\n'+\\n 'trap \\\\'clean_scratch\\\\' TERM EXIT\\\\n'+\\n 'if [ ! -d \\\"$SCRATCHDIR\\\" ] ; then echo \\\"Scratch not created!\\\" 1>&2; exit 1; fi \\\\n' +\\n 'DATADIR=\\\"/storage/plzen1/home/holcovam/ScanTools\\\"\\\\n' +\\n 'cp $DATADIR/'+ input_dir + \\\"FSC2input_\\\" + folder_name+ \\\"/\\\" + samp_name + \\\"_\\\" + tpl_name + '* $SCRATCHDIR || exit 1\\\\n'+\\n 'cp '+fsc2_path+' $SCRATCHDIR || exit 1\\\\n'+\\n 'cd $SCRATCHDIR || exit 2\\\\n' +\\n 'echo data loaded at `date`\\\\n\\\\n' +\\n 'chmod +x fsc26 \\\\n' +\\n #'ls -l \\\\n' +\\n './fsc26 -t ' + samp_name + \\\"_\\\" + tpl_name + '.tpl -e ' + samp_name + \\\"_\\\" + tpl_name + '.est -n ' + str(min_sims) + ' -u -d -q -L ' + str(max_ecm) + ' -M \\\\n' + \\n 'rm seed.txt \\\\n'+\\n 'rm fsc26\\\\n'+\\n 'rm *DSFS.obs\\\\n'+\\n 'rm *.sh\\\\n'+\\n 'rm *.tpl \\\\n'+\\n 'rm *.est \\\\n'+\\n #'ls -l \\\\n' +\\n 'cp $SCRATCHDIR/*.par $DATADIR/'+ input_dir + \\\"FSC2input_\\\" + folder_name+' || exit 1\\\\n'+\\n 'rm *.par \\\\n'+\\n 'cp -r $SCRATCHDIR/* $DATADIR/'+input_dir+' || export CLEAN_SCRATCH=false\\\\n'+\\n 'printf \\\"\\\\\\\\nFinished\\\\\\\\n\\\\\\\\n\\\"\\\\n')\\n shfile5.close()\\n shlist.append(shname)\\n\\n############IF PROBLEM WITH EXCESS OF NONCONVERGED CHAINS, COPY /home/majda/alpine/fastsimcoal2/afterWPSG/scripts/notConverged.py here ###################\\n\\n else:\\n print(\\\"Output for \\\" + samp_name + \\\"_\\\" + tpl_name + \\\" already exists. Use hard_overwrite = True to overwrite.\\\")\\n return shlist\",\n \"def addSplitNamVCFJobs(self, workflow, inputData=None, db_vervet=None, transferOutput=True,\\\\\\n\\t\\t\\t\\t\\t\\tmaxContigID=None, outputDirPrefix=\\\"\\\"):\\n\\t\\tsys.stderr.write(\\\"Adding split Nam VCF-file jobs for %s vcf files ... \\\"%(len(inputData.jobDataLs)))\\n\\t\\tno_of_jobs= 0\\n\\t\\t\\n\\t\\t\\n\\t\\ttopOutputDir = \\\"%sSampleInUCLAID\\\"%(outputDirPrefix)\\n\\t\\ttopOutputDirJob = self.addMkDirJob(outputDir=topOutputDir)\\n\\t\\tno_of_jobs += 1\\n\\t\\t\\n\\t\\treturnData = PassingData()\\n\\t\\treturnData.jobDataLs = []\\n\\t\\tfor jobData in inputData.jobDataLs:\\n\\t\\t\\tinputF = jobData.vcfFile\\n\\t\\t\\tif maxContigID:\\n\\t\\t\\t\\tcontig_id = self.getContigIDFromFname(inputF.name)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tcontig_id = int(contig_id)\\n\\t\\t\\t\\t\\tif contig_id>maxContigID:\\t#skip the small contigs\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tsys.stderr.write('Except type: %s\\\\n'%repr(sys.exc_info()))\\n\\t\\t\\t\\t\\timport traceback\\n\\t\\t\\t\\t\\ttraceback.print_exc()\\n\\t\\t\\tinputFBaseName = os.path.basename(inputF.name)\\n\\t\\t\\tcommonPrefix = inputFBaseName.split('.')[0]\\n\\t\\t\\toutputVCF = File(os.path.join(topOutputDir, '%s.vcf'%(commonPrefix)))\\n\\t\\t\\tabstractMapperJob = self.addAbstractMapperLikeJob(workflow, executable=workflow.SplitNamVCFIntoMultipleSingleChrVCF, \\\\\\n\\t\\t\\t\\t\\tinputVCF=inputF, outputF=outputVCF, \\\\\\n\\t\\t\\t\\t\\tparentJobLs=[topOutputDirJob]+jobData.jobLs, transferOutput=False, job_max_memory=200,\\\\\\n\\t\\t\\t\\t\\textraArguments=None, extraDependentInputLs=[])\\n\\t\\t\\t\\n\\t\\t\\tVCFGzipOutputF = File(\\\"%s.gz\\\"%outputVCF.name)\\n\\t\\t\\tVCFGzipOutput_tbi_F = File(\\\"%s.gz.tbi\\\"%outputVCF.name)\\n\\t\\t\\tbgzip_tabix_VCF_job = self.addBGZIP_tabix_Job(workflow, bgzip_tabix=workflow.bgzip_tabix, \\\\\\n\\t\\t\\t\\t\\tparentJobLs=[abstractMapperJob], inputF=abstractMapperJob.output, outputF=VCFGzipOutputF, \\\\\\n\\t\\t\\t\\t\\ttransferOutput=transferOutput)\\n\\t\\t\\t\\n\\t\\t\\treturnData.jobDataLs.append(PassingData(jobLs=[bgzip_tabix_VCF_job], vcfFile=VCFGzipOutputF, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ttbi_F=VCFGzipOutput_tbi_F, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfileLs=[VCFGzipOutputF, VCFGzipOutput_tbi_F]))\\n\\t\\t\\t\\n\\t\\t\\tno_of_jobs += 2\\n\\t\\tsys.stderr.write(\\\"%s jobs.\\\\n\\\"%(self.no_of_jobs))\\n\\t\\treturn returnData\",\n \"def writeFileBloatReport(f, baselineName, buildName):\\n logging.info('Running bloaty diff between %s and %s', baselineName, buildName)\\n f.write('Comparing %s and %s:\\\\n\\\\n' % (baselineName, buildName))\\n\\n result = subprocess.run(\\n ['bloaty', '--csv', buildName, '--', baselineName],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT,\\n )\\n\\n if result.returncode != 0:\\n logging.warning('Bloaty execution failed: %d', result.returncode)\\n f.write('BLOAT EXECUTION FAILED WITH CODE %d:\\\\n' % result.returncode)\\n\\n content = result.stdout.decode('utf8')\\n\\n f.write(content)\\n f.write('\\\\n')\\n\\n result = ComparisonResult(os.path.basename(buildName))\\n try:\\n reader = csv.reader(io.StringIO(content))\\n\\n for row in reader:\\n section, vm, f = row\\n if (section in SECTIONS_TO_WATCH) or (vm not in ['0', 'vmsize']):\\n result.sectionChanges.append(SectionChange(section, int(f), int(vm)))\\n except:\\n pass\\n\\n return result\",\n \"def form_cand_queries_batch(self, batch_input, gram, additional_neg_batch=0):\\n N = batch_input['pair_objects'].size(0)\\n\\n # For each gram s,r,o get unique list of positive labels in the batch\\n if gram in ['s','r','o']:\\n\\n labels = batch_input['labels_' + gram]\\n\\n cat_batch = []\\n idx = []\\n for j in range(N):\\n\\n cats = (labels[j,:]==1).nonzero().data[:,0].tolist()\\n\\n count = 0\\n cat = cats[0]\\n while count < len(cats) and cats[count]>-1:\\n cat = cats[count]\\n if cat not in cat_batch:\\n idx.append(tuple([j,len(cat_batch)]))\\n cat_batch.append(cat)\\n else:\\n idx.append(tuple([j,cat_batch.index(cat)]))\\n count += 1\\n\\n # Add negatives at random (later can refine and add hard negatives)\\n if additional_neg_batch>0:\\n neg_cat_sampled = np.random.randint(0, len(self.vocab[gram]), size=additional_neg_batch) # can be duplicate, it is ok\\n\\n # Append the ones that are not positive for any example in the batch\\n for neg_cat in neg_cat_sampled:\\n if neg_cat not in cat_batch:\\n cat_batch.append(neg_cat) \\n \\n\\n labels_query = np.zeros((N,len(cat_batch)))\\n for j in range(len(idx)):\\n labels_query[idx[j][0], idx[j][1]] = 1\\n\\n cat_batch = Variable(torch.from_numpy(np.array(cat_batch).astype(int)))\\n if self.use_gpu:\\n cat_batch = cat_batch.cuda()\\n\\n query = self.idx_to_vocab[gram].index_select(0, cat_batch)\\n\\n labels_query = Variable(torch.from_numpy(labels_query)).type(query.data.type())\\n query = query.unsqueeze(0) # (M,1) -> (1,M,1)\\n\\n\\n return query, labels_query\\n\\n\\n # The triplets for sro are all the positives\\n if gram=='sro':\\n\\n triplet_cat_batch = np.empty((0,3), dtype=int)\\n idx_triplet = []\\n for j in range(N):\\n\\n sub_cats = (batch_input['labels_s'][j,:]==1).nonzero().data[:,0].tolist()\\n obj_cats = (batch_input['labels_o'][j,:]==1).nonzero().data[:,0].tolist()\\n rel_cats = (batch_input['labels_r'][j,:]==1).nonzero().data[:,0].tolist()\\n\\n\\n # Do not add the triplets containing __background__ -> not in vocab\\n for sub_cat in sub_cats:\\n for obj_cat in obj_cats:\\n\\n if sub_cat==0 or obj_cat==0:\\n continue\\n\\n count = 0\\n while count < len(rel_cats) and rel_cats[count]>-1:\\n rel_cat = rel_cats[count]\\n triplet_cat = np.array([sub_cat, rel_cat, obj_cat])\\n idx_triplet_cat_batch = np.where(np.logical_and(triplet_cat_batch[:,0]==triplet_cat[0], \\\\\\n np.logical_and(\\n triplet_cat_batch[:,1]==triplet_cat[1], \\\\\\n triplet_cat_batch[:,2]==triplet_cat[2])))[0]\\n if len(idx_triplet_cat_batch)==0:\\n idx_triplet.append(tuple([j,triplet_cat_batch.shape[0]]))\\n triplet_cat_batch = np.vstack((triplet_cat_batch, triplet_cat))\\n else:\\n idx_triplet.append(tuple([j,idx_triplet_cat_batch[0]]))\\n\\n count += 1\\n\\n\\n # Add negatives at random\\n if additional_neg_batch>0:\\n\\n neg_cat_sampled_sub = np.random.randint(0, len(self.vocab['s']), size=additional_neg_batch)\\n neg_cat_sampled_obj = np.random.randint(0, len(self.vocab['o']), size=additional_neg_batch)\\n neg_cat_sampled_rel = np.random.randint(0, len(self.vocab['r']), size=additional_neg_batch)\\n neg_cat_sampled = np.vstack((neg_cat_sampled_sub, neg_cat_sampled_rel, neg_cat_sampled_obj)).T\\n\\n\\n # Append the ones that are not positive for any example in the batch\\n for j in range(len(neg_cat_sampled)):\\n\\n idx_batch = np.where(np.logical_and(triplet_cat_batch[:,0]==neg_cat_sampled[j,0], \\\\\\n np.logical_and(\\n triplet_cat_batch[:,1]==neg_cat_sampled[j,1], \\\\\\n triplet_cat_batch[:,2]==neg_cat_sampled[j,2])))[0]\\n\\n if len(idx_batch)==0:\\n triplet_cat_batch = np.vstack((triplet_cat_batch, neg_cat_sampled[j,:]))\\n\\n\\n labels_query_sro = np.zeros((N,triplet_cat_batch.shape[0]))\\n for j in range(len(idx_triplet)):\\n labels_query_sro[idx_triplet[j][0], idx_triplet[j][1]] = 1\\n\\n triplet_cat_batch = Variable(torch.from_numpy(triplet_cat_batch))\\n if self.use_gpu:\\n triplet_cat_batch = triplet_cat_batch.cuda()\\n query_sro = torch.cat([ self.idx_to_vocab['s'].index_select(0,triplet_cat_batch[:,0]),\\\\\\n self.idx_to_vocab['r'].index_select(0,triplet_cat_batch[:,1]),\\\\\\n self.idx_to_vocab['o'].index_select(0,triplet_cat_batch[:,2])], 1)\\n\\n labels_query_sro = Variable(torch.from_numpy(labels_query_sro)).type(query_sro.data.type()) \\n query_sro = query_sro.unsqueeze(0) # (M,3) -> (1,M,3)\\n\\n\\n return query_sro, labels_query_sro\",\n \"def main():\\n file_one_path, file_two_path, output_path =\\\\\\n get_command_line_arguments(\\n ['/home/ehler002/project/groups/go/Data/Cluster_Data/Dataset.txt',\\n '/home/ehler002/project/groups/go/Data/Cluster_Data/translated_genes.fpkm_table',\\n '/home/ehler002/project/groups/go/Data/Cluster_Data/Full_fpkm_Table.txt'])\\n pattern = 'CRO_T'\\n for file_path in [file_one_path, file_two_path]:\\n assert os.path.exists(file_path), 'File %s does not exist.' % file_path\\n start_time = datetime.datetime.now()\\n print('Started concatenation at %s' % start_time)\\n file_contents, headers = get_file_contents(file_two_path)\\n file_contents = sort_file_contents(file_contents)\\n file_contents = remove_pattern(file_contents, pattern)\\n concatenate_files(file_one_path, file_contents, headers, output_path)\\n print('Finished concatenation in %s' % (datetime.datetime.now() - start_time))\",\n \"def generate_patches(self):\\n sets = os.listdir(self.__data_dir)\\n import re\\n sets = [a_set for a_set in sets if re.match(r'[0-9a-zA-Z_]+', a_set)]\\n total = len(sets)\\n count = 1\\n for a_set in sets:\\n print('\\\\r', 'slicing {0}/{1}'.format(count, total).ljust(20), end='')\\n self.__generate_slices_a_set(a_set)\\n count += 1\",\n \"def produce_train_all(root_dir):\\n train = 'trainImnamesSe.csv'\\n all_bboxes = 'bboxesDF.csv'\\n train_lab = 'trainLabeledDF.csv'\\n\\n if train in os.listdir(root_dir) and all_bboxes in os.listdir(root_dir) \\\\\\n and train_lab in os.listdir(root_dir):\\n train_imnames = pd.read_csv(osp.join(root_dir, train),\\n header=None, squeeze=True)\\n all_bboxes = pd.read_csv(osp.join(root_dir, all_bboxes))\\n train_lab = pd.read_csv(osp.join(root_dir, train_lab))\\n else:\\n train_imnames, _ = process_pool_mat(root_dir)\\n all_bboxes, _ = process_images_mat(root_dir)\\n train_lab = process_train_mat(root_dir)\\n\\n # pick out items whose `imname` appear in `train_imnames`\\n # to form training set. NOTE there is another way to finish it\\n # ```\\n # train_all = all_bboxes[all_bboxes['imname'].isin(train_imnames)]\\n # train_all.index = range(train_all.shape[0])\\n # ```\\n train_all = pd.merge(all_bboxes, train_imnames.to_frame(name='imname'))\\n train_all = pd.merge(train_all, train_lab, how='outer') # set pids\\n train_all = train_all.fillna(-1) # set -1 to unlabeled samples\\n train_all['pid'] = train_all['pid'].values.astype(np.int32)\\n\\n train_all.to_csv(osp.join(root_dir, 'trainAllDF.csv'), index=False)\\n\\n return train_all\",\n \"def format_preparation_files(run_dir, sample_sheet, output_dir, pipeline,\\n verbose):\\n sample_sheet = KLSampleSheet(sample_sheet)\\n df_sheet = sample_sheet_to_dataframe(sample_sheet)\\n\\n if pipeline == 'atropos-and-bowtie2':\\n click.echo('Stats collection is not supported for pipeline '\\n 'atropos-and-bowtie2')\\n else:\\n stats = run_counts(run_dir, sample_sheet)\\n\\n stats['sample_name'] = \\\\\\n df_sheet.set_index('lane', append=True)['sample_name']\\n\\n # returns a map of (run, project_name, lane) -> preparation frame\\n preps = preparations_for_run(run_dir, df_sheet, pipeline=pipeline)\\n\\n os.makedirs(output_dir, exist_ok=True)\\n\\n for (run, project, lane), df in preps.items():\\n fp = os.path.join(output_dir, f'{run}.{project}.{lane}.tsv')\\n\\n if pipeline == 'fastp-and-minimap2':\\n # stats are indexed by sample name and lane, lane is the first\\n # level index. When merging, make sure to select the lane subset\\n # that we care about, otherwise we'll end up with repeated rows\\n df = df.merge(stats.xs(lane, level=1), how='left',\\n on='sample_name')\\n\\n # strip qiita_id from project names in sample_project column\\n df['sample_project'] = df['sample_project'].map(\\n lambda x: re.sub(r'_\\\\d+$', r'', x))\\n\\n # center_project_name is a legacy column that should mirror\\n # the values for sample_project.\\n df['center_project_name'] = df['sample_project']\\n\\n df.to_csv(fp, sep='\\\\t', index=False)\\n\\n if verbose:\\n project_name = remove_qiita_id(project)\\n # assume qiita_id is extractable and is an integer, given that\\n # we have already passed error-checking.\\n qiita_id = project.replace(project_name + '_', '')\\n print(\\\"%s\\\\t%s\\\" % (qiita_id, abspath(fp)))\",\n \"def prepare_runs(args):\\n output_directory = _prepare_output_dir(args.output_directory)\\n z_score_dir = args.z_score_dir\\n region_list = args.region_list \\n if args.region_list is None:\\n try:\\n flanking_region = int(args.flanking_region)\\n except ValueError:\\n logging.error('Flanking region argument needs to be an integer')\\n sys.exit(COMMAND_LINE_ERROR)\\n build = args.build\\n bed_directory = args.bed_directory\\n # Create the SNPList\\n try:\\n min_maf = float(args.maf)\\n except:\\n logging.error(\\\"Min Maf -m or --min-maf needs to be an floating point number\\\")\\n sys.exit(COMMAND_LINE_ERROR)\\n if args.region_list is not None:\\n region_list = {}\\n snp_list = []\\n with open(args.region_list) as input_file:\\n # When using no flaking region SNP must be valid, but it doesn't actually matter what it is, need to ensure that is actually the case.\\n for i, line in enumerate(input_file):\\n rsid = str(i)+ \\\"_\\\" + ''.join(line.strip().split(\\\"\\\\t\\\"))\\n chromosome = line.strip().split(\\\":\\\")[0] \\n snp = Snp(chromosome,\\\"1\\\",rsid)\\n snp_list.append(snp)\\n region_list[snp.rsid] = line.strip()\\n else:\\n snp_list = SnpList(args.snp_list, build)\\n logging.info(snp_list)\\n # Locus to process\\n # population_to_extract_vcf\\n if not args.annotation_only:\\n no_flanking = args.flanking_units\\n if no_flanking:\\n raise NotImplementedError(\\\"Using a number of flanking SNPs instead of a region is not supported\\\")\\n populations= args.populations.split(',')\\n logging.info(\\\"Populations to process: {0}\\\".format(populations))\\n loci = []\\n gemini_databases = []\\n output_vcfs = []\\n for snp in snp_list:\\n logging.info('Preparing output files for SNP {0}'.format(snp.rsid))\\n locus = snp.rsid\\n loci.append(locus)\\n logging.info(\\\"Obtaining VCF file from the 1000 genomes project\\\")\\n if region_list is not None:\\n vcf = get_vcf_file(snp, string_region=region_list[locus])\\n else: \\n vcf = get_vcf_file(snp, flanking_region=flanking_region)\\n for population in populations:\\n tmp_vcf = extract_population_from_1000_genomes(vcf=vcf, super_population=population)\\n z_score_file = get_relevant_zscore(snp.chrom, population, z_score_dir)\\n pos_list_zscore = create_pos_hash_table(z_score_file)\\n output_vcf = generate_zscore_and_vcf_output(output_directory=output_directory, zscore_hash=pos_list_zscore, vcf=tmp_vcf, locus=locus,population=population, multiply_rsquare=args.multiply_rsquare)\\n if bed_directory is None:\\n logging.info(\\\"Creating gemini database\\\")\\n # TODO: Fix broxen gemini referenec\\n gemini_databases.append(create_gemini_database(vcf=output_vcf))\\n vcf_to_plink(locus, output_directory=output_directory, vcf=output_vcf, population=population)\\n plink_to_ld_matrix(locus, output_directory=output_directory, population=population)\\n logging.info(\\\"Generate transancestrals matrices\\\")\\n generate_transancestral_output(loci, populations, output_directory)\\n if bed_directory is None:\\n logging.info(\\\"Generating annotation matrices to be used with Paintor\\\")\\n logging.info(gemini_databases)\\n generate_and_write_encode_annotations(databases=gemini_databases, output_directory=output_directory, loci=snp_list)\\n else:\\n logging.info(\\\"Annotation using bed files\\\")\\n generate_bed_file_annotations(loci=loci, bed_directory=bed_directory, output_directory=output_directory) \\n # So finally we need to fix the LD matrices for inputting into PAINTOR. \\n\\n with open(os.path.join(output_directory, 'input.files'), 'w') as out_f:\\n for snp in snp_list:\\n out_f.write(snp.rsid +'\\\\n')\\n # Remove .tbi files\\n for file in os.listdir('.'):\\n if fnmatch.fnmatch(file, '*.tbi'):\\n try:\\n os.remove(file)\\n except OSError:\\n logging.warning(\\\"Could not remove a .tbi file from the 1000 genomes tabix run\\\")\\n else: \\n loci = []\\n for snp in snp_list:\\n loci.append(snp.rsid)\\n if bed_directory is not None:\\n logging.info(\\\"Annotation using bed files\\\")\\n generate_bed_file_annotations(loci=loci, bed_directory=bed_directory, output_directory=output_directory) \\n logging.info(\\\"Finemapping file preparation complete\\\")\",\n \"def get_batch_data(cur_samples, is_training=False):\\n psg1_max_len, psg2_max_len, batch_max_path_cnt, psg1_max_ems_cnt, psg2_max_ems_cnt, \\\\\\n joint_max_ent_cnt, psg1_max_sent, psg2_max_sent, max_arg1_mention, max_arg2_mention = get_max_len(cur_samples)\\n psg1_max_len += 1\\n\\n psg1_words_list = list()\\n psg1_mask_list = list()\\n psg2_words_list = list()\\n psg2_mask_list = list()\\n psg1_coref_list = list()\\n psg2_coref_list = list()\\n arg1_s_list = []\\n arg1_e_list = []\\n arg2_s_list = []\\n arg2_e_list = []\\n path_e1_s = []\\n path_e1_e = []\\n path_c1_s = []\\n path_c1_e = []\\n path_c2_s = []\\n path_c2_e = []\\n path_e2_s = []\\n path_e2_e = []\\n path_mask = []\\n arg1_mask = []\\n arg2_mask = []\\n\\n ems_path_e1 = []\\n ems_path_c1 = []\\n ems_path_c2 = []\\n ems_path_e2 = []\\n ems_path_mask = []\\n\\n psg1_ems_start_list = []\\n psg1_ems_end_list = []\\n psg1_ems_mask_list = []\\n psg1_ems_adj_mat_list = []\\n psg1_ems_words_mask_list = []\\n\\n psg2_ems_start_list = []\\n psg2_ems_end_list = []\\n psg2_ems_mask_list = []\\n psg2_ems_adj_mat_list = []\\n psg2_ems_words_mask_list = []\\n\\n joint_ent_list = []\\n joint_ent_mask_list = []\\n joint_ent_adj_mat_list = []\\n joint_ent_sent_mask_list = []\\n linked_entities_list = []\\n linked_entities_mask = []\\n\\n rel_labels_list = []\\n sample_idx = 0\\n for sample in cur_samples:\\n words_seq, words_mask = get_words_index_seq(sample.Psg1Words, psg1_max_len, True)\\n psg1_words_list.append(words_seq)\\n psg1_mask_list.append(words_mask)\\n\\n words_seq, words_mask = get_words_index_seq(sample.Psg2Words, psg2_max_len, False)\\n psg2_words_list.append(words_seq)\\n psg2_mask_list.append(words_mask)\\n\\n psg1_coref_list.append(get_padded_coref_seq(sample.Psg1Coref, psg1_max_len, True))\\n psg2_coref_list.append(get_padded_coref_seq(sample.Psg2Coref, psg2_max_len, False))\\n\\n cur_arg1_s = [sample_idx * psg1_max_len + idx[0] for idx in sample.Arg1Loc] + \\\\\\n [sample_idx * psg1_max_len for i in range(max_arg1_mention - len(sample.Arg1Loc))]\\n cur_arg1_e = [sample_idx * psg1_max_len + idx[1] for idx in sample.Arg1Loc] + \\\\\\n [sample_idx * psg1_max_len for i in range(max_arg1_mention - len(sample.Arg1Loc))]\\n\\n cur_arg2_s = [sample_idx * psg2_max_len + idx[0] for idx in sample.Arg2Loc] + \\\\\\n [sample_idx * psg2_max_len for i in range(max_arg2_mention - len(sample.Arg2Loc))]\\n cur_arg2_e = [sample_idx * psg2_max_len + idx[1] for idx in sample.Arg2Loc] + \\\\\\n [sample_idx * psg2_max_len for i in range(max_arg2_mention - len(sample.Arg2Loc))]\\n\\n arg1_s_list.append(cur_arg1_s)\\n arg1_e_list.append(cur_arg1_e)\\n arg2_s_list.append(cur_arg2_s)\\n arg2_e_list.append(cur_arg2_e)\\n\\n arg1_mask.append(get_arg_mask(sample.Arg1Loc, max_arg1_mention))\\n arg2_mask.append(get_arg_mask(sample.Arg2Loc, max_arg2_mention))\\n\\n path_mask_len = batch_max_path_cnt - len(sample.Paths[0])\\n cur_path_mask = [0 for i in range(len(sample.Paths[0]))] + [1 for i in range(path_mask_len)]\\n path_mask.append(cur_path_mask)\\n\\n cur_path_e1_s = [sample_idx * psg1_max_len + idx for idx in sample.Paths[0]] + \\\\\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\\n cur_path_e1_e = [sample_idx * psg1_max_len + idx for idx in sample.Paths[1]] + \\\\\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\\n\\n cur_path_c1_s = [sample_idx * psg1_max_len + idx for idx in sample.Paths[2]] + \\\\\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\\n cur_path_c1_e = [sample_idx * psg1_max_len + idx for idx in sample.Paths[3]] + \\\\\\n [sample_idx * psg1_max_len for i in range(path_mask_len)]\\n\\n cur_path_c2_s = [sample_idx * psg2_max_len + idx for idx in sample.Paths[4]] + \\\\\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\\n cur_path_c2_e = [sample_idx * psg2_max_len + idx for idx in sample.Paths[5]] + \\\\\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\\n\\n cur_path_e2_s = [sample_idx * psg2_max_len + idx for idx in sample.Paths[6]] + \\\\\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\\n cur_path_e2_e = [sample_idx * psg2_max_len + idx for idx in sample.Paths[7]] + \\\\\\n [sample_idx * psg2_max_len for i in range(path_mask_len)]\\n\\n path_e1_s.append(cur_path_e1_s)\\n path_e1_e.append(cur_path_e1_e)\\n\\n path_c1_s.append(cur_path_c1_s)\\n path_c1_e.append(cur_path_c1_e)\\n\\n path_c2_s.append(cur_path_c2_s)\\n path_c2_e.append(cur_path_c2_e)\\n\\n path_e2_s.append(cur_path_e2_s)\\n path_e2_e.append(cur_path_e2_e)\\n\\n cur_psg1_ems_s = [sample_idx * psg1_max_len + tup[0] for tup in sample.Psg1EMs] + \\\\\\n [sample_idx * psg1_max_len for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs))]\\n cur_psg1_ems_e = [sample_idx * psg1_max_len + tup[1] for tup in sample.Psg1EMs] + \\\\\\n [sample_idx * psg1_max_len for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs))]\\n cur_psg1_ems_mask = [0 for i in range(len(sample.Psg1EMs))] + \\\\\\n [1 for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs))]\\n psg1_ems_start_list.append(cur_psg1_ems_s)\\n psg1_ems_end_list.append(cur_psg1_ems_e)\\n psg1_ems_mask_list.append(cur_psg1_ems_mask)\\n\\n cur_psg1_words_mask = []\\n for tup in sample.Psg1EMsWords:\\n ems_words = [1 for idx in range(psg1_max_len)]\\n ems_words[tup[0]: tup[1] + 1] = [0 for i in range(tup[1] + 1 - tup[0])]\\n cur_psg1_words_mask.append(ems_words)\\n for i in range(psg1_max_ems_cnt - len(sample.Psg1EMs)):\\n cur_psg1_words_mask.append([0 for idx in range(psg1_max_len)])\\n psg1_ems_words_mask_list.append(cur_psg1_words_mask)\\n\\n cur_psg2_ems_s = [sample_idx * psg2_max_len + tup[0] for tup in sample.Psg2EMs] + \\\\\\n [sample_idx * psg2_max_len for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs))]\\n cur_psg2_ems_e = [sample_idx * psg2_max_len + tup[1] for tup in sample.Psg2EMs] + \\\\\\n [sample_idx * psg2_max_len for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs))]\\n cur_psg2_ems_mask = [0 for i in range(len(sample.Psg2EMs))] + \\\\\\n [1 for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs))]\\n psg2_ems_start_list.append(cur_psg2_ems_s)\\n psg2_ems_end_list.append(cur_psg2_ems_e)\\n psg2_ems_mask_list.append(cur_psg2_ems_mask)\\n\\n cur_psg2_words_mask = []\\n for tup in sample.Psg2EMsWords:\\n ems_words = [1 for idx in range(psg2_max_len)]\\n ems_words[tup[0]: tup[1] + 1] = [0 for i in range(tup[1] + 1 - tup[0])]\\n cur_psg2_words_mask.append(ems_words)\\n for i in range(psg2_max_ems_cnt - len(sample.Psg2EMs)):\\n cur_psg2_words_mask.append([0 for idx in range(psg2_max_len)])\\n psg2_ems_words_mask_list.append(cur_psg2_words_mask)\\n\\n cur_psg1_ems_adj_mat = np.zeros((psg1_max_ems_cnt, psg1_max_ems_cnt), dtype=np.float32)\\n cur_psg1_ems_adj_mat[:len(sample.Psg1EMs), :len(sample.Psg1EMs)] = sample.Psg1EMsAdjMat\\n psg1_ems_adj_mat_list.append(cur_psg1_ems_adj_mat)\\n\\n cur_psg2_ems_adj_mat = np.zeros((psg2_max_ems_cnt, psg2_max_ems_cnt), dtype=np.float32)\\n cur_psg2_ems_adj_mat[:len(sample.Psg2EMs), :len(sample.Psg2EMs)] = sample.Psg2EMsAdjMat\\n psg2_ems_adj_mat_list.append(cur_psg2_ems_adj_mat)\\n\\n cur_joint_ents = []\\n cur_linked_entities = []\\n\\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\\n for idx in sample.Arg1EMs:\\n cur_joint_ent[idx] = 1\\n cur_joint_ents.append(cur_joint_ent)\\n\\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\\n for idx in sample.Arg2EMs:\\n cur_joint_ent[psg1_max_ems_cnt + idx] = 1\\n cur_joint_ents.append(cur_joint_ent)\\n\\n for i in range(0, len(sample.BothPsgEntEMs), 2):\\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\\n for idx in sample.BothPsgEntEMs[i]:\\n cur_joint_ent[idx] = 1\\n for idx in sample.BothPsgEntEMs[i + 1]:\\n cur_joint_ent[psg1_max_ems_cnt + idx] = 1\\n cur_joint_ents.append(cur_joint_ent)\\n cur_linked_entities.append(2 + i)\\n\\n cur_ems_paths = []\\n for ems_e1_idx in sample.Arg1EMs:\\n for ems_e2_idx in sample.Arg2EMs:\\n for ems_link_idx in range(0, len(sample.BothPsgEntEMs), 2):\\n for ems_c1_idx in sample.BothPsgEntEMs[ems_link_idx]:\\n for ems_c2_idx in sample.BothPsgEntEMs[ems_link_idx + 1]:\\n cur_ems_paths.append((ems_e1_idx, ems_c1_idx, ems_c2_idx, ems_e2_idx))\\n cur_ems_paths = cur_ems_paths[:min(batch_max_path_cnt, len(cur_ems_paths))]\\n ems_path_mask_len = batch_max_path_cnt - len(cur_ems_paths)\\n cur_ems_path_mask = [0 for i in range(len(cur_ems_paths))] + [1 for i in range(ems_path_mask_len)]\\n ems_path_mask.append(cur_ems_path_mask)\\n cur_ems_path_e1 = [sample_idx * psg1_max_ems_cnt + tup[0] for tup in cur_ems_paths] + \\\\\\n [sample_idx * psg1_max_ems_cnt for i in range(ems_path_mask_len)]\\n ems_path_e1.append(cur_ems_path_e1)\\n cur_ems_path_c1 = [sample_idx * psg1_max_ems_cnt + tup[1] for tup in cur_ems_paths] + \\\\\\n [sample_idx * psg1_max_ems_cnt for i in range(ems_path_mask_len)]\\n ems_path_c1.append(cur_ems_path_c1)\\n cur_ems_path_c2 = [sample_idx * psg2_max_ems_cnt + tup[2] for tup in cur_ems_paths] + \\\\\\n [sample_idx * psg2_max_ems_cnt for i in range(ems_path_mask_len)]\\n ems_path_c2.append(cur_ems_path_c2)\\n cur_ems_path_e2 = [sample_idx * psg2_max_ems_cnt + tup[3] for tup in cur_ems_paths] + \\\\\\n [sample_idx * psg2_max_ems_cnt for i in range(ems_path_mask_len)]\\n ems_path_e2.append(cur_ems_path_e2)\\n\\n for i in range(len(sample.OnlyPsg1EntEMs)):\\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\\n for idx in sample.OnlyPsg1EntEMs[i]:\\n cur_joint_ent[idx] = 1\\n cur_joint_ents.append(cur_joint_ent)\\n\\n for i in range(len(sample.OnlyPsg2EntEMs)):\\n cur_joint_ent = [0 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)]\\n for idx in sample.OnlyPsg2EntEMs[i]:\\n cur_joint_ent[psg1_max_ems_cnt + idx] = 1\\n cur_joint_ents.append(cur_joint_ent)\\n\\n cur_linked_ents = [sample_idx * joint_max_ent_cnt + idx for idx in cur_linked_entities] + \\\\\\n [sample_idx * joint_max_ent_cnt for idx in range(joint_max_ent_cnt - len(cur_linked_entities))]\\n linked_entities_list.append(cur_linked_ents)\\n cur_linked_ents_mask = [0 for i in range(len(cur_linked_entities))] + \\\\\\n [1 for i in range(joint_max_ent_cnt-len(cur_linked_entities))]\\n linked_entities_mask.append(cur_linked_ents_mask)\\n\\n cur_joint_ents_sents_mask = []\\n for lst in sample.EntsSents:\\n cur_joint_ent_sents_mask = [1 for idx in range(psg1_max_sent + psg2_max_sent)]\\n for tup in lst:\\n if tup[0] == 1:\\n cur_joint_ent_sents_mask[tup[1]] = 0\\n else:\\n cur_joint_ent_sents_mask[psg1_max_sent + tup[1]] = 0\\n cur_joint_ents_sents_mask.append(cur_joint_ent_sents_mask)\\n\\n for i in range(joint_max_ent_cnt - len(cur_joint_ents)):\\n cur_joint_ent_sents_mask = [0 for idx in range(psg1_max_sent + psg2_max_sent)]\\n cur_joint_ents_sents_mask.append(cur_joint_ent_sents_mask)\\n\\n joint_ent_sent_mask_list.append(cur_joint_ents_sents_mask)\\n\\n cur_joint_ent_mask = [0 for i in range(len(cur_joint_ents))] + \\\\\\n [1 for i in range(joint_max_ent_cnt - len(cur_joint_ents))]\\n joint_ent_mask_list.append(cur_joint_ent_mask)\\n\\n cur_joint_ent_adj_mat = np.zeros((joint_max_ent_cnt, joint_max_ent_cnt), dtype=np.float32)\\n cur_joint_ent_adj_mat[:len(cur_joint_ents), :len(cur_joint_ents)] = sample.MergedAdjMat\\n\\n joint_ent_adj_mat_list.append(cur_joint_ent_adj_mat)\\n\\n for i in range(joint_max_ent_cnt - len(cur_joint_ents)):\\n cur_joint_ents.append([1 for idx in range(psg1_max_ems_cnt + psg2_max_ems_cnt)])\\n\\n joint_ent_list.append(cur_joint_ents)\\n\\n sample_idx += 1\\n\\n if is_training:\\n rel_labels_list.append(rel_name_to_idx[sample.Relation])\\n\\n return {'psg1_words': np.array(psg1_words_list, dtype=np.float32),\\n 'psg1_mask': np.array(psg1_mask_list),\\n 'psg1_coref': np.array(psg1_coref_list),\\n 'psg2_words': np.array(psg2_words_list, dtype=np.float32),\\n 'psg2_mask': np.array(psg2_mask_list),\\n 'psg2_coref': np.array(psg2_coref_list),\\n 'arg1_start': np.array(arg1_s_list),\\n 'arg1_end': np.array(arg1_e_list),\\n 'arg2_start': np.array(arg2_s_list),\\n 'arg2_end': np.array(arg2_e_list),\\n 'arg1_mask': np.array(arg1_mask),\\n 'arg2_mask': np.array(arg2_mask),\\n 'path_e1_s': np.array(path_e1_s),\\n 'path_e1_e': np.array(path_e1_e),\\n 'path_c1_s': np.array(path_c1_s),\\n 'path_c1_e': np.array(path_c1_e),\\n 'path_c2_s': np.array(path_c2_s),\\n 'path_c2_e': np.array(path_c2_e),\\n 'path_e2_s': np.array(path_e2_s),\\n 'path_e2_e': np.array(path_e2_e),\\n 'path_mask': np.array(path_mask),\\n 'psg1_ems_start': np.array(psg1_ems_start_list),\\n 'psg1_ems_end': np.array(psg1_ems_end_list),\\n 'psg1_ems_mask': np.array(psg1_ems_mask_list),\\n 'psg1_ems_adj_mat': np.array(psg1_ems_adj_mat_list),\\n 'psg1_ems_words_mask': np.array(psg1_ems_words_mask_list),\\n 'psg2_ems_start': np.array(psg2_ems_start_list),\\n 'psg2_ems_end': np.array(psg2_ems_end_list),\\n 'psg2_ems_mask': np.array(psg2_ems_mask_list),\\n 'psg2_ems_adj_mat': np.array(psg2_ems_adj_mat_list),\\n 'psg2_ems_words_mask': np.array(psg2_ems_words_mask_list),\\n 'joint_ent': np.array(joint_ent_list),\\n 'joint_ent_mask': np.array(joint_ent_mask_list),\\n 'joint_ent_adj_mat': np.array(joint_ent_adj_mat_list),\\n 'linked_entities_map': np.array(linked_entities_list),\\n 'linked_entities_mask':np.array(linked_entities_mask),\\n 'ems_path_e1': np.array(ems_path_e1),\\n 'ems_path_c1': np.array(ems_path_c1),\\n 'ems_path_c2': np.array(ems_path_c2),\\n 'ems_path_e2': np.array(ems_path_e2),\\n 'ems_path_mask': np.array(ems_path_mask)}, \\\\\\n {'target': np.array(rel_labels_list, dtype=np.int32)}\",\n \"def build(self, fasta_files, subtype_file):\\n\\n # Check subtype file\\n subtypes = {}\\n\\n for row in csv.reader(open(subtype_file,'r'),delimiter='\\\\t'):\\n name = row[0]\\n subt = row[1]\\n\\n subtypes[name] = subt.lower()\\n \\n if isinstance(fasta_files, str):\\n # Create list\\n fasta_files = [fasta_files]\\n\\n if len(fasta_files) != self._nloci:\\n raise Exception(\\\"Missing fasta file. {} fasta files provided for {} number of loci.\\\".format(len(fasta_files), self._nloci))\\n\\n concat = LociConcat()\\n sequences = concat.collect(fasta_files)\\n \\n for name,seqslist in sequences.iteritems():\\n this_subt = subtypes[name]\\n\\n for seqs in seqslist:\\n self.add(seqs, name, this_subt)\",\n \"def _main_extract_CDS(args = None, stdout = None, stderr = None,\\n gb_record_fmtdict = None,\\n gb_cds_fmtdict = None) :\\n if stdout is None :\\n stdout = sys.stdout\\n if stderr is None :\\n stderr = sys.stderr\\n if gb_record_fmtdict is None :\\n gb_record_fmtdict = _GB_RECORD_FMTDICT\\n if gb_cds_fmtdict is None :\\n gb_cds_fmtdict = _GB_CDS_FMTDICT\\n # Process arguments\\n if args is None :\\n parser = _makeParser_extract_CDS()\\n args = parser.parse_args()\\n args = _processArgsToLogic_extract_CDS(args, stdout, stderr,\\n gb_record_fmtdict, gb_cds_fmtdict)\\n # Go through the input files\\n uniqueSeq = dict()\\n i_file = 0\\n for fi in args.genbank_records :\\n i_file += 1\\n if args.verbose :\\n stderr.write(time.asctime() + \\\" - \\\" +\\n \\\"Processing file \\\" + str(i_file) + \\\" : \\\" +\\n os.path.basename(fi) + \\\" - \\\" +\\n \\\"N unique seq : \\\" + str(len(uniqueSeq.keys())) + \\\"\\\\n\\\")\\n record = SeqIO.parse(fi, \\\"genbank\\\")\\n for r in record :\\n if not args.actionFlags.get(\\\"DoCount\\\", False) :\\n (summaryString, uniqueSeq, newSeq) = (\\n _summarizeRecord(r, args.outfmt, args.hash, uniqueSeq))\\n stdout.write(summaryString)\\n else :\\n count = len([x for x in r.features if x.type == \\\"CDS\\\"])\\n stdout.write(r.annotations[\\\"gi\\\"] + \\\"\\\\t\\\" + str(count) + \\\"\\\\n\\\")\\n # Write unique sequences\\n if args.actionFlags.get(\\\"DoUniqueSequences\\\", False) :\\n with open(args.unique, \\\"w\\\") as fo :\\n for (k, v) in uniqueSeq.items() :\\n fo.write(\\\">\\\" + k + \\\"\\\\n\\\")\\n fo.write(v + \\\"\\\\n\\\")\",\n \"def generate_batches(source,n=20):\\n blist=[]\\n with open(source) as f_source:\\n start=next_n_lines(f_source, n); string=gen_string(start); blist.append(string)\\n while start!=[]: start=next_n_lines(f_source, n); string=gen_string(start); blist.append(string)\\n print('2. Generation of batches completed!')\\n return blist\",\n \"def add_batch(self,ligand_file_path,receptor_file_path,batch_predictions,batch_labels):\\n\\n\\n # extract meaningful names from the file path\\n def extract_file_from_path(file_path):\\n return file_path.split(\\\"/\\\")[-1]\\n\\n ligand_filenames = np.char.array(map(extract_file_from_path,ligand_file_path))\\n receptor_filenames = np.char.array(map(extract_file_from_path,receptor_file_path))\\n batch_pl_pairs = ligand_filenames + \\\",\\\" + receptor_filenames\\n\\n # sort the batch by protein-ligand pairs\\n order = batch_pl_pairs.argsort()\\n batch_pl_pairs = batch_pl_pairs[order]\\n batch_predictions = batch_predictions[order]\\n batch_labels = batch_labels[order]\\n\\n # check if all of the entries in the batch are unique\\n if not np.array_equal(batch_pl_pairs,np.unique(batch_pl_pairs)):\\n raise Exception(\\\"batch has duplicate entries\\\")\\n\\n # get binmask with True for each non-unique protein-ligand pair, False for unique protein-ligand pair\\n binmask_self = (np.searchsorted(batch_pl_pairs,self.pl_pairs, 'right') - np.searchsorted(batch_pl_pairs,self.pl_pairs,'left')) == 1\\n binmask_batch = (np.searchsorted(self.pl_pairs,batch_pl_pairs, 'right') - np.searchsorted(self.pl_pairs,batch_pl_pairs,'left')) == 1\\n\\n # check if the entries appended to each other have similar names\\n if not np.array_equal(batch_pl_pairs[binmask_batch],self.pl_pairs[binmask_self]):\\n raise Exception('Error while merging arrays. Names do not match')\\n\\n # check if labels are similar\\n if not np.array_equal(batch_labels[binmask_batch],self.labels[binmask_self]):\\n raise Exception('Error while merging arrays. Labels for the same example should be similar')\\n\\n # split into overlapping and not overlapping entries\\n overlap_pl_pairs = batch_pl_pairs[binmask_batch]\\n overlap_predictions = np.char.array(self.predictions[binmask_self])\\n batch_overlap_predictions = self.usd_format(batch_predictions[binmask_batch])\\n\\n # for known entries join all of the predictions together\\n overlap_predictions = overlap_predictions + \\\",\\\" + batch_overlap_predictions\\n overlap_labels = batch_labels[binmask_batch]\\n\\n # merge unique and not unique predictions\\n self.pl_pairs = np.hstack((self.pl_pairs[-binmask_self],batch_pl_pairs[-binmask_batch],overlap_pl_pairs))\\n self.predictions = np.hstack((self.predictions[-binmask_self],self.usd_format(batch_predictions[-binmask_batch]),overlap_predictions))\\n self.labels = np.hstack((self.labels[-binmask_self],batch_labels[-binmask_batch],overlap_labels))\\n\\n # now sort everything by the first column\\n order = self.pl_pairs.argsort()\\n self.pl_pairs = self.pl_pairs[order]\\n self.predictions = self.predictions[order]\\n self.labels = self.labels[order]\",\n \"def get_batch(self):\\n self._next_minibatch()\\n scans = np.empty((self.batch_size, 2, RANGE_BINS, HEADING_BINS), dtype=np.float32)\\n targets = np.empty((self.batch_size, 1, RANGE_BINS, HEADING_BINS), dtype=np.float32)\\n features = np.empty((self.batch_size, 2048, 3, 12), dtype=np.float32)\\n\\n long_ids = []\\n assert len(self.batch) == self.batch_size\\n for n,item in enumerate(self.batch):\\n long_ids.append(item['scan'] + \\\"_\\\" + item['image_id'])\\n # Select one feature if there are multiple versions\\n selected_features = random.choice(item['features'])\\n\\n if self.augment:\\n # random rotation by a 30 degree increment\\n rotation = random.randint(0,12)\\n ix = int(len(item['laser'])/12*rotation)\\n laser = np.roll(item['laser'], ix) # end rolls around to start\\n tgt_heading = np.array([normalize_angle(h + (math.pi/6)*rotation) for h in item['target_heading']])\\n feat = np.roll(selected_features, rotation, axis=1)\\n else:\\n laser = np.array(item['laser'], copy=True)\\n tgt_heading = item['target_heading']\\n feat = selected_features\\n\\n # missing part of scan\\n length = len(laser)\\n miss_start = random.randint(0, length)\\n miss_end = miss_start + int((360-self.laser_fov_deg)/360 * length)\\n laser[miss_start:miss_end] = -1\\n if miss_end >= length:\\n laser[:miss_end-length] = -1\\n\\n # dropout. Unlike conventional dropout, this occurs at both train and test time and is \\n # considered to represent missing return values in the laser scan.\\n drop = np.random.random_sample((len(laser),))\\n laser[drop < self.dropout] = -1 # Indicates missing return.\\n\\n scans[n, 1, :, :] = radial_occupancy(laser).transpose((2,0,1))\\n # add a range indicating channel\\n r = np.linspace(-0.5, 0.5, num=RANGE_BINS)\\n scans[:,0,:,:] = np.expand_dims(np.expand_dims(r, axis=0), axis=2)\\n targets[n, :, :, :] = radial_target(tgt_heading, item['target_range']).transpose((2,0,1))\\n features[n, :, :, :] = feat.transpose((2,0,1))\\n # features = np.zeros_like(features) # How does it work without image features?\\n # scans = np.zeros_like(scans) # How does it work with only image features?\\n # Normalize targets into a probability dist\\n targets /= targets.reshape(targets.shape[0], -1).sum(axis=1).reshape(-1, 1, 1, 1)\\n return scans, features, targets, long_ids\",\n \"def build_training_data():\\r\\n for i in range(len(FILE_NAMES)):\\r\\n input_text = read_file(FILE_NAMES[i])\\r\\n list_of_word_lines = limiting_sentence_length(input_text)\\r\\n data = create_training_data_file(list_of_word_lines, LANGUAGE[i])\\r\\n write_training_data(data, LANGUAGE[i])\\r\\n merge_training_data()\",\n \"def __divide_into_batches(self):\\n print('Creating batches for parallel execution')\\n num_suites = len(self.execution_file_json['suites'])\\n full_batches = num_suites // self.max_suites\\n print('- Full batches=%s' % full_batches)\\n if num_suites % self.max_suites > 0:\\n has_partial = True\\n else:\\n has_partial = False\\n print('- Partial batch at end: %s' % has_partial)\\n if has_partial:\\n total_batches = full_batches + 1\\n else:\\n total_batches = full_batches\\n print('- %s suites will be divided into %s container batches using max suites %s' % (\\n num_suites, total_batches, self.max_suites))\\n self.suite_batches = []\\n # split full batches\\n for batch_counter in range(0, full_batches):\\n start_index = batch_counter * self.max_suites\\n batch = []\\n for counter in range(start_index, start_index + self.max_suites):\\n batch.append(self.execution_file_json['suites'][counter])\\n self.suite_batches.append(batch)\\n print('- full batches created', self.suite_batches)\\n # add partial batch\\n if has_partial:\\n start_index = full_batches * self.max_suites\\n batch = []\\n for counter in range(start_index, num_suites):\\n batch.append(self.execution_file_json['suites'][counter])\\n self.suite_batches.append(batch)\\n print('- partial batch created', self.suite_batches)\",\n \"def main():\\n # Verbosity: 1=Selection Results, >1 is various debugging information\\n verbose = 0\\n print \\\"build_all.py running with verbose=%s\\\"%(str(verbose))\\n if verbose:\\n print \\\"Fiducial Cut: \\\",fid_cut_hex,\\\"(apethum, z_min, z_max)\\\"\\n print \\\"Max Drift Distance = %.4f us\\\"%(max_drift_time)\\n\\n tree = get_data_tree(list='All') # Golden All\\n \\n # We use the EXOFitting processed tree to get high-level physical quantities\\n # like the anticorrelated energy, etc. \\n #ptree_file = ROOT.TFile(preprocessed_tree)\\n #ptree = ROOT.Get(\\\"dataTree\\\")\\n #if verbose: print \\\"Indexing EXOFitting PreProcessed tree\\\"\\n #ptree.BuildIndex(\\\"runNum\\\", \\\"eventNum\\\")\\n #if verbose: print \\\" ...done\\\"\\n\\n cuts = \\\"\\\"\\n\\n #There must be at least 1 scintillation cluster:\\n #cuts = \\\"@fScintClusters.size()>=1\\\"\\n #cuts = \\\"(fScintClusters.GetCountsOnAPDPlane(0)+fScintClusters.GetCountsOnAPDPlane(1))>20000\\\"\\n\\n # The minimum scintinlation counts must be > 20000 and <70000\\n # I observe that three peaks presumable alphas are at 38500, 42200, and 55000\\n # So Rn222=5.4MeV, Po218=6MeV, Po214=7.7MeV\\n # calibrate:: y=mx+b, m=6167, b=5198\\n #cuts = \\\"fScintClusters.fRawEnergy>20000 && fScintClusters.fRawEnergy<70000\\\"\\n #cuts += \\\"&& fScintClusters.fRawEnergy>22000 && fScintClusters.fRawEnergy<80000\\\"\\n #cuts += \\\" && Sum$(fAPDs.fRawCounts) > 8000\\\"\\n\\n # Ignore Noise and Muon tagged events\\n cuts +=\\\"fEventHeader.fTaggedAsNoise==0 && fEventHeader.fTaggedAsMuon==0\\\" \\n\\n # That's the last of the cuts, lets show the user what the cut looks like\\n print \\\"Applying Cuts to data: \\\\n%s\\\"%cuts\\n\\n #Draw is the fastest method to apply cuts, in the end what we want is a reduced data list\\n # to perform a more targeted analysis...\\n tree.Draw(\\\">>+elist_alpha_canidates\\\",cuts,\\\"goff\\\")\\n elist_alpha_canidates = ROOT.gDirectory.Get(\\\"elist_alpha_canidates\\\")\\n print \\\"There are %d events passing the initial cuts\\\"%elist_alpha_canidates.GetN()\\n\\n #Now we have to look at events passing the cuts individually\\n tf = ROOT.TFile(\\\"all.root\\\",\\\"RECREATE\\\")\\n Rntree = tree.CloneTree(0)\\n \\n for i in range(elist_alpha_canidates.GetN()):\\n # Print Progress\\n if i%int(elist_alpha_canidates.GetN()/20) == 0:\\n print \\\"%d of %d\\\"%(i,elist_alpha_canidates.GetN())\\n\\n #Grab the event data\\n tree.GetEntry(elist_alpha_canidates.GetEntry(i))\\n #ed = tree.EventBranch\\n #if verbose>1: print_event_data(ed,verbose)\\n\\n #is_alphaish = check_alpha_like(ed,verbose)\\n \\n #is the event a fully reconstructed BiPo?\\n #is_bipo = check_full_BiPo(ed,verbose)\\n\\n # Case1 (position matched Bi-Po)\\n #is_case1 = check_case1(ed,verbose)\\n #print \\\"BiPo=%s, Case1=%s\\\"%(is_bipo, is_case1) \\n #raw_input('<hit any key to continue>')\\n #if is_bipo or is_alphaish:\\n # Write the EventData of events which pass any of our selection criteria\\n # to ROOT file\\n Rntree.Fill()\\n\\n Rntree.AutoSave()\",\n \"def process_batch(self, batch):\\n # extend with current batch\\n self._extend(batch)\\n\\n # unpack and compute bounds\\n length = len(self.obs)\\n c = self.c\\n\\n # normally we cannot compute samples for the last c elements, but\\n # in the terminal case, we halluciante values where necessary\\n end = length if batch.terminal else length - c\\n\\n # collect samples to return in a FeudalBatch\\n feudal_batch = FeudalBatch()\\n for t in range(c, end):\\n\\n # state difference\\n s_diff = self.s[t + c] - self.s[t]\\n\\n # intrinsic reward\\n ri = 0\\n # note that this for loop considers s and g values\\n # 1 timestep to c timesteps (inclusively) ago\\n for i in range(1, c + 1):\\n ri_s_diff = self.s[t] - self.s[t - i]\\n if np.linalg.norm(ri_s_diff) != 0:\\n ri += cosine_similarity(ri_s_diff, self.g[t - i])\\n ri /= c\\n\\n # sum of g values used to derive w, input to the linear transform\\n gsum = np.zeros_like(self.g[t - c])\\n for i in range(t - c, t + 1):\\n gsum += self.g[i]\\n\\n # add to the batch\\n feudal_batch.add(self.obs[t], self.a[t], self.returns[t], s_diff,\\n ri, gsum, self.features[t])\\n\\n # in the terminal case, set reset flag\\n if batch.terminal:\\n self.last_terminal = True\\n # in the general case, forget all but the last 2 * c elements\\n # reason being that the first c of those we have already computed\\n # a batch for, and the second c need those first c\\n else:\\n twoc = 2 * self.c\\n self.obs = self.obs[-twoc:]\\n self.a = self.a[-twoc:]\\n self.returns = self.returns[-twoc:]\\n self.s = self.s[-twoc:]\\n self.g = self.g[-twoc:]\\n self.features = self.features[-twoc:]\\n\\n return feudal_batch.get_batch()\",\n \"def __concatonate_files(self, new_file_name, parent_folder):\\n\\n # make the output directory\\n output_file = self.save_directory + \\\"/\\\" + new_file_name\\n\\n # check if save_directory exists\\n if not os.path.exists(self.save_directory):\\n try:\\n # make the directory\\n os.makedirs(self.save_directory)\\n except PermissionError:\\n # if the user is unable to write to this directory, we should not continue\\n print(\\\"You do not have the correct permissions for creating a directory here. Please try again.\\\")\\n exit(-1)\\n\\n barcode_files = []\\n for root, directory, files in os.walk(parent_folder):\\n # we need to know where each file is in the barcode folder so we can read data from it\\n for name in files:\\n barcode_files.append( os.path.join(root, name) )\\n\\n with open(output_file, 'w') as writer:\\n for name in barcode_files:\\n with open(name, 'r') as reader:\\n for line in reader:\\n writer.write(line)\",\n \"def generate_sample_sheet(self):\\n pool = self.pool\\n bcl2fastq_sample_ids = []\\n i7_names = []\\n i7_sequences = []\\n i5_names = []\\n i5_sequences = []\\n wells = []\\n plate = pool.container.external_id\\n sample_ids = []\\n sequencer_type = self.sequencer.equipment_type\\n\\n for component in pool.components:\\n lp_composition = component['composition']\\n # Get the well information\\n wells.append(lp_composition.container.well_id)\\n # Get the i7 index information\\n i7_comp = lp_composition.i7_composition.primer_set_composition\\n i7_names.append(i7_comp.external_id)\\n i7_sequences.append(i7_comp.barcode)\\n # Get the i5 index information\\n i5_comp = lp_composition.i5_composition.primer_set_composition\\n i5_names.append(i5_comp.external_id)\\n i5_sequences.append(i5_comp.barcode)\\n # Get the sample id\\n sample_id = lp_composition.normalized_gdna_composition.\\\\\\n gdna_composition.sample_composition.content\\n sample_ids.append(sample_id)\\n\\n # Transform te sample ids to be bcl2fastq-compatible\\n bcl2fastq_sample_ids = [\\n SequencingProcess._bcl_scrub_name(sid) for sid in sample_ids]\\n # Reverse the i5 sequences if needed based on the sequencer\\n i5_sequences = SequencingProcess._sequencer_i5_index(\\n sequencer_type, i5_sequences)\\n\\n data = SequencingProcess._format_sample_sheet_data(\\n bcl2fastq_sample_ids, i7_names, i7_sequences, i5_names,\\n i5_sequences, wells=wells, sample_plate=plate,\\n description=sample_ids, sample_proj=self.run_name,\\n lanes=self.lanes, sep=',')\\n\\n contacts = {c.name: c.email for c in self.contacts}\\n pi = self.principal_investigator\\n principal_investigator = {pi.name: pi.email}\\n sample_sheet_dict = {\\n 'comments': SequencingProcess._format_sample_sheet_comments(\\n principal_investigator=principal_investigator,\\n contacts=contacts),\\n 'IEMFileVersion': '4',\\n 'Investigator Name': pi.name,\\n 'Experiment Name': self.experiment,\\n 'Date': str(self.date),\\n 'Workflow': 'GenerateFASTQ',\\n 'Application': 'FASTQ Only',\\n 'Assay': self.assay,\\n 'Description': '',\\n 'Chemistry': 'Default',\\n 'read1': self.fwd_cycles,\\n 'read2': self.rev_cycles,\\n 'ReverseComplement': '0',\\n 'data': data}\\n return SequencingProcess._format_sample_sheet(sample_sheet_dict)\",\n \"def build(self):\\n cursor = self.cnx.cursor()\\n query = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS `FileNameFilter_scores` (\\n id BIGINT unsigned NOT NULL,\\n score double DEFAULT NULL,\\n PRIMARY KEY(id),\\n CONSTRAINT `FNF_unique_file1_id` FOREIGN KEY (`id`)\\n REFERENCES `unique_file` (`id`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n ) ENGINE=InnoDB\\n \\\"\\\"\\\"\\n cursor.execute(query)\\n self.cnx.commit()\\n\\n query = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS FileNameFilter_unique_name (\\n id BIGINT UNSIGNED NOT NULL AUTO_INCREMENT,\\n file_name VARCHAR(255) CHARACTER SET utf8 COLLATE utf8_bin NOT NULL,\\n unique_path_id INT(10) NOT NULL,\\n count INT DEFAULT 1,\\n PRIMARY KEY (id),\\n UNIQUE INDEX file_path_idx USING BTREE (file_name ASC, unique_path_id),\\n INDEX file_name_idx USING BTREE (file_name ASC)\\n ) ENGINE=InnoDB;\\n \\\"\\\"\\\"\\n cursor.execute(query)\\n self.cnx.commit()\\n cursor.close()\",\n \"def start_queryResult_generator(inFile, fDic, work_sheet):\\n \\\"\\\"\\\" http://biopython.org/DIST/docs/api/Bio.SearchIO.BlastIO-module.html\\\"\\\"\\\"\\n qGenerator = SearchIO.parse(inFile, 'blast-xml')\\n max_hits = 0\\n query_count = 1\\n # Step through all the records in the lump xml data file and write out\\n # each separate hit to file. Also write the summary information to the\\n # work sheet.\\n for query_result in qGenerator:\\n print('Processing Query BLAST return ' + str(query_count))\\n number_hits = int(len(query_result.hits))\\n # Extend header out right if new MAXHITS\\n if number_hits > max_hits:\\n max_hits = number_hits \\n if number_hits == 0:\\n # Construct path plus file name for no hit query\\n filename = str(fDic['topDir'] + fDic['noHit'] + 'Query_' \\n + str(query_count) + '_H_none.xml')\\n # Write out any Queries that had to hits to a no Hit subfolder\\n SearchIO.write(query_result, filename, 'blast-xml')\\n write_qr_to_ws(query_count, query_result, work_sheet)\\n else :\\n # Now set up a counter of 'hits' in the QueryResult so hit's\\n # can be sliced away into their own record cleanly.\\n hit_count = 0;\\n for hit in query_result.hits:\\n total_hsps = len (hit.hsps)\\n lowest_eval = hit.hsps[0].evalue\\n best_hsp = hit.hsps[0]\\n for hsp in hit.hsps:\\n if hsp.evalue < lowest_eval:\\n lowest_eval = hsp.evalue\\n best_hsp = hsp\\n filename = str(fDic['topDir'] + outputFileName(query_count, hit, best_hsp))\\n SearchIO.write(query_result[hit_count:(hit_count + 1)], filename , 'blast-xml')\\n hit_count += 1\\n # Write out query_result to worksheet \\n write_qr_to_ws(query_count, query_result, work_sheet)\\n query_count += 1\\n # break is debugging code\\n # if query_count == 20:\\n # break\\n build_ws_header(work_sheet, max_hits)\\n return qGenerator\",\n \"def test_split_fasta_on_sample_ids_to_files(self):\\r\\n temp_output_dir = mkdtemp()\\r\\n self.dirs_to_remove.append(temp_output_dir)\\r\\n\\r\\n split_fasta_on_sample_ids_to_files(\\r\\n parse_fasta(self.fasta2),\\r\\n output_dir=temp_output_dir,\\r\\n per_sample_buffer_size=2)\\r\\n self.files_to_remove.extend(glob('%s/*fasta' % temp_output_dir))\\r\\n\\r\\n # confirm that all files are as expected\\r\\n self.assertEqual(open('%s/Samp1.fasta' % temp_output_dir).read(),\\r\\n \\\">Samp1_42\\\\nACCGGTT\\\\n>Samp1_43 some comme_nt\\\\nAACCG\\\\n>Samp1_44\\\\nA\\\\n\\\")\\r\\n self.assertEqual(open('%s/s2_a.fasta' % temp_output_dir).read(),\\r\\n \\\">s2_a_50\\\\nGGGCCC\\\\n\\\")\\r\\n self.assertEqual(open('%s/s3.fasta' % temp_output_dir).read(),\\r\\n \\\">s3_25\\\\nAAACCC\\\\n\\\")\\r\\n # confirm number of files is as expected\\r\\n self.assertEqual(len(glob('%s/*' % temp_output_dir)), 3)\",\n \"def merge_walkupseq_files(latest_tsca_id):\\n paths = glob.glob('walkupseq_files/*sample_info*')\\n\\n dfs = []\\n for f in paths:\\n tmp = pd.read_table(f, encoding='latin1')\\n dfs.append(tmp)\\n\\n df = pd.concat(dfs, axis=0)\\n df.to_csv('walkupseq_files/walkupseq_all_combined_%s.txt'%latest_tsca_id, sep=\\\"\\\\t\\\", index=None)\\n return df\",\n \"def make_all(self):\\n # General matrices #\\n self.tsv_seq_to_concepts()\\n self.tsv_seq_to_names()\\n self.list_sequence_concept()\\n # Only in the with 'samples' case #\\n if self.a.abundances: self.tsv_samples_to_names()\\n if self.a.abundances: self.biom_output()\\n # Graphical outputs #\\n self.per_seq_dot_files()\\n if self.a.abundances: self.per_sample_dot_files()\",\n \"def get_result_files(self):\\n name_pattern = \\\"{mapper}.{ngs_library.name}\\\"\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"out\\\", name_pattern + \\\"{ext}\\\"), ext=EXT_VALUES\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"log\\\", \\\"{mapper}.{ngs_library.name}.{ext}\\\"),\\n ext=(\\n \\\"log\\\",\\n \\\"conda_info.txt\\\",\\n \\\"conda_list.txt\\\",\\n \\\"log.md5\\\",\\n \\\"conda_info.txt.md5\\\",\\n \\\"conda_list.txt.md5\\\",\\n ),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.{report}.txt\\\"\\n ),\\n report=(\\\"bamstats\\\", \\\"flagstats\\\", \\\"idxstats\\\"),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.{report}.txt.md5\\\"\\n ),\\n report=(\\\"bamstats\\\", \\\"flagstats\\\", \\\"idxstats\\\"),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.bamstats.html\\\"\\n )\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.bamstats.html.md5\\\"\\n )\\n )\\n\\n for sheet in self.shortcut_sheets:\\n for ngs_library in sheet.all_ngs_libraries:\\n if ngs_library.name in self.ngs_library_to_kit:\\n extraction_type = ngs_library.test_sample.extra_infos[\\\"extractionType\\\"]\\n suffix = (\\n \\\"_long\\\"\\n if ngs_library.extra_infos[\\\"seqPlatform\\\"] in (\\\"PacBio\\\", \\\"ONP\\\")\\n else \\\"\\\"\\n )\\n # Per-sample target coverage report.\\n yield from expand(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"cov_qc\\\", name_pattern + \\\".{ext}\\\"\\n ),\\n mapper=self.config[\\\"tools\\\"][extraction_type.lower() + suffix],\\n ngs_library=[ngs_library],\\n ext=[\\\"txt\\\", \\\"txt.md5\\\"],\\n )\\n yield \\\"output/target_cov_report/out/target_cov_report.txt\\\"\\n yield \\\"output/target_cov_report/out/target_cov_report.txt.md5\\\"\\n if (\\n self.config[\\\"picard_hs_metrics\\\"][\\\"path_targets_interval_list\\\"]\\n and self.config[\\\"picard_hs_metrics\\\"][\\\"path_baits_interval_list\\\"]\\n ):\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"picard_hs_metrics\\\", name_pattern + \\\".txt\\\"\\n )\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"picard_hs_metrics\\\", name_pattern + \\\".txt.md5\\\"\\n )\\n )\\n if self.config[\\\"compute_coverage_bed\\\"]:\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"report\\\", \\\"coverage\\\", name_pattern + \\\"{ext}\\\"),\\n ext=(\\\".bed.gz\\\", \\\".bed.gz.tbi\\\"),\\n )\\n else:\\n print(\\n \\\"Genome-wide coverage BED generation disabled\\\", file=sys.stderr\\n ) # pragma: no cover\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.672763","0.58610225","0.5566432","0.5434747","0.5414289","0.5365546","0.53649545","0.5345907","0.5329101","0.52768123","0.52546614","0.5217361","0.5212177","0.5211676","0.5201498","0.5170869","0.5153864","0.5102112","0.5095486","0.509299","0.5088659","0.5079078","0.5077062","0.50588095","0.5046648","0.5032905","0.5029239","0.5021231","0.4982987","0.49702692","0.49553558","0.4942674","0.49408212","0.4936943","0.49357447","0.49331656","0.49249196","0.49176985","0.4915322","0.49093476","0.4904611","0.48976213","0.48937595","0.4879698","0.4879388","0.487499","0.48681104","0.4853083","0.48474678","0.48451877","0.48327225","0.48253003","0.48164722","0.48149142","0.4809307","0.4803546","0.47971258","0.47965774","0.47924852","0.4783548","0.47807804","0.47758272","0.4774097","0.4771749","0.47637168","0.47628757","0.47594526","0.47590452","0.4754957","0.47506037","0.47502518","0.47501397","0.47457293","0.474461","0.47368878","0.47284898","0.47256526","0.47249252","0.47183537","0.47146443","0.47139233","0.47138545","0.47078738","0.47068512","0.47046235","0.47042778","0.47035906","0.47034103","0.4702785","0.4695712","0.46943644","0.46908936","0.46837568","0.46828914","0.46823037","0.46774766","0.4674112","0.46722856","0.46714824","0.46615916"],"string":"[\n \"0.672763\",\n \"0.58610225\",\n \"0.5566432\",\n \"0.5434747\",\n \"0.5414289\",\n \"0.5365546\",\n \"0.53649545\",\n \"0.5345907\",\n \"0.5329101\",\n \"0.52768123\",\n \"0.52546614\",\n \"0.5217361\",\n \"0.5212177\",\n \"0.5211676\",\n \"0.5201498\",\n \"0.5170869\",\n \"0.5153864\",\n \"0.5102112\",\n \"0.5095486\",\n \"0.509299\",\n \"0.5088659\",\n \"0.5079078\",\n \"0.5077062\",\n \"0.50588095\",\n \"0.5046648\",\n \"0.5032905\",\n \"0.5029239\",\n \"0.5021231\",\n \"0.4982987\",\n \"0.49702692\",\n \"0.49553558\",\n \"0.4942674\",\n \"0.49408212\",\n \"0.4936943\",\n \"0.49357447\",\n \"0.49331656\",\n \"0.49249196\",\n \"0.49176985\",\n \"0.4915322\",\n \"0.49093476\",\n \"0.4904611\",\n \"0.48976213\",\n \"0.48937595\",\n \"0.4879698\",\n \"0.4879388\",\n \"0.487499\",\n \"0.48681104\",\n \"0.4853083\",\n \"0.48474678\",\n \"0.48451877\",\n \"0.48327225\",\n \"0.48253003\",\n \"0.48164722\",\n \"0.48149142\",\n \"0.4809307\",\n \"0.4803546\",\n \"0.47971258\",\n \"0.47965774\",\n \"0.47924852\",\n \"0.4783548\",\n \"0.47807804\",\n \"0.47758272\",\n \"0.4774097\",\n \"0.4771749\",\n \"0.47637168\",\n \"0.47628757\",\n \"0.47594526\",\n \"0.47590452\",\n \"0.4754957\",\n \"0.47506037\",\n \"0.47502518\",\n \"0.47501397\",\n \"0.47457293\",\n \"0.474461\",\n \"0.47368878\",\n \"0.47284898\",\n \"0.47256526\",\n \"0.47249252\",\n \"0.47183537\",\n \"0.47146443\",\n \"0.47139233\",\n \"0.47138545\",\n \"0.47078738\",\n \"0.47068512\",\n \"0.47046235\",\n \"0.47042778\",\n \"0.47035906\",\n \"0.47034103\",\n \"0.4702785\",\n \"0.4695712\",\n \"0.46943644\",\n \"0.46908936\",\n \"0.46837568\",\n \"0.46828914\",\n \"0.46823037\",\n \"0.46774766\",\n \"0.4674112\",\n \"0.46722856\",\n \"0.46714824\",\n \"0.46615916\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7443647"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11581,"cells":{"query":{"kind":"string","value":"Runs GenotypeGVCFs on all combined files produced previosuly (assumes folder structure)"},"document":{"kind":"string","value":"def GenotypeGVCFs():\n #creates sbatch files to merge batches of batch_size genomics vcf\n cwd = os.getcwd()\n sbatch_files = []\n if not os.path.isdir(os.path.join(cwd, \"01_CombineGVCFs\")):\n sys.exit(\"Directory 01_CombineGVCFs does not exits exists, something went wrong here.\")\n if os.path.isdir(os.path.join(cwd, \"02_GenotypeGVCFs\")):\n print \"WARNING: 02_GenotypeGVCFs already present, assuming this step has been completed with success.\"\n return sbatch_files\n else:\n #create the folder structure\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"sbatch\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"std_err\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"std_out\"))\n os.mkdir(os.path.join(cwd, \"02_GenotypeGVCFs\", \"VCF\"))\n #Build the sbatch files for the join calling step\n working_dir = os.path.join(cwd, \"02_GenotypeGVCFs\")\n #now retrive the VCF stored in 01_CombineGVCFs/VCF/\n combined_gvcfs_to_process = []\n if len(CONFIG[\"intervals_list\"]) == 0:\n #no intervals, I have one file for each batch\n combined_gvcf_files = []\n for current_batch in range(1, CONFIG[\"batch_number\"] +1):\n # for each batch create the vcf file that need to be created by combine step\n combined_gvcf_name = \"{}_batch{}.g.vcf.gz\".format(CONFIG[\"output_header\"], current_batch)\n combined_gvcf_full_path = os.path.join(cwd, \"01_CombineGVCFs\", \"VCF\", combined_gvcf_name)\n combined_gvcf_files.append(combined_gvcf_full_path)\n combined_gvcfs_to_process.append(combined_gvcf_files)\n else:\n for interval in CONFIG[\"intervals_list\"]:\n interval_name = os.path.basename(interval).split(\".\")[0]\n combined_gvcf_files = []\n for current_batch in range(1, CONFIG[\"batch_number\"] +1):\n # for each batch create the vcf file that need to be created by combine step\n combined_gvcf_name = \"{}_batch{}_{}.g.vcf.gz\".format(CONFIG[\"output_header\"], current_batch, interval_name)\n combined_gvcf_full_path = os.path.join(cwd, \"01_CombineGVCFs\", \"VCF\", combined_gvcf_name)\n combined_gvcf_files.append(combined_gvcf_full_path)\n #now ceate a list with interval file and all gvcf to be combines\n interval_plus_gvcfs = [interval ,combined_gvcf_files]\n combined_gvcfs_to_process.append(interval_plus_gvcfs)\n for interval_plus_gvcfs in combined_gvcfs_to_process:\n interval = interval_plus_gvcfs[0]\n combined_gvcf_files = interval_plus_gvcfs[1]\n sbatch_file = build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, CONFIG[\"scratch\"], interval)\n sbatch_files.append(sbatch_file)\n return sbatch_files"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def genotype_gvcfs(gatk, xmx, cores,\n inputs, output,\n reference, bed_file=None):\n commands = []\n command = GENOTYPEGVCFS_TEMPLATE.format(xmx, gatk, reference, output)\n command = command + ' --variant ' + ' --variant '.join(inputs)\n if bed_file is not None:\n command = command + \" -L \" + bed_file\n commands.append(command)\n output = os.path.join(os.path.dirname(output), 'all_sites.vcf')\n command = GENOTYPEGVCFS_TEMPLATE.format(xmx, gatk, reference, output)\n command = command + ' --variant ' + ' --variant '.join(inputs)\n command = command + ' --includeNonVariantSites'\n if bed_file is not None:\n command = command + \" -L \" + bed_file\n commands.append(command)\n queue_jobs(commands, \"genotypeGVCFs\", cores)","def build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, scratch=False, interval=None):\n \n name_batch1 = os.path.basename([item for item in combined_gvcf_files if \"batch1\" in item][0])\n interval_name = \"\"\n #there must be at least one batch so look for it, not elegant but works\n if name_batch1.split(\"batch1\") != \".g.vcf.gz\":\n interval_name = name_batch1.split(\"batch1\")[1].split(\".\")[0]\n job_name = \"GenotypeGVCFs{}\".format(interval_name)\n output_file = \"{}_joincalled{}.g.vcf.gz\".format(CONFIG[\"output_header\"], interval_name)\n #create the sbatch file to analyse the current batch of samples\n sbatch_file = os.path.join(working_dir, \"sbatch\", \"{}.sbatch\".format(job_name))\n with open(sbatch_file, \"w\") as GenotypeGVCFs:\n slurm = slurm_header(CONFIG[\"uppmax_project\"], job_name, working_dir)\n GenotypeGVCFs.write(slurm)\n GenotypeGVCFs.write(\"\\n\")\n #rsync to scratch all samples\n if scratch:\n GenotypeGVCFs.write(\"mkdir -p $SNIC_TMP/{} \\n\".format(job_name)) # create tmp directory\n GenotypeGVCFs.write(\"mkdir -p $SNIC_TMP/{}/VCF/ \\n\".format(job_name)) # create tmp directory\n #now cycle over the samples, build the GATK command\n combined_gvcf_string_input = \"\"\n for combined_gvcf in combined_gvcf_files:\n combined_gvcf_path_dir = combined_gvcf\n if scratch:\n GenotypeGVCFs.write(\"rsync -rptoDLv {}* $SNIC_TMP/{}/\\n\".format(combined_gvcf, job_name))\n combined_gvcf_name = os.path.basename(combined_gvcf)\n combined_gvcf_path_dir = \"$SNIC_TMP/{}/{}\".format(job_name, combined_gvcf_name)\n combined_gvcf_string_input += \"-V {} \\\\\\n\".format(combined_gvcf_path_dir)\n\n GATK_command= \"java -Xmx250g -jar {} -T GenotypeGVCFs \\\\\\n\".format(CONFIG[\"GATK\"])\n for option in CONFIG[\"walkers\"][\"GenotypeGVCFs\"]:\n GATK_command += \"{} \\\\\\n\".format(option)\n GATK_command += \"{} \".format(combined_gvcf_string_input)\n if interval is not None:\n GATK_command += \"-L {} \\\\\\n\".format(interval)\n\n if scratch:\n GATK_command += \"-o $SNIC_TMP/{}/VCF/{}\\n\".format(job_name, output_file)\n #once this is done rsync back to lupus\n GATK_command += \"rsync $SNIC_TMP/{}/VCF/{}* {}/VCF/\\n\".format(job_name, output_file , working_dir)\n else:\n GATK_command += \"-o {}/VCF/{}\\n\\n\".format(working_dir, output_file)\n GenotypeGVCFs.write(GATK_command)\n #return path to sbach file\n return sbatch_file","def main(arguments):\n folder_list = glob.glob(f\"{arguments.f}/*/\")\n for d in folder_list:\n if \"GALEN\" in d: continue\n # get vcf file\n try:\n vcf = glob.glob(f\"{d}/*.vcf\")[0]\n except:\n raise FileNotFoundError(f\"{d} - vcf file not found\")\n \n print(f\"Processing {vcf}...\")\n f = load(vcf)\n final = find_gene(f, arguments.c)\n output_file = vcf.replace(\".vcf\", \"_analyzed.csv\")\n final.to_csv(output_file, index=False)\n print(\"Done!\")","def merge_gvcfs(gatk, xmx, cores, gvcfs, reference):\n commands = []\n outputs = []\n no_groups = (len(gvcfs)/SPLIT_SIZE) + 1\n for i in range(0, no_groups):\n output = str(i) + '.g.vcf'\n outputs.append(output)\n command = MERGE_GVCFS_TEMPLATE.format(xmx, gatk, reference, output)\n command = command + '--variant ' + ' --variant '.join(gvcfs[i:(i*SPLIT_SIZE + SPLIT_SIZE)])\n commands.append(command)\n queue_jobs(commands, \"mergeGVCFs\", cores)\n return outputs","def combine_gvcf(self, reference, gvcf_list, output, input_is_sorted=False, extension_list=[\"g.vcf\",],\n tmp_dir=\"./tmp_combine_gvcf/\", max_files_per_merging=50, iteration=0, threads=None,\n remove_intermediate_files=False):\n\n filtered_gvcf_list = []\n for filename in gvcf_list:\n for extension in extension_list:\n if extension == filename[-len(extension):]:\n filtered_gvcf_list.append(filename)\n break\n \n if len(filtered_gvcf_list) <= max_files_per_merging:\n options = self.parse_options(reference, filtered_gvcf_list, output, input_is_sorted, extension_list=extension_list)\n self.execute(options, runtype=\"cp\")\n if remove_intermediate_files:\n shutil.rmtree(tmp_dir, ignore_errors=True)\n\n else:\n self.safe_mkdir(tmp_dir)\n iteration_dir = \"%s/iteration_%i/\" % (tmp_dir, iteration)\n self.safe_mkdir(iteration_dir)\n\n number_of_files = len(filtered_gvcf_list)\n\n bins = np.arange(0, number_of_files, max_files_per_merging)\n #print(bins)\n if bins[-1] != number_of_files:\n if number_of_files - bins[-1] < 2:\n bins[-1] = number_of_files\n else:\n bins = np.append(bins, number_of_files)\n\n output_file_list = []\n options_list = []\n\n merged_files = 0\n for i in range(0, len(bins)-1):\n output_file = \"%s/%i.g.vcf\" % (iteration_dir, i)\n output_file_list.append(output_file)\n #print(bins[i], bins[i+1])\n\n merged_files += bins[i+1] - bins[i]\n options_list.append(self.parse_options(reference,\n filtered_gvcf_list[bins[i]:bins[i+1]],\n output_file,\n input_is_sorted, extension_list=extension_list))\n print(\"%i/%i files will be merged\" % (merged_files, number_of_files))\n\n self.parallel_execute(options_list, threads=threads, runtype=\"cp\")\n\n self.combine_gvcf(reference, output_file_list, output, input_is_sorted=input_is_sorted,\n extension_list=extension_list,\n tmp_dir=tmp_dir,\n max_files_per_merging=max_files_per_merging, iteration=iteration+1)","def main():\n parser = argparse.ArgumentParser(description='MergeGVCFs and genotype them using the GATK')\n parser.add_argument('-g', '--gatk', dest='gatk', help=\"Location of the GATK\", required=True)\n parser.add_argument('-x', '--xmx', dest='xmx', help=\"Memory to use with JAVA\", required=True)\n parser.add_argument('-c', '--cores', dest='cores', help=\"Number of cores to use\")\n parser.add_argument('-o', '--output', dest='output', \n help='Final output from the haplotype caller')\n parser.add_argument('-r', '--reference', dest='reference', \n help='Reference FASTA file')\n parser.add_argument('-b','--bed', dest='bed_file',\n help=\"Bed file for limiting the GATK\")\n parser.add_argument('-p', '--ploidy', dest='ploidy', \n help=\"Sample ploidy\", default=2)\n parser.add_argument('-d', '--out_directory', dest='directory', help='Output director')\n parser.add_argument('bams', nargs=\"*\", help='gVCF variant call files output from the GATK')\n args = parser.parse_args()\n args.cores = int(args.cores)\n args.xmx = args.xmx.strip('\"')\n print args.bams\n genovcfs = haplotype_caller(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\n bams=args.bams, reference=args.reference,\n out_directory=args.directory, ploidy=args.ploidy, bed_file=args.bed_file)\n outputs = merge_gvcfs(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\n gvcfs=genovcfs, reference=args.reference)\n genotype_gvcfs(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\n inputs=outputs, output=args.output, reference=args.reference,bed_file=args.bed_file)\n #haplotype_single(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\n # inputs=args.gvcfs, reference=args.reference)","def main():\n\n # this will analyze all files in the input_files directory\n for folder in [x for x in os.listdir(os.path.join(os.getcwd(), 'test_directory')) if os.path.isdir(os.path.join(os.getcwd(), 'test_directory', x))]:\n try:\n print(f'Creating GED_Repo for files in {folder}')\n g = GED_Repo([os.path.join(os.getcwd(), 'test_directory', folder, f) for f in os.listdir(os.path.join(os.getcwd(), 'test_directory', folder)) if f.endswith('.ged')])\n g.check_data()\n g.print_data()\n g.print_individuals()\n g.print_families()\n except ValueError as v:\n print(v)\n except FileNotFoundError as f:\n print(f)","def main():\n\n file_list = []\n # this will analyze all files in the input_files directory\n for folder in [x for x in os.listdir(os.path.join(os.getcwd(), 'test_directory')) if os.path.isdir(os.path.join(os.getcwd(), 'test_directory', x))]:\n try:\n # print(f'Reading files in {folder}')\n file_list = file_list + [os.path.join(os.getcwd(), 'test_directory', folder, f) for f in os.listdir(os.path.join(os.getcwd(), 'test_directory', folder)) if f.endswith('.ged')]\n except ValueError as v:\n print(v)\n except FileNotFoundError as f:\n print(f)\n\n try:\n print(f'Analyzing final cumulative file data.')\n # print(file_list)\n g = GED_Repo(file_list)\n g.check_data()\n g.print_data()\n g.print_individuals()\n g.print_families()\n except ValueError as v:\n print(v)\n except FileNotFoundError as f:\n print(f)","def run(self):\n\n # If the specified outdir doesn't exist, make it.\n if os.path.exists(self.outdir) == False:\n os.mkdir(self.outdir)\n\n # Get occurrence data.\n self.get_gbif_occs()","def combineAllGraphFiles(chroms, final_out):\n outfile = open(final_out,'w');\n outfile.close();\n \n for chrom in chroms:\n graph_file = chrom + \".graph\";\n try:\n if os.system('%s %s >> %s' %\n (cat, graph_file, final_out)): raise\n except: sys.stderr.write(\"cat failed at %s\\n\" % chrom)","def run_concat_vcfs(job, context, vcf_ids, tbi_ids):\n\n work_dir = job.fileStore.getLocalTempDir()\n\n vcf_names = ['chrom_{}.vcf.gz'.format(i) for i in range(len(vcf_ids))]\n out_name = 'genome.vcf.gz'\n\n for vcf_id, tbi_id, vcf_name in zip(vcf_ids, tbi_ids, vcf_names):\n job.fileStore.readGlobalFile(vcf_id, os.path.join(work_dir, vcf_name))\n job.fileStore.readGlobalFile(tbi_id, os.path.join(work_dir, vcf_name + '.tbi'))\n\n cmd = ['bcftools', 'concat'] + [vcf_name for vcf_name in vcf_names] + ['-O', 'z']\n \n with open(os.path.join(work_dir, out_name), 'wb') as out_file:\n context.runner.call(job, cmd, work_dir=work_dir, outfile = out_file)\n\n cmd = ['tabix', '-f', '-p', 'vcf', out_name]\n context.runner.call(job, cmd, work_dir=work_dir)\n\n out_vcf_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name))\n out_tbi_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name + '.tbi'))\n\n return out_vcf_id, out_tbi_id","def FeaturesGen(ChopChopresults, outputDir, sgRNA_type):\n \n #make output Directory if it does not already exist\n if not os.path.isdir(outputDir):\n os.makedirs(outputDir)\n \n #list the directory contents \n for i,j,k in os.walk(ChopChopresults): #use walk to go through and find all directories\n \n if j == []: #no subdirectories\n saveDF = pd.DataFrame() #initiate dataframe\n for target in k: #loop through to find the sgRNA sequences\n if target.endswith('.offtargets'):\n with open(os.path.join(i,target), 'r+') as f:\n guide = f.readlines()\n #add them to a dataframe\n temp = pd.Series()\n temp['guideNo'] = target.split('.')[0] + sgRNA_type\n temp['guideSeq'] = guide.pop(0).rstrip()\n \n saveDF = saveDF.append(temp.to_frame().transpose())\n saveDF['type'] = 'sgRNA'\n \n if sgRNA_type == 'General' or sgRNA_type == None:\n saveDF['fwd'] = 'pink'\n saveDF['rev'] = 'green'\n elif sgRNA_type == 'GG':\n saveDF['fwd'] = 'yellow'\n saveDF['rev'] = 'plum'\n elif sgRNA_type == 'GA':\n saveDF['fwd'] = 'cyan'\n saveDF['rev'] = 'cornflower blue'\n \n \n #save to txt file with tab delimiter\n saveDF.to_csv(os.path.join(outputDir, os.path.basename(i) + '_features.txt'),\\\n index = False, header = False, sep = '\\t')\n \n del saveDF","def main():\n\n\n\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\n\n # fetch and sort the .mnc and .tag files\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\n tag_files = [f for f in skulls_folder if 'tag' in f]\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\n \n mnc_files.sort()\n tag_files.sort()\n mnc_names.sort()\n\n # Process and package ndarrays as tuples inside npy file\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\n \n print('\\n' * 5)\n\n # Push the npy files to GCP Cloud Storage\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)","def prepare_subset_vcf_files_by_population():\n if not os.path.exists(VCF_BY_POPULATION_PATH):\n print(\"preparing subset vcf by population\")\n os.makedirs(VCF_BY_POPULATION_PATH)\n sleep(10)\n vcf_tools_runner = VCFToolsDockerRunner()\n samples = glob(f\"{SAMPLES_FOLDER}/*.csv\")\n\n with tqdm(total=len(samples)) as pbar:\n for sample in samples:\n sample = sample.replace('\\\\', '/')\n sample_name = get_filename_from_path(sample)\n sample_path = \"/\".join([IMAGE_SHARE_FOLDER_PATH] + sample.split('/')[1:])\n pbar.set_description(f\"Processing {sample_name}\")\n vcf_tools_runner(\n f\"vcf-subset -c {sample_path} \"\n f\"{IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_VCF_FILE_NAME} | fill-an-ac > \"\n f\"{IMAGE_SHARE_FOLDER_PATH}/{VCF_BY_POPULATION_FOLDER}/{sample_name}.vcf\")\n pbar.update(1)\n else:\n print(f\"Subset VCF files by population already exist in: {VCF_BY_POPULATION_PATH}\")","def main():\n file_one_path, file_two_path, output_path =\\\n get_command_line_arguments(\n ['/home/ehler002/project/groups/go/Data/Cluster_Data/Dataset.txt',\n '/home/ehler002/project/groups/go/Data/Cluster_Data/translated_genes.fpkm_table',\n '/home/ehler002/project/groups/go/Data/Cluster_Data/Full_fpkm_Table.txt'])\n pattern = 'CRO_T'\n for file_path in [file_one_path, file_two_path]:\n assert os.path.exists(file_path), 'File %s does not exist.' % file_path\n start_time = datetime.datetime.now()\n print('Started concatenation at %s' % start_time)\n file_contents, headers = get_file_contents(file_two_path)\n file_contents = sort_file_contents(file_contents)\n file_contents = remove_pattern(file_contents, pattern)\n concatenate_files(file_one_path, file_contents, headers, output_path)\n print('Finished concatenation in %s' % (datetime.datetime.now() - start_time))","def process_cgc(path, return_dataframe=False, fusions=False):\n # read in data\n df = pd.read_table(path)\n\n # keep small somatic variants\n if not fusions:\n s = df['Mutation Types']\n is_small = s.str.contains('Mis|F|N|S').fillna(False)\n is_somatic = ~df['Tumour Types(Somatic)'].isnull()\n df = df[is_small & is_somatic].copy()\n\n # label oncogenes / TSG\n df['Is Oncogene (CGC)'] = 'No'\n df.loc[df['Role in Cancer'].fillna('').str.contains('oncogene'), 'Is Oncogene'] = 'Yes'\n df['Is Tumor Suppressor Gene (CGC)'] = 'No'\n df.loc[df['Role in Cancer'].fillna('').str.contains('TSG'), 'Is Tumor Suppressor Gene'] = 'Yes'\n df['Is Driver Gene (CGC)'] = 'Yes'\n\n # rename columns\n df = df.rename(columns={'Entrez GeneId': 'Entrez Gene ID', 'Gene Symbol': 'Hugo Symbol'})\n\n # get gene names\n if not return_dataframe:\n cgc_genes = df['Gene Symbol'].tolist()\n else:\n cgc_genes = df\n\n return cgc_genes\n else:\n # return fusion gene information\n has_fus_partner = ~df['Translocation Partner'].isnull()\n output_list = []\n for ix, row in df[has_fus_partner].iterrows():\n g1 = row[\"Gene Symbol\"]\n for g2 in row['Translocation Partner'].split(', '):\n output_list.append([g1, g2])\n output_df = pd.DataFrame(output_list, columns=[\"Gene1\", \"Gene2\"])\n output_df['GENE_ID'] = output_df['Gene1'] + '--' + output_df['Gene2']\n\n if not return_dataframe:\n cgc_genes = list(set(output_df[\"Gene1\"].unique()) | set(output_df[\"Gene2\"]))\n else:\n cgc_genes = output_df\n\n return cgc_genes","def main():\n\n args = parseArgs()\n\n path = args.path\n is_open_gl = args.g\n\n success, failure = genFiles(path, is_open_gl)\n\n print(\"Success: \", \", \".join(success))\n print(\"Failure: \", \", \".join(failure))\n\n ratio = len(success) / (len(success) + len(failure))\n\n print(\"%% success = %.2f\" % (100 * ratio))","def main():\n\n #Getthefiles\n all_plasmid_path = []\n path_to_all_info = '/Users/gustavotamasco/mdrkrp/project_MDR_KRP/all_vir_files'\n dirpath=os.getcwd()\n os.chdir(path_to_all_info)\n files = list_files_simple(path_to_all_info)\n\n bad_files = [\"Hemo_536_vfdb_genome.tsv\", \"MI_119_vfdb.tsv\", \"Hemo_536_vfdb.tsv\",\n \"MI_119_vfdb_genome.tsv\",\n \"URO_775_vfdb_genome.tsv\", \"Hemo_825_vfdb.tsv\", \"URO_775_vfdb.tsv\",\n \"Hemo_825_vfdb_genome.tsv\",\n \"MI_329_vfdb.tsv\", \"MI_569_vfdb_genome.tsv\", \"MI_329_vfdb_genome.tsv\",\n \"MI_569_vfdb.tsv\",\n \"Hemo_989_vfdb_genome.tsv\", \"MI_78_vfdb.tsv\", \"Hemo_989_vfdb.tsv\",\n \"MI_78_vfdb_genome.tsv\"]\n\n final_files = list([x for x in files if x not in bad_files])\n print(len(final_files))\n\n\n '''Building metadata'''\n #All genes to each genome\n metadata = {}\n for file in final_files:\n with open(file) as vir_info:\n parse_genes_v2(file, vir_info, metadata)\n\n\n #All genes that occured\n all_genes = sorted(set(get_all_genes(metadata)))\n print(all_genes)\n\n #All vir classess\n\n\n '''Build dataframe for the classes plot'''\n df_info = {}\n df_major_classes = build_class_df(df_info, all_genes, metadata)\n df = pd.DataFrame.from_dict(df_major_classes, orient='index', columns=['entA', 'entB', 'entE', 'entS', 'fepA', 'fepB', 'fepC', 'fepD', 'fepG', 'fimA', 'fimE', 'fyuA', 'irp1', 'irp2', 'mgtB', 'mgtC', 'ompA', 'xcpA/pilD', 'xcpR', 'yagV/ecpE', 'yagW/ecpD', 'yagX/ecpC', 'yagY/ecpB', 'yagZ/ecpA', 'ybtA', 'ybtE', 'ybtP', 'ybtQ', 'ybtS', 'ybtT', 'ybtU', 'ybtX', 'ykgK/ecpR'])\n #df = df.transpose()\n #df.to_csv('arg_genes.csv', sep='\\t', encoding='utf-8')\n #sns.set(font_scale=0.65)\n #Need both\n #not_full = sns.clustermap(df, label='small', cmap=\"vlag\", standard_scale=1, linewidths=0)\n full_plot = sns.clustermap(df, label='small', cmap=\"vlag\", linewidths=0)\n #plt.title('Antibiotic resistance genes across 34 organism', fontsize=15)\n #sns.set(font_scale=1)\n plt.show()\n full_plot.savefig(\"final_genome_plasmid_vir.pdf\", bbox_inches='tight')\n #not_full.savefig(\"final_genome_plasmid_vir_scalled.pdf\", bbox_inches='tight')","def GatherVcfs(\n b: hb.Batch,\n input_vcfs: List,\n disk_size: int,\n output_vcf_path: str = None,\n) -> Job:\n j = b.new_job('VQSR: FinalGatherVcf')\n j.image(utils.GATK_IMAGE)\n j.memory(f'16G')\n j.storage(f'{disk_size}G')\n j.declare_resource_group(\n output_vcf={'vcf.gz': f'{NAME}_gathered.vcf.gz', 'vcf.gz.tbi': f'{NAME}_gathered.vcf.gz.tbi'}\n )\n\n input_cmdl = ' '.join([f'--input {v}' for v in input_vcfs])\n j.command(\n f\"\"\"set -euo pipefail\n # --ignore-safety-checks makes a big performance difference so we include it in \n # our invocation. This argument disables expensive checks that the file headers \n # contain the same set of genotyped samples and that files are in order \n # by position of first record.\n gatk --java-options -Xms6g \\\\\n GatherVcfsCloud \\\\\n --gather-type BLOCK \\\\\n {input_cmdl} \\\\\n --output {j.output_vcf['vcf.gz']}\n tabix {j.output_vcf['vcf.gz']}\"\"\"\n )\n if output_vcf_path:\n b.write_output(j.output_vcf, f'{output_vcf_path}{NAME}_gathered{LABEL}')\n return j","def generate_all(files, alignement_h5f, input_h5f,\n nframes=7, vad=None):\n def try_remove(fname):\n try:\n os.remove(fname)\n except:\n pass\n try:\n directory = os.path.dirname(os.path.abspath(input_h5f))\n\n # create temporary files:\n _, fb_h5f = tempfile.mkstemp(dir=directory)\n _, fb_mvn_h5f = tempfile.mkstemp(dir=directory)\n os.remove(fb_h5f)\n os.remove(fb_mvn_h5f)\n\n # generate mfccs:\n h5features_compute(files, alignement_h5f, featfunc=do_mfccs)\n\n # generate stacked mvn fbanks:\n h5features_compute(files, fb_h5f, featfunc=do_fbank)\n mean_variance_normalisation(fb_h5f, fb_mvn_h5f, vad=vad)\n h5features_feats2stackedfeats(fb_mvn_h5f, input_h5f, nframes=nframes)\n finally:\n try_remove(fb_h5f)\n try_remove(fb_mvn_h5f)","def main():\n processSetOfCerFiles(sys.argv[1:])","def main():\n onlyfiles = [f for f in listdir(RAWDATA_PATH) if isfile(join(RAWDATA_PATH, f))]\n for file in onlyfiles:\n create_RCSB_fastas(file)","def main():\n nbin = 60\n nbinM = 100\n \n maxmag = -20\n minmag = -29\n mag = np.linspace(minmag,maxmag,nbinM) \n minz = 0.1\n maxz = 5\n z = np.linspace(minz,maxz,nbin)\n \n minv = 10**1.6\n maxv = 10**2.6\n vel = np.linspace(minv,maxv,nbin)\n \n # writing the script for galfic once and for all\n gl.write_script()\n\n result = [[] for i in range(5)]\n \n for zl in z:\n zrange = z[np.where(z > zl)]\n for zs in zrange:\n for v in vel:\n gl.write_initfile(v,zl,zs)\n os.system('./script_gl > /dev/null 2>&1')\n sigma = gl.analyse_output(mag,zs,zl,v)\n for i in range(nbinM):\n result[0].append(zs)\n result[1].append(zl)\n result[2].append(v)\n result[3].append(mag[i])\n result[4].append(sigma[i])\n\n np.savez('crosssection.npz',x=result)","def generate_files(self):\n\t\tapply_stemmer, xml_file, query_file, expected_file = self.read_config_file()\n\t\tself.generate_query_file(query_file, xml_file, apply_stemmer)\n\t\tself.generate_expected_file(expected_file, xml_file)\n\t\tlogging.info('FINALIZADO: MÓDULO PROCESSADOR DE CONSULTAS')","def cleanup_sub(vg_dir):\n\tnew_dir = vg_dir+'_clean'\n\tos.mkdir(new_dir)\n\tphot_vg_files = filter(lambda x: '.txt' in x, os.listdir(vg_dir))\n\t# phot_vg_phottot_files = filter(lambda x: 'phottot' in x, phot_vg_files)\n\tfor f in phot_vg_files:\n\t\tdf = pd.read_table(vg_dir+'/'+f,\n\t\t\tnames = ['id','ra','dec','flux','unc','x','y','flux_uncor'],\n\t\t\tdelim_whitespace=True)\n\t\tstarnums, dithers = zip(*[i.split('_')[1:4:2] for i in df.id])\n\t\tdf['id'] = [int(i) for i in starnums]\n\t\tdf['dither'] = [int(i) for i in dithers]\n\t\tsorted_df = df.sort(['id','dither'])\n\t\t# new: remove the aperture correction applied by varoujan to the uncertainties\n\t\tch = f.split('-')[2]\n\t\tif ch == 'ch1':\n\t\t\tsorted_df['unc'] /= 1.205\n\t\telif ch == 'ch2':\n\t\t\tsorted_df['unc'] /= 1.221\n\t\telse:\n\t\t\traise(TypeError(\"unexpected channel\"))\n\t\tfnew = '_'.join(f.split('-')[::2])+'_raw.csv'\n\t\tsorted_df.to_csv(new_dir+'/'+fnew, index=False, float_format='%.8f')\n\t\t# also calculate mean RA/Dec, flux, and quadrature sum uncertainty\n\t\tgrouped = sorted_df.groupby('id')\n\t\tagg = grouped[['ra','dec','flux']].aggregate(np.median)\n\t\tquadsum = grouped['unc'].aggregate(lambda x: np.sqrt(np.sum(x**2)))\n\t\tagg['unc'] = quadsum\n\t\tfnew = '_'.join(f.split('-')[::2])+'_agg.csv'\n\t\tagg.to_csv(new_dir+'/'+fnew, index=True, float_format='%.8f')","def __load_cogs(self):\n for cog in self.__cogs.get():\n logging.info('loading %s', cog)\n self.load_extension(cog)","def features_from_folder(label_folder, audio_folder, output_folder):\n print('Listing label files from folder.')\n #scan labels folder\n labels_list = os.listdir(label_folder)\n label_files = []\n for filename in labels_list:\n #get its extension\n file_extension = filename.split('.')[-1]\n if file_extension != 'txt':\n continue\n #save to without its extension\n label_files.append(filename[:-4])\n\n print('Listing audio files from folder.')\n #scan audio folder\n audios_list = os.listdir(audio_folder)\n audio_files = []\n for filename in audios_list:\n #get its extension\n file_extension = filename.split('.')[-1]\n if file_extension != 'wav':\n continue\n #save to without its extension\n audio_files.append(filename[:-4])\n\n print('Removing files without matches')\n #use only the files with matching audio/label\n files_to_process = []\n for label_file in label_files:\n if label_file in audio_files:\n files_to_process.append(label_file)\n\n print('Processing each file...')\n i = 1\n class_count = {}\n total_f = len(files_to_process)\n #for each file\n for processing in files_to_process:\n print('File', str(i) + '/' + str(total_f))\n i += 1\n\n #\n label_file = os.path.join(label_folder, processing + \".txt\")\n audio_file = os.path.join(audio_folder, processing + \".wav\")\n\n #get the segments from the corresponding label file\n segments = get_segments(label_file)\n\n #\n total_s = len(segments)\n j = 1\n #for each segment\n for segment in segments:\n print('\\tSegment', str(j) + '/' + str(total_s), segment['class'])\n j += 1\n\n if class_count.get(segment['class']) is None:\n class_count[segment['class']] = 1\n else:\n class_count[segment['class']] += 1\n output_filename = segment['class']\n output_filename += '-' + format(class_count[segment['class']], '04d')\n output_filename = os.path.join(output_folder, output_filename)\n\n #get its features\n segment_features = features_from_label(audio_file, segment)\n\n #save it to a file\n fe.write_as_bin(output_filename, segment_features)","def generate_megafile():\n\n print(\"\\nFetching testing dataset…\")\n testing = get_testing()\n\n print(\"\\nFetching ECDC dataset…\")\n ecdc = get_ecdc()\n\n location_mismatch = set(testing.location).difference(set(ecdc.location))\n for loc in location_mismatch:\n print(f\"<!> Location '{loc}' has testing data but is absent from ECDC data\")\n\n print(\"\\nFetching OxCGRT dataset…\")\n cgrt = get_cgrt()\n\n all_covid = (\n ecdc\n .merge(testing, on=[\"date\", \"location\"], how=\"outer\")\n .merge(cgrt, on=[\"date\", \"location\"], how=\"left\")\n .sort_values([\"location\", \"date\"])\n )\n\n # Add ISO codes\n print(\"Adding ISO codes…\")\n iso_codes = pd.read_csv(os.path.join(INPUT_DIR, \"iso/iso3166_1_alpha_3_codes.csv\"))\n\n missing_iso = set(all_covid.location).difference(set(iso_codes.location))\n if len(missing_iso) > 0:\n print(missing_iso)\n raise Exception(\"Missing ISO code for some locations\")\n\n all_covid = iso_codes.merge(all_covid, on=\"location\")\n\n # Add continents\n print(\"Adding continents…\")\n continents = pd.read_csv(\n os.path.join(INPUT_DIR, \"owid/continents.csv\"),\n names=[\"_1\", \"iso_code\", \"_2\", \"continent\"],\n usecols=[\"iso_code\", \"continent\"],\n header=0\n )\n\n all_covid = continents.merge(all_covid, on=\"iso_code\", how=\"right\")\n\n # Add macro variables\n # - the key is the name of the variable of interest\n # - the value is the path to the corresponding file\n macro_variables = {\n \"population\": \"un/population_2020.csv\",\n \"population_density\": \"wb/population_density.csv\",\n \"median_age\": \"un/median_age.csv\",\n \"aged_65_older\": \"wb/aged_65_older.csv\",\n \"aged_70_older\": \"un/aged_70_older.csv\",\n \"gdp_per_capita\": \"wb/gdp_per_capita.csv\",\n \"extreme_poverty\": \"wb/extreme_poverty.csv\",\n \"cardiovasc_death_rate\": \"gbd/cardiovasc_death_rate.csv\",\n \"diabetes_prevalence\": \"wb/diabetes_prevalence.csv\",\n \"female_smokers\": \"wb/female_smokers.csv\",\n \"male_smokers\": \"wb/male_smokers.csv\",\n \"handwashing_facilities\": \"un/handwashing_facilities.csv\",\n \"hospital_beds_per_thousand\": \"owid/hospital_beds.csv\",\n \"life_expectancy\": \"owid/life_expectancy.csv\",\n \"human_development_index\": \"un/human_development_index.csv\",\n }\n all_covid = add_macro_variables(all_covid, macro_variables)\n\n print(\"Writing to CSV…\")\n all_covid.to_csv(os.path.join(DATA_DIR, \"owid-covid-data.csv\"), index=False)\n\n print(\"Writing to XLSX…\")\n all_covid.to_excel(os.path.join(DATA_DIR, \"owid-covid-data.xlsx\"), index=False)\n\n print(\"Writing to JSON…\")\n df_to_json(all_covid, os.path.join(DATA_DIR, \"owid-covid-data.json\"), macro_variables.keys())\n\n # Store the last updated time\n timestamp_filename = os.path.join(DATA_DIR, \"owid-covid-data-last-updated-timestamp.txt\")\n with open(timestamp_filename, \"w\") as timestamp_file:\n timestamp_file.write(datetime.utcnow().replace(microsecond=0).isoformat())\n\n print(\"All done!\")","def gci(path):\n parents = os.listdir(path)\n for parent in parents:\n if parent == \"forgifs\" or parent == \"hilariousgifs\":\n pass\n else:\n child = os.path.join(path,parent)\n #print(child)\n if os.path.isdir(child):\n gci(child)\n else:\n filepath.append(child)\n #print(child)","def main():\n args = get_args()\n out_dir = args.outdir\n pct_gc = args.pct_gc\n\n if not os.path.isdir(out_dir):\n os.makedirs(out_dir)\n\n if not 0 < pct_gc <= 100:\n die('--pct_gc \"{}\" must be between 0 and 100'.format(pct_gc))\n\n num_seqs = 0\n for i, file in enumerate(args.fasta, start=1):\n if not os.path.isfile(file):\n warn('\"{}\" is not a file'.format(file))\n continue\n\n print('{:3}: {}'.format(i, os.path.basename(file)))\n\n base, ext = os.path.splitext(os.path.basename(file))\n high_file = os.path.join(out_dir, ''.join([base, '_high', ext]))\n low_file = os.path.join(out_dir, ''.join([base, '_low', ext]))\n\n high_fh = open(high_file, 'wt')\n low_fh = open(low_file, 'wt')\n\n for rec in SeqIO.parse(file, 'fasta'):\n num_seqs += 1\n bases = Counter(rec.seq.upper())\n gc = bases.get('G', 0) + bases.get('C', 0)\n pct = int((gc / len(rec.seq)) * 100)\n SeqIO.write(rec, low_fh if pct < pct_gc else high_fh, 'fasta')\n\n print('Done, wrote {} sequence{} to out dir \"{}\"'.format(\n num_seqs, '' if num_seqs == 1 else 's', out_dir))","def main():\n\n\t\"\"\"\n\tArguments:\n\t\troot_directory: The absolute path to the root directory of the BIOS source code\n\t\tsearch_directory: The absolute path to the directory from the code will start searching for .inf files\n\t\"\"\"\n\n\t\"\"\"\n\tReturns:\n\t\tBy the end of the execution of this program a new file called unused_components.txt\n\t\tis created in root_directory\n\t\"\"\"\n\n\tcurrent_directory = '';\n\troot_directory = '';\n\n\tif len(sys.argv) < 2:\n\t\tprint('Use: gc.py root_directory [search_directory]');\n\t\tquit();\n\n\troot_directory = str(sys.argv[1]);\n\n\tif len(sys.argv) < 3:\n\t\tsearch_directory = os.curdir;\n\telse:\n\t\tsearch_directory = str(sys.argv[2]);\n\n\tgc = GarbageCorrection()\n\tgc.detect_unused_elements(root_directory, search_directory);","def gos_files_creation(annotation_file, go_namespace_studied):\n go_ontology = pronto.Ontology('http://purl.obolibrary.org/obo/go/go-basic.obo')\n\n # For each GO terms look to the namespaces associated with them.\n go_namespaces = {}\n for go_term in go_ontology:\n go_namespaces[go_term.id] = go_term.other['namespace'][0]\n\n # For each GO terms look if there is an alternative ID fo them.\n go_alt_ids = {}\n for go_term in go_ontology:\n if 'alt_id' in go_term.other:\n for go_alt in go_term.other['alt_id']:\n go_alt_ids[go_alt] = go_term.id\n\n # Genome file with genes associated with GO terms.\n df = pa.read_csv(annotation_file, sep='\\t', header=None)\n df.columns = ['Gene_Name', 'GOs']\n df.replace(np.nan, '', inplace=True)\n\n gos_in_df = []\n for gos in df['GOs']:\n for go in gos.split(','):\n if go not in gos_in_df:\n gos_in_df.append(go)\n\n df.set_index('Gene_Name', inplace=True)\n\n gene_gos = []\n for gene, row in df.iterrows():\n for go in row['GOs'].split(','):\n gene_gos.append((go, gene))\n\n dic_go_genes = {}\n for go in tqdm(gos_in_df):\n genes = []\n for gene_go in gene_gos:\n if go != '' and go not in go_namespaces:\n go = go_alt_ids[go]\n if gene_go[0] == go and go != '' and go_namespaces[go] == go_namespace_studied:\n genes.append(gene_go[1])\n if go != '':\n dic_go_genes[go] = genes\n\n print(len(dic_go_genes))\n\n delete_keys = []\n for go in dic_go_genes:\n if len(dic_go_genes[go]) < 4:\n delete_keys.append(go)\n\n for key in delete_keys:\n del dic_go_genes[key]\n print(len(dic_go_genes))\n\n df_go = pa.DataFrame.from_dict(dic_go_genes, orient='index')\n df_go.insert(0, 'Description', 'GO_terms')\n\n df_go.to_csv('go_gene.gmt', sep='\\t', header=False)\n\n df.reset_index(inplace=True)\n df_query_go = pa.concat([pa.Series(row['Gene_Name'], row['GOs'].split(','))\n for _, row in df.iterrows()]).reset_index()\n df_query_go.columns = ['GOs', 'Gene_Name']\n df_query_go = df_query_go[['Gene_Name', 'GOs']]\n df_query_go.to_csv('query_go.tsv', sep='\\t', index=False)","def main():\n # checking the directory\n cwd = os.getcwd()\n print(f'The working directory: {cwd}')\n # counting time \n start_time = time.process_time()\n # passing args\n arg = parse_arguments()\n sub_dir = arg.sub_dir\n dir_out = arg.dir_out\n file_amb = 'csv_to_clean'\n names_ambigous = defaultdict(str)\n with open(file_amb, 'r') as fh:\n for line in fh:\n name = line.strip().split('/')[2]\n names_ambigous[name] = names_ambigous.get(name, '')\n names_ambigous[name] += line.strip()\n print(f'number files: {len(names_ambigous)}')\n # checking if the output directory exist\n # if not make it\n f_pwd = os.path.join('Results', 'kmer_counts')\n # get the genus names\n cnt = 0\n for name, filename in names_ambigous.items():\n cleaned = get_csv_clean(filename)\n full_path = os.path.join(f_pwd, name)\n if os.path.exists(full_path):\n print(f'The path {full_path} exist')\n pass\n else:\n os.makedirs(full_path)\n csv_name = f'{full_path}/{name}_k2_8_chr.csv'\n print(f'Checking the full path {csv_name}')\n with open(csv_name, 'w') as fout:\n for km, cn in cleaned.items():\n fout.write(f'{km},{cn}\\n')\n cnt += 1\n # get final time of the script\n end = time.process_time()\n total_time = end - start_time\n print(f'The script takes {total_time} to finish!')\n print(f'Where read and manipulated {cnt} files')\n print('Done!')","def searchforcog(sfc_cogs, sfc_twogs, sfc_prots):\n cogupdate(sfc_cogs, sfc_twogs, sfc_prots)\n newcogfind(sfc_cogs, sfc_twogs, sfc_prots)\n newtwogfile(sfc_twogs)","def main(input_folder, output_images_folder, output_files_folder, bb_file,\n archive_folder, name_mapping):\n\n output_images_folder = Path(output_images_folder)\n output_files_folder = Path(output_files_folder)\n archive_folder = Path(archive_folder)\n output_images_folder.mkdir(exist_ok=True)\n archive_folder.mkdir(exist_ok=True)\n logger.info(\"Converting Dicom to Nifty - START\")\n converter = NiftiConverter(\n padding=\"whole_image\",\n resampling_spacing=-1,\n list_labels=[\"GTVt\"],\n cores=10,\n )\n _ = converter(input_folder, output_folder=output_images_folder)\n\n logger.info(\"Converting Dicom to Nifty - END\")\n logger.info(\"Removing extra VOI - START\")\n move_extra_vois(output_images_folder, archive_folder)\n logger.info(\"Removing extra VOI - END\")\n logger.info(\"Renaming files- START\")\n correct_names(output_images_folder, name_mapping)\n logger.info(\"Renaming files- END\")\n logger.info(\"Cleaning the VOIs - START\")\n clean_vois(output_images_folder)\n logger.info(\"Cleaning the VOIs - END\")\n\n logger.info(\"Computing the bounding boxes - START\")\n bb_df = compute_bbs(output_images_folder)\n bb_df.to_csv(bb_file)\n logger.info(\"Computing the bounding boxes - END\")","def convert(self):\n \n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\n splitAt = len(self.fullPath) + 1\n \n if len(vrtlist)!=0:\n for i in range(0,len(vrtlist)):\n prefix = str(vrtlist[i].split(\".vrt\")[0])\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \n prefix = prefix, subset = self.subset, res = self.resolution, \n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\n ct.run()\n mosdel = glob.glob(self.fullPath + '/*mos.tif')\n for f in mosdel:\n os.remove(f)\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \n for f in xmldel:\n os.remove(f)\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\n for f in vrtdel:\n os.remove(f)\n tifCount = len(glob.glob(self.fullPath + '/*.tif'))\n dataCount = self.subset.count('1')\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d mosaicked images were successfully converted to %d %s files.' % (dataCount, len(vrtlist), tifCount, str(self.outformat)))\n \n \n if len(vrtlist)==0: \n \n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\n for i in range(len(hdflist)):\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i]], subset = self.subset, outformat = 'GTiff')\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\n\n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\n splitAt = len(self.fullPath) + 1\n \n for i in range(0,len(vrtlist)):\n prefix = str(vrtlist[i].split(\".vrt\")[0])\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \n prefix = prefix, subset = self.subset, res = self.resolution, \n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\n ct.run()\n \n mosdel = glob.glob(self.fullPath + '/*mos.tif')\n for f in mosdel:\n os.remove(f)\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \n for f in xmldel:\n os.remove(f)\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\n for f in vrtdel:\n os.remove(f)\n tifCount = len(glob.glob(self.fullPath + '/full*.tif'))\n dataCount = self.subset.count('1')\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d HDF files were successfully converted to %d %s files.' % (dataCount, len(hdflist), tifCount, str(self.outformat)))","def getAllVCFs(inputFolder):\n listOfFiles = []\n numErrors = 0\n for folder in os.listdir(inputFolder): #Loop through all folders\n # print(folder)\n try: # using a try-xcept block to avoid errors with other files \n # not following this structure\n # TODO: make this cleaner\n vcfLoc = os.path.join(inputFolder, os.path.join(folder, \"pilon/\"))\n # print(vcfLoc)\n for potentialFile in os.listdir(vcfLoc):\n # print(potentialFile)\n if(potentialFile.endswith(\".vcf.gz\")):\n listOfFiles.append(os.path.join(vcfLoc, potentialFile))\n except:\n # print(\"error at \" + folder)\n numErrors += 1\n print(numErrors)\n return listOfFiles","def main():\n arg = parse_args()\n print('Start.')\n arg.folder = Path(arg.folder)\n trees = list(arg.folder.glob('*'))\n trees = [i.absolute() for i in trees]\n info = parse_info(arg)\n types = [arg.folder/i for i in info.keys()]\n types_dict = dict(zip(info.keys(), types))\n for i in types:\n i.mkdir()\n result = divide_trees(trees, info, types)\n result_csv = arg.folder / 'result.csv'\n with open(result_csv, 'w') as out:\n out.write('Tree,Type,Confidence\\n')\n for i in result:\n out.write('{},{},{}\\n'.format(*i))\n Path(types_dict[i[1]]/i[0].name).write_text(i[0].read_text())\n print('Done.')","def genImgVallCCD(filename=None,Nstar=None,seeing=[0.9,0.,0.],npix=None,zenith=0,filter='g', theta=0.,phi=0, corrector='corrector',x=0.,y=0.,z=0.,suband=None,regular=False, moffat=False):\n hduList = pf.HDUList()\n hdu = pf.PrimaryHDU(np.array([0]))\n hdu.header.set('RAYPATT',raypattern)\n hdu.header.set('NPIX',npix)\n hdu.header.set('SCALE',scale)\n hdu.header.set('FWHM',diffusionfwhm)\n hdu.header.set('ZENITH',zenith)\n hdu.header.set('FILTER',filter)\n hdu.header.set('THETA',theta)\n hdu.header.set('CORRT',corrector)\n hdu.header.set('PHI',phi)\n if x != None:\n hdu.header.set('X',x)\n if y != None:\n hdu.header.set('Y',y)\n if z != None:\n hdu.header.set('Z',z)\n if seeing != 0.:\n hdu.header.set('s_fwhm',seeing[0])\n hdu.header.set('e1',seeing[1])\n hdu.header.set('e2',seeing[2])\n hduList.append(hdu)\n for ccd in N[1:]+S[1:]:\n print ccd\n res = genImgV(filename=filename,Nstar=Nstar,ccd=ccd,seeing=seeing,npix=npix,zenith=zenith,filter=filter, theta=theta, phi=phi,corrector=corrector,x=x,y=y,z=z,suband=suband,regular=regular,moffat=moffat)\n hdu = pf.PrimaryHDU(res[0])\n hdu.header.set('ccdPos',ccd[0])\n hdu.header.set('ccdXcen',ccd[1])\n hdu.header.set('ccdYcen',ccd[2])\n hduList.append(hdu)\n if filename != None:\n if os.path.exists(filename):\n os.system('rm '+filename)\n hduList.writeto(filename)\n else:\n hduList.writeto(filename)\n os.system('gzip '+filename)\n return hduList","def datamerge_run(filenames, outdir, roc_cols):\n \n tbldict = collect2dict(filenames, outdir)\n tbldict = cogtest_manipulation(tbldict, roc_cols)\n \n #count number of tps\n tbldict['cogtests'] = count_instances(tbldict['cogtests'], 'codeb', 'NP_NoTps')\n tbldict['aseg_change'] = count_instances(tbldict['aseg_change'], 'codea', 'MRI_NoTps')\n tbldict['pibparams'] = count_instances(tbldict['pibparams'], 'codea', 'PIB_NoTps')\n \n new_tbldict = {}\n for key, tbl in tbldict.iteritems():\n tpcol = [s for s in tbl.columns if ('_Tp' in s)]\n if tpcol:\n tpcol = tpcol[0]\n tblflat, tblflatnm = flatten(tbl, tpcol, key, [1, '1'])\n new_tbldict[tblflatnm] = tblflat\n tbldict.update(new_tbldict)\n \n #make sure each table contains SubjID and BAC# fields\n for key, tbl in tbldict.iteritems():\n tbl = addcodes(tbl, tbldict['codetranslator'])\n tbldict[key] = tbl\n \n #merge tables\n tblstojoin = ['cogtests_flat','pibparams_flat','aseg_change_flat','fdg_metaroi_flat','subjinfo']\n joincol = ['codea','codeb']\n subjtbl = mergelots(tbldict, tblstojoin, joincol)\n \n #merge tables\n tblstojoin = ['cogtests','subjinfo','pibparams_flat','aseg_change_flat','fdg_metaroi_flat']\n joincol = ['codea','codeb']\n NPtbl = mergelots(tbldict, tblstojoin, joincol)\n \n cf.save_xls_and_pkl(subjtbl, 'subjtbl', outdir)\n cf.save_xls_and_pkl(NPtbl, 'NPtbl', outdir)\n \n return tbldict, NPtbl, subjtbl","def gtiff(location):\n\n for src_asc in os.listdir(location):\n if \".asc\" in src_asc:\n dest_gtif = src_asc[:-4] + '.gtiff'\n command = \"gdal_translate -of GTiff -ot Float32 \" + location +'\\\\' \\\n + src_asc + ' ' + location +'\\\\' + dest_gtif\n os.system(command)","def main():\n print('Calculating gas compositions.')\n resf = 'results/cell_gas' # results folder\n if not os.path.isdir(resf):\n os.makedirs(resf)\n sizex = 0.03 # sample size\n sizey = 0.02 # sample size\n sizez = 0.02 # sample size\n volume = sizex**3 # sample volume\n volume = sizex * sizey * sizez # sample volume\n # polymer density\n rhop = INPUTS['polymer_density']\n # molecular weight\n mw_ba = INPUTS['molar_mass']\n # foam density\n rhof = INPUTS['foam_density']\n # cell size for Kelvin effect on saturated vapour pressure\n dcell = INPUTS['cell_size']\n # initial weight fraction of BA\n w_ba_ini = INPUTS['initial_weight_fraction']\n names = w_ba_ini.keys()\n if 'H2O' in w_ba_ini:\n if 'CO2' in w_ba_ini:\n print(\"WARNING: H2O and CO2 are both in initial_weight_fraction.\",\n \"We will sum these contributions.\")\n else:\n w_ba_ini['CO2'] = 0\n w_ba_ini['CO2'] += w_ba_ini['H2O'] * mw_ba['CO2'] / mw_ba['H2O']\n names.append('CO2')\n names.remove('H2O')\n temps = linspace(\n INPUTS['temperature']['min'],\n INPUTS['temperature']['max'],\n INPUTS['temperature']['points']\n )\n por = 1 - rhof / rhop # porosity\n m_foam = rhof * volume # foam sample weight\n m_pol = m_foam * (1 - sum(w_ba_ini.values())) # weight of polymer\n if ARGS['--verbose']:\n print('Foam weight {0:.3f} g'.format(m_foam * 1e3))\n args = [por, mw_ba, m_foam, m_pol, volume, dcell]\n for name in names:\n with open(os.path.join(resf, 'cell_gas_{0}.csv'.format(name)),\n 'w') as csvfile:\n fieldnames = ['temp', 'pres_ba', 'w_ba_g', 'w_ba_d', 'w_ba_c']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n for temp in temps:\n pres_ba, w_ba_g, w_ba_d, w_ba_c = initial_pressure(\n name, w_ba_ini[name], temp, args)\n writer.writerow(\n {'temp': temp, 'pres_ba': pres_ba, 'w_ba_g': w_ba_g,\n 'w_ba_d': w_ba_d, 'w_ba_c': w_ba_c})\n print('End.')","def main(inCL=None):\n headList = [] # Stores header of coronavirus sequences in fasta file\n orfList = [] # Stores sequences containing ORFs of coronavirus sequences in fasta file\n validNucs = ['A', 'C', 'G', 'T']\n myReader = FastAreader('Combined-ALL-SARS-CoV.fasta') \n for head, seq in myReader.readFasta(): # Using fastAreader to read in .fasta files\n headList.append(head)\n for i in seq:\n if i not in validNucs: # Removing non-valid bases\n seq = seq.replace(i,\"\") \n orf = OrfFinder(seq, 300, True) # Includes the largest ORF greater than 300 nucleotides within a stop codon\n geneOrfList = orf.getOrfs()\n geneSeq = [] # Stores ORF sequences\n for openFrame in geneOrfList:\n geneSeq.append(seq[openFrame[1]-1:openFrame[2]-1])\n orfList.append(geneSeq)\n # Calls methods to create SeqRecords and then .py file to print gene trees\n myPhylo = GeneTree() \n for i in range(0,4,1): # Loops to print the first four gene trees of every sequence\n records = myPhylo.geneSpecificRecord(orfList, headList, i) # Creates list of SeqRecords that represent a sequence\n # alignments = myPhylo.fastaToPhylip(records) # Makes a .phy file using a .fasta file\n print(\"GENE \" + str(i+1) + \":\")\n # printTree = myPhylo.printGeneTree() # Prints Gene Trees\n x = 0\n print('\\n\\n============================================ K E Y ============================================\\n')\n for header in headList: # Loops through headers to print key\n header = header.split(',')\n header = header[0]\n print(\"{} = {}\" .format(x, header)) # Prints each line containing the header\n x += 1","def merge_wrapper(processdir, basedir, starglob, superstarglob, calibrootglob, njobs=2, invert=False):\n for glob in [starglob, superstarglob, calibrootglob]:\n assert path.dirname(glob), \\\n f\"Glob : {glob} should be/contain a subdirectory\"\n\n superstarGlobNew = get_glob_strings(superstarglob)\n calibrootGlob1, calibrootGlob2 = get_glob_strings(calibrootglob)\n superstardir = get_dir_from_glob(processdir, superstarglob)\n calibdir = get_dir_from_glob(basedir, calibrootglob)\n starglob = processdir + starglob\n\n # ssmcolfnames = converter(superstardir,\n # globstr1=superstarGlobNew,\n # globstr2=superstarGlobNew,\n # njobs=42,\n # mergecolsonly=True)\n # yecho(\"SuperStarfiles done.\")\n # tofiltercalibglob = converter(processdir,\n # globstr1=calibrootGlob1,\n # globstr2=calibrootGlob2,\n # njobs=42,\n # mergecolsonly=False)\n # yecho(\"Extracting done.\")\n tofiltercalibglob = \"./csv/*.csv\"\n ssmcolfnames = glob_and_check(\"./superstar/mergecols/*.csv\")\n\n yecho(\"Removing events.\")\n if njobs > 1:\n splitcalib = split_by_dates(tofiltercalibglob)\n splitstar = split_by_dates(starglob)\n splitss = split_by_dates(ssmcolfnames)\n # needs filename output\n assert len(splitcalib) == len(splitstar) == len(splitss), \"only works the first time when no calibfiles got moved, for everything else this needs a new function with more logic\"\n Parallel(n_jobs=njobs)\\\n (delayed(single_remove_events)(calibglob, starglob, ssglob, njobs, invert)\n for calibglob, starglob, ssglob in zip(splitcalib, splitstar, splitss))\n # filteredFiles = [f for arr in filteredFiles for f in arr]\n else:\n check_telescope_files(rootdir=None, globstr1=ssmcolfnames,\n globstr2=calibmcolfnames, replacer=(\"_Y_\", \"_I_\"))\n remover = EventRemover(tofiltercalibglob=tofiltercalibglob,\n starglob=starglob,\n superstarmcolglob=ssmcolfnames)\n remover.remove_events()\n filteredFiles = remover.outfilenames\n yecho(\"Removed events that get thrown out during image cleaning and superstar processing and wrote the merged runs to:\")\n yecho(f\"{path.basename(filteredFiles[0])}\")\n # return filteredFiles","def main():\n glob_pattern = \"{root}/{child}/*.xml\".format(root=MANCHESTER_ROOT, child=TARGET_CHILD)\n corpus_files = glob(glob_pattern)\n for filename in corpus_files:\n print(filename)\n to_csv(filtered_parent_freq_count([filename], 2))","def _main_extract_CDS(args = None, stdout = None, stderr = None,\n gb_record_fmtdict = None,\n gb_cds_fmtdict = None) :\n if stdout is None :\n stdout = sys.stdout\n if stderr is None :\n stderr = sys.stderr\n if gb_record_fmtdict is None :\n gb_record_fmtdict = _GB_RECORD_FMTDICT\n if gb_cds_fmtdict is None :\n gb_cds_fmtdict = _GB_CDS_FMTDICT\n # Process arguments\n if args is None :\n parser = _makeParser_extract_CDS()\n args = parser.parse_args()\n args = _processArgsToLogic_extract_CDS(args, stdout, stderr,\n gb_record_fmtdict, gb_cds_fmtdict)\n # Go through the input files\n uniqueSeq = dict()\n i_file = 0\n for fi in args.genbank_records :\n i_file += 1\n if args.verbose :\n stderr.write(time.asctime() + \" - \" +\n \"Processing file \" + str(i_file) + \" : \" +\n os.path.basename(fi) + \" - \" +\n \"N unique seq : \" + str(len(uniqueSeq.keys())) + \"\\n\")\n record = SeqIO.parse(fi, \"genbank\")\n for r in record :\n if not args.actionFlags.get(\"DoCount\", False) :\n (summaryString, uniqueSeq, newSeq) = (\n _summarizeRecord(r, args.outfmt, args.hash, uniqueSeq))\n stdout.write(summaryString)\n else :\n count = len([x for x in r.features if x.type == \"CDS\"])\n stdout.write(r.annotations[\"gi\"] + \"\\t\" + str(count) + \"\\n\")\n # Write unique sequences\n if args.actionFlags.get(\"DoUniqueSequences\", False) :\n with open(args.unique, \"w\") as fo :\n for (k, v) in uniqueSeq.items() :\n fo.write(\">\" + k + \"\\n\")\n fo.write(v + \"\\n\")","def b_cosmic(folder_name, PATH, CCD):\n\n list_files = search_files(location=folder_name, keyword='*.fits')\n # print list_files\n\n obj_list, obj_l_gr7, obj_l_gr8, pas_list = list_object(list_files, PATH)\n flat_list, f_l_gr7, flat_l_gr8, pas_list = list_flat(list_files, PATH)\n # cosmic_curr_list = list(set(obj_list).union(flat_list))\n # file which needed to correct for cosmic ray\n\n cosmic_curr_list = obj_list # file which needed to correct for cosmic ray\n cosmic_curr_list_flats = flat_list\n print(len(cosmic_curr_list))\n write_list(file_list=cosmic_curr_list, file_name='cosmic_curr_list',\n location=PATH)\n\n cr_check_list = cosmic_correction(cosmic_curr_list_flats, location=PATH)\n for file in cr_check_list:\n remove_file(str(file))\n\n # cosmic-ray correction manually for individual files\n # or all files automatically\n message = \"How do you like to proceed Cosmic ray correction?\"\n choices = ['Default', 'Manually']\n input = options(message, choices)\n\n if input.lower() == 'manually':\n cr_check_list = cosmic_correction_individual(cosmic_curr_list,\n CCD=CCD, location=PATH)\n else:\n cr_check_list = cosmic_correction_batch(cosmic_curr_list, CCD=CCD,\n location=PATH)\n print(len(cr_check_list))\n # Stop running code for checking the cosmic ray corrected files\n message = \"\"\"Cosmic ray correction is done.\nDo you want to check chk files then continue?\"\"\"\n choices = ['Yes', 'No']\n value = options(message, choices)\n\n if value == \"Yes\":\n display_co(image_list=cosmic_curr_list, location=PATH)\n # for file in cr_check_list:\n # remove_file(str(file))\n elif value == \"No\":\n pass\n # for file in cr_check_list:\n # remove_file(str(file))","def get_result_files(self):\n name_pattern = \"{mapper}.{ngs_library.name}\"\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"out\", name_pattern + \"{ext}\"), ext=EXT_VALUES\n )\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"log\", \"{mapper}.{ngs_library.name}.{ext}\"),\n ext=(\n \"log\",\n \"conda_info.txt\",\n \"conda_list.txt\",\n \"log.md5\",\n \"conda_info.txt.md5\",\n \"conda_list.txt.md5\",\n ),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.{report}.txt\"\n ),\n report=(\"bamstats\", \"flagstats\", \"idxstats\"),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.{report}.txt.md5\"\n ),\n report=(\"bamstats\", \"flagstats\", \"idxstats\"),\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.bamstats.html\"\n )\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"bam_qc\", name_pattern + \".bam.bamstats.html.md5\"\n )\n )\n\n for sheet in self.shortcut_sheets:\n for ngs_library in sheet.all_ngs_libraries:\n if ngs_library.name in self.ngs_library_to_kit:\n extraction_type = ngs_library.test_sample.extra_infos[\"extractionType\"]\n suffix = (\n \"_long\"\n if ngs_library.extra_infos[\"seqPlatform\"] in (\"PacBio\", \"ONP\")\n else \"\"\n )\n # Per-sample target coverage report.\n yield from expand(\n os.path.join(\n \"output\", name_pattern, \"report\", \"cov_qc\", name_pattern + \".{ext}\"\n ),\n mapper=self.config[\"tools\"][extraction_type.lower() + suffix],\n ngs_library=[ngs_library],\n ext=[\"txt\", \"txt.md5\"],\n )\n yield \"output/target_cov_report/out/target_cov_report.txt\"\n yield \"output/target_cov_report/out/target_cov_report.txt.md5\"\n if (\n self.config[\"picard_hs_metrics\"][\"path_targets_interval_list\"]\n and self.config[\"picard_hs_metrics\"][\"path_baits_interval_list\"]\n ):\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"picard_hs_metrics\", name_pattern + \".txt\"\n )\n )\n yield from self._yield_result_files(\n os.path.join(\n \"output\", name_pattern, \"report\", \"picard_hs_metrics\", name_pattern + \".txt.md5\"\n )\n )\n if self.config[\"compute_coverage_bed\"]:\n yield from self._yield_result_files(\n os.path.join(\"output\", name_pattern, \"report\", \"coverage\", name_pattern + \"{ext}\"),\n ext=(\".bed.gz\", \".bed.gz.tbi\"),\n )\n else:\n print(\n \"Genome-wide coverage BED generation disabled\", file=sys.stderr\n ) # pragma: no cover","def dir_resolution(self, src_path, frag_length=128):\n src_path = os.path.join(self.root_path, src_path)\n files = os.listdir(src_path)\n\n MFCCs = None\n labels = None\n cnt = 1\n total_num = len(files)\n for wav in files:\n wav_path = os.path.join(src_path, wav)\n MFCCs_each, labels_each = self.features_and_labels(wav_path, frag_length)\n if MFCCs is not None:\n MFCCs = torch.cat((MFCCs, MFCCs_each))\n labels = torch.cat((labels, labels_each))\n else:\n MFCCs, labels = MFCCs_each, labels_each\n\n if cnt % 1000 == 0:\n print('{} data pieces have been loaded in and {} are left'.format(cnt, total_num-cnt))\n cnt += 1\n\n np.save(self.feature_file, MFCCs.numpy()) \n np.save(self.label_file, labels.numpy())\n print('Loading into files finished!')","def create_merged_genotype_file(snps_file_path):\n print(\"creating merged genotype file\")\n plink_runner = Plink2DockerRunner()\n shutil.copyfile(snps_file_path, f\"{GENOTYPE_DATA_PATH}/{SNP_LIST_FILE_NAME}\")\n plink_runner(f\"./plink2 --pfile {IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_GENOTYPE_FILE} vzs \"\n f\"--extract {IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{SNP_LIST_FILE_NAME} --export vcf \"\n f\"--out {IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_GENOTYPE_FILE}\")","def split_per(folderin, folderout, split_col='ECO_ID', colNms=['i_h100','i_cd',\n 'doy','i_wflen','i_acqdate','b1','vcf','ECO_NAME','ECO_ID','BIOME','geometry']):\n\n split_files = glob.glob(folderin + '*.shp')\n\n for filename in split_files:\n print(filename)\n basename = os.path.splitext(os.path.basename(filename))[0]\n dfa = gpd.read_file(filename)\n df = dfa.astype({split_col: 'int32'}) \n ecoNames = list(np.unique(df[split_col]))#get list of unique ecoregions \n \n for eco in ecoNames:\n #create new df with just columns I want\n df2 = gpd.GeoDataFrame(df, columns=colNms)\n ID = str(eco)\n df_eco = df.loc[df2[split_col]==eco, colNms]\n df_eco.to_file(folderout + '/{}_eco_{}.shp'.format(basename, ID))","def __concatonate_files_controller(self):\n\n # find all barcode file paths\n barcode_directories = []\n for root, directory, files in os.walk(self.input_directory):\n for name in directory:\n barcode_directories.append( os.path.join(root, name) )\n\n # iterate through each barcode directory, item is the file path\n for item in barcode_directories:\n file = os.listdir(item)[0]\n path = item\n\n new_file_name = self.__return_new_file_name(file_name=file, file_path=path)\n self.__concatonate_files(new_file_name=new_file_name, parent_folder=path)\n self.__write_logs_to_file(new_file_name)","def rawvc_gatk_genotype_gvcfs_input(wildcards):\n ref = cfg['db']['ref']\n db = \"{results}/{group}/rawvc/gatkhc/genomicsdb/{population}{dot}{region}.{target}.db\".format(**dict(wildcards))\n faext = wildcards_or(ext[\"fa\"])\n d = re.sub(faext, \".dict\", ref)\n targets = os.path.join(\n os.path.dirname(ref), \"gatkhc\", f\"{wildcards.region}.{wildcards.target}.bed\")\n return {'ref': ref, 'db': db, 'dict': d, 'targets': targets}","def TextureFiles():\n import shutil\n\n # first convert the .psd files to .png\n\n FbmDir = glo.outputFolder + '.fbm'\n\n for d1, d2, filenames in os.walk(FbmDir):\n for filename in filenames:\n \"\"\"filename: vitrin_diffuse.psd\n \"\"\"\n # print \"TextureFiles():\", filename\n if filename[-4:].upper() == '.PSD':\n #print \" -- FbmDir:\" , FbmDir\n #print \" -- in the if clause with filename:\" , filename\n #print \" -- glo.outputFolder\" , glo.outputFolder\n # FbmDir = '../fbx/simplelifeembedmedia.fbm'\n # filename = 'shelves_light.PSD'\n PsdToPngConverter(FbmDir, filename)\n\n # Move only the .png file to the ../png/ directory\n filename = filename[:-4] + '.png'\n src = os.path.join(FbmDir, filename)\n elif filename[0] != '.':\n src = os.path.join(FbmDir, filename)\n pass\n\n shutil.copy(src, glo.outputFolder)\n print os.path.join(glo.outputFolder, filename), \"\\n\"\n sys.stdout.flush()\n # for d1, d2, files in os.walk(glo.outputFolder):\n # if not filename in files:\n # #print \"moving: \", files, filename, not filename in files\n # shutil.copy(src, glo.outputFolder)\n # print os.path.join(glo.outputFolder, filename), \"\\n\"\n # else:\n # print \"%s/%s already exists. File not moved\" % (glo.outputFolder,filename)","def main(data_dir):\n\n face2face_dir = '{}/manipulated_sequences/Face2Face/c0/videos'.format(data_dir)\n orig_dir = '{}/original_sequences/c0/videos'.format(data_dir)\n base_dir = '{}/manipulated_sequences/GANnotation'.format(data_dir)\n output_enc_dir = '{}/encodings'.format(base_dir)\n output_vid_dir = '{}/{}/videos'.format(base_dir, COMPRESSION_LEVEL)\n\n pairs = get_seq_combos(face2face_dir)\n\n # Compute all video encodings and save them to disk.\n # We precompute these because they take roughly 10 times as long to compute\n # as the reenactments, and we may want to recompute the reenactments with\n # different images later.\n print('Computing video encodings...')\n if not os.path.exists(output_enc_dir):\n os.makedirs(output_enc_dir)\n enc_count = 0\n for source_id, _ in pairs:\n encoding_path = get_encoding_path(output_enc_dir, source_id)\n if os.path.exists(encoding_path):\n continue # Encoding already calculated for this video sequence.\n print('Computing encoding for sequence {}...'.format(source_id))\n video_path = '{}/{}.mp4'.format(orig_dir, source_id)\n cap = cv2.VideoCapture(video_path)\n points = compute_video_encoding(cap)\n cap.release()\n try:\n np.savetxt(encoding_path, points.reshape((132,-1)).transpose())\n except KeyboardInterrupt as e:\n # Safely handle premature termination.\n # Remove unfinished file.\n if os.exists(encoding_path):\n os.remove(encoding_path)\n raise e\n enc_count += 1\n\n if enc_count == 0:\n print('No encodings were calculated')\n else:\n print('{} video sequences encoded'.format(enc_count))\n\n print()\n print('Computing reenactments...')\n\n # Load pre-trained model.\n gann_path = os.path.join(dirname, 'models/myGEN.pth')\n my_gann = GANnotation.GANnotation(path_to_model=gann_path)\n\n image_dir = '{}/original_sequences_images/{}/images'.format(data_dir, COMPRESSION_LEVEL)\n if not os.path.exists(output_vid_dir):\n os.makedirs(output_vid_dir)\n reenact_count = 0\n for source_id, driver_id in pairs:\n output_path = '{}/{}_{}.mp4'.format(output_vid_dir, source_id, driver_id)\n if os.path.exists(output_path):\n # Do not recreate a video if it already exists.\n # If the user wants to recreated a video\n # the existing video must be deleted first.\n continue\n\n print('Computing reenactment for {} onto {}...'.format(driver_id, source_id))\n # Validate that input files exist.\n encoding_path = get_encoding_path(output_enc_dir, driver_id)\n if not os.path.isfile(encoding_path):\n print('Failed to find encoding for video sequence {}'.format(driver_id),\n file=stderr)\n continue\n image_path = '{}/{}.png'.format(image_dir, source_id)\n if not os.path.isfile(image_path):\n print('Failed to find image for sequence {}'.format(source_id),\n file=stderr)\n continue\n\n points = np.loadtxt(encoding_path).transpose().reshape(66, 2, -1)\n\n # Load and transform image for inputting.\n image = cv2.imread(image_path)\n cropped = get_gann_cropped_face(image)\n\n # Compute reenactment.\n frames, _ = my_gann.reenactment(cropped, points)\n\n output_path = os.path.abspath(output_path)\n print('Writing video to \"{}\"'.format(output_path))\n try:\n write_video(frames, FPS, (128, 128), output_path)\n except KeyboardInterrupt as e:\n # Safely handle premature termination.\n # Remove unfinished file.\n if os.exists(output_path):\n os.remove(output_path)\n raise e\n reenact_count += 1\n\n if reenact_count == 0:\n print('No reenactments were created')\n else:\n print('{} reenactments created'.format(reenact_count))","def gtf_processing(genome=None, prefix='gencov'):\n all_bed = prefix + \".all.bed\"\n\n if not os.path.exists(all_bed) or os.stat(all_bed).st_size == 0:\n log.info(\"Preprocessing annotation...\")\n features = ('exon', 'gene', 'intron', 'intergenic')\n merged_exons, merged_genes = map(preprocess, features[:2])\n ins = {\n 'intron': [merged_genes, merged_exons],\n 'intergenic': [merged_genes, genome]\n }\n intron_bed, intergenic_bed = map(preprocess, features[2:], [ins, ins])\n\n log.info(\"Concatenate bed files for all elements...\")\n with open(all_bed, 'w') as out_bed:\n cat_all(merged_exons, merged_genes, intron_bed, intergenic_bed, out_bed=out_bed)\n\n for f in (merged_exons, merged_genes, intron_bed, intergenic_bed):\n os.remove(f)\n\n return all_bed","def FileGen(TargetDir, extension = \".gbk\",subset_tag = \"final\"):\n \n for F in next(os.walk(TargetDir))[2]:\n print(F)\n if F[-1*len(extension):] == extension:\n if F.split(\".\")[-2][:len(subset_tag)] == subset_tag:\n yield(TargetDir + '/' + F)","def generate_filename(self):\n file_pattern = os.path.join(self.path, \"TCGA-*\")\n for f in glob(file_pattern):\n organ = get_organ(f)\n for raw_f in glob(os.path.join(f, \"*.tif\")):\n gt_f = raw_f.replace(\".tif\", \".xml\")\n yield raw_f, gt_f, organ","def generate_gazetteer(data_dir):\n gaz_dir = os.path.join(data_dir, 'gazetteers/')\n sources = ['full']\n\n # Cognitive Atlas gazetteer\n weights = {'isSelf': 1,\n 'isKindOf': 1,\n 'inCategory': 1}\n vocab_df = pd.read_csv(join(ec.utils.get_resource_path(), 'ontology',\n 'unstemmed_cogat_vocabulary.csv'))\n weight_df = pd.read_csv(join(ec.utils.get_resource_path(), 'ontology',\n 'unstemmed_cogat_weights.csv'), index_col='id')\n for source in sources:\n text_folder = join(data_dir, 'text/cleaned_{0}/'.format(source))\n out_dir = join(data_dir, 'text/cogat_cleaned_{0}/'.format(source))\n count_df = ec.extract.extract_folder(text_folder, vocab_df, stem=False,\n subs_folder=out_dir, abbrev=True)\n count_df.to_csv(join(data_dir, 'features/cogat_counts_{0}.csv'.format(source)), index_label='pmid')\n\n weighted_df = ec.extract.expand(count_df, weight_df)\n weighted_df.to_csv(join(data_dir, 'features/cogat_{0}.csv'.format(source)), index_label='pmid')\n\n print('Completed cogat extraction.')","def main():\n args = setup_args()\n header_info = extract_header_info_from_probes(args.probe)\n\n for gene in header_info.keys():\n # check there is a folder for gene, else create it\n gene_out_dir = os.path.join(args.output_path, gene)\n if not os.path.exists(gene_out_dir):\n os.mkdir(gene_out_dir)\n\n gene_msa_fname = os.path.join(gene_out_dir, '{}_msa.fa'.format(gene))\n gene_ref = os.path.join(os.path.abspath(args.gene_refs), gene + '.fa')\n generate_msa_for_gene(gene, header_info[gene], gene_ref, gene_msa_fname)","def gatherfiles(self):\n\t\tfrom subprocess import Popen,PIPE\n\t\timport os\n\t\timport tarfile\n\t\timport glob\n\t\t\n\t\tprint \"=== \",self.nameID,\": Joining all the files in one\"\n\t\t# FIXME: Only there are 1 file, not needed the hadd\n\t\tfinalfile = os.path.join(\"Results\",self.outputfile)\n\t\t# FIXED BUG: just cp when there is only one file, otherwise\n\t\t# there are problems with the TTree\n\t\tif len(self.outputfiles) == 1:\n\t\t\t# Note that when there is only 1 file, always its #task=1\n\t\t\tcommand = [ 'cp', self.outputfiles[1], finalfile ]\n\t\telse:\n\t\t\tcommand = [ 'haddPlus', finalfile ]\n\t\t\tfor f in self.outputfiles.itervalues():\n\t\t\t\tcommand.append( f )\n\t\tp = Popen( command ,stdout=PIPE,stderr=PIPE ).communicate()\n\t\t# Checking if everything was allright\n\t\ttotalevts = self.getevents(finalfile,True)\n\t\tif totalevts != self.nevents:\n\t\t\tmessage = \"\\033[33;1mclustermanager.gatherfiles: WARNING\\033[0m the total file\"\n\t\t\tmessage += \"'\"+finalfile+\"' do not contain all the events:\\n\"\n\t\t\tmessage += \"Total events to be processed:\"+str(self.nevents)+\"\\n\"\n\t\t\tmessage += \"Total events in '\"+finalfile+\"':\"+str(totalevts)+\"\\n\"\n\t\t\tprint message\n\t\t\treturn \n\t\t# If everything was fine, deleting the files \n\t\t# and cleaning the directory\n\t\tfor f in self.outputfiles.itervalues():\n\t\t\tos.remove( f )\n\t\t# Taring and compressing\n\t\tfilestotar = glob.glob(\"./*.*\")\n\t\tfilestotar.append( \".storedmanager\")\n\t\ttar = tarfile.open(os.path.basename(self.cwd)+\".tar.gz\",\"w:gz\")\n\t\tfor f in filestotar:\n\t\t\ttar.add(f)\n\t\ttar.close()\n\t\t# if everything was fine, deleting the files\n\t\tif os.path.exists(os.path.basename(self.cwd)+\".tar.gz\"):\n\t\t\tfor f in filestotar:\n\t\t\t\tos.remove(f)\n\t\telse:\n\t\t\tmessage = \"\\033[33;1mclustermanager.gatherfiles: WARNING\\033[0m I can't manage\\n\"\n\t\t\tmessage += \"to create the backup .tar.gz file\\n\"\n\t\t\tprint message\n\n\t\tprint \"Created \"+finalfile\n\t\tprint \"========= Process Completed =========\"","def run_pipeline(directory):\n\n # io = IO(path)\n # df = io.load_cleaned_file(download_always=False)\n # df = add_choke_events(df)\n\n # Add calls to features.Xxx here\n\n #directory = main_directory\n site=os.listdir(directory)\n site_dicom={}\n site_dicom_sub={}\n site_sub_files={}\n i,k,j=0,0,0\n for filename in site:\n site_dicom[i]=directory+'/'+filename+'/DICOM-raw'\n temporary_path=os.listdir(site_dicom[i])\n\n for another_file in temporary_path:\n site_dicom_sub[j]=site_dicom[i]+'/'+another_file+'/scans'\n temporary_path_1 = os.listdir(site_dicom_sub[j])\n for another_file_1 in temporary_path_1:\n site_sub_files[k]=site_dicom_sub[j]+'/'+another_file_1+'/'\n k=k+1\n j = j + 1\n i=i+1\n splitted={}\n output_mif={}\n for i in range (len(site_sub_files)):\n splitted[i]=site_sub_files[i].split('/')\n output_mif[i]=directory+'/'+splitted[i][5]+'/MIF-raw/'+splitted[i][5]+'_'+splitted[i][7]+'_'+splitted[i][9]+'.mif'\n\n\n # save (or return) dataframe here?\n return site_sub_files,output_mif","def calculate_mixture_features(args):\n workspace = args.workspace\n speech_dir = args.speech_dir\n noise_dir = args.noise_dir\n data_type = args.data_type\n fs = cfg.sample_rate\n dir_name = args.dir_name\n\n fid_clean = open(speech_dir, 'r')\n lines_clean = fid_clean.readlines()\n fid_clean.close()\n\n fid_reverb = open(noise_dir, 'r')\n lines_reverb = fid_reverb.readlines()\n fid_reverb.close()\n\n for files_clean, files_reverb in zip(lines_clean, lines_reverb):\n\n files_clean = files_clean.strip('\\n')\n files_reverb = files_reverb.strip('\\n')\n\n fid = open(files_clean,'r')\n wavLines_clean = fid.readlines()\n fid.close()\n fid = open(files_reverb,'r')\n wavLines_reverb = fid.readlines()\n fid.close()\n\n cnt = 0 \n\n for wavs_clean, wavs_reverb in zip(wavLines_clean, wavLines_reverb):\n \n t1 = time.time()\n # cnt = 0\n\n wav_name_clean, wav_path_clean = wavs_clean.split()\n wav_name_reverb, wav_path_reverb = wavs_reverb.split()\n \n # Read clean speech audio. \n (speech_audio, _) = read_audio(wav_path_clean, target_fs=fs)\n \n # Read reverb speech audio. \n (noise_audio, _) = read_audio(wav_path_reverb, target_fs=fs)\n \n # Cut reverb speech to the same length as clean speech. \n if len(noise_audio) > len(speech_audio):\n noise_audio = noise_audio[0: len(speech_audio)]\n \n # Extract spectrogram. \n mixed_complx_x = calc_sp(noise_audio, mode='complex')\n speech_x = calc_sp(speech_audio, mode='magnitude')\n\n # Write out features. \n out_feat_path = os.path.join(workspace, \"features\", \"spectrogram\", \n data_type, dir_name, \"%s.p\" % wav_name_reverb)\n create_folder(os.path.dirname(out_feat_path))\n data = [mixed_complx_x, speech_x, wav_name_reverb]\n pickle.dump(data, open(out_feat_path, 'wb'), protocol=pickle.HIGHEST_PROTOCOL)\n \n # Print. \n if cnt % 100 == 0:\n print(cnt)\n # print(mixed_complx_x)\n # print(speech_x)\n \n cnt += 1\n\n print(\"Extracting feature time: %s\" % (time.time() - t1))","def _iterate_over_files(self):\n stats = Statistics()\n\n args = arguments.Args()\n\n for file in args.files:\n\n if isimage(file):\n before_size = stats.calculate_before_optimization(file)\n\n puts(\"%s %s\" % (\n e(\"==>\"),\n os.path.basename(file))\n )\n\n if \"--lossy\" in args.flags:\n Optimize.lossy(file)\n if \"--lossless\" in args.flags:\n Optimize.lossless(file)\n after_size = stats.calculate_after_optimization(file)\n\n puts(\"%s %s (%s)\" % (\n p(\"<==\"),\n os.path.basename(file),\n s(after_size) if after_size < before_size else after_size\n ))\n\n stats.show_statistics()","def gc(args):\n p = OptionParser(gc.__doc__)\n p.add_option(\"--binsize\", default=500, type=\"int\", help=\"Bin size to use\")\n opts, args = p.parse_args(args)\n\n if len(args) != 1:\n sys.exit(not p.print_help())\n\n (fastafile,) = args\n binsize = opts.binsize\n allbins = []\n for name, seq in parse_fasta(fastafile):\n for i in range(len(seq) / binsize):\n atcnt = gccnt = 0\n for c in seq[i * binsize : (i + 1) * binsize].upper():\n if c in \"AT\":\n atcnt += 1\n elif c in \"GC\":\n gccnt += 1\n totalcnt = atcnt + gccnt\n if totalcnt == 0:\n continue\n gcpct = gccnt * 100 / totalcnt\n allbins.append(gcpct)\n\n from jcvi.graphics.base import asciiplot\n from collections import Counter\n\n title = \"Total number of bins={}\".format(len(allbins))\n c = Counter(allbins)\n x, y = zip(*sorted(c.items()))\n asciiplot(x, y, title=title)","def multiple_files_analysis(lower_wavelength, upper_wavelength, CCD_height, CCD_width, CCD_height_corr, CCD_width_corr, file_paths, file_path_corr_data, progress_update):\r\n \r\n all_files_data = []\r\n FWHM_data = []\r\n central_energy_data = []\r\n counter = 1\r\n\r\n for file_path in file_paths:\r\n analysis = QDot_Spectroscopy(file_path=r\"{}\".format(file_path), meas_corr_curve_file_path=r\"{}\".format(file_path_corr_data), lower_wavelength=lower_wavelength, upper_wavelength=upper_wavelength, CCD_height=CCD_height, CCD_width=CCD_width, CCD_height_corr=CCD_height_corr , CCD_width_corr=CCD_width_corr)\r\n\r\n twod_map_raw = analysis.matrix_map(bkg_reduction=False, data_correction=False)\r\n twod_map_no_bkg = analysis.matrix_map(bkg_reduction=True, data_correction=False)\r\n twod_map_raw_corr = analysis.matrix_map(bkg_reduction=False, data_correction=True)\r\n twod_map_no_bkg_corr = analysis.matrix_map(bkg_reduction=True, data_correction=True)\r\n Q_Dot_plot_data, fit_statistics = analysis.gaussian_fit()\r\n\r\n file_analysis = [twod_map_raw, twod_map_no_bkg, twod_map_raw_corr, twod_map_no_bkg_corr, Q_Dot_plot_data, fit_statistics]\r\n all_files_data.append(file_analysis)\r\n\r\n # Creates a histogram from the collected FWHM and central energy data from all the analyzed datafales containing quantumdots\r\n for FWHM_value in fit_statistics['FWHM'].to_numpy():\r\n FWHM_data.append(FWHM_value)\r\n for CE_value in fit_statistics['Centre'].to_numpy():\r\n central_energy_data.append(CE_value)\r\n\r\n progress_update.emit(counter * 100/len(file_paths))\r\n counter += 1\r\n \r\n return all_files_data, FWHM_data, central_energy_data","def rbp_cna(tumor_type, genelist):\n subdir_list = os.listdir(tumor_type+\"/\")\n if '.DS_Store' in subdir_list:\n subdir_list.remove('.DS_Store')\n for subfolder in subdir_list:\n f = os.path.join(tumor_type, subfolder, \"data_CNA.txt\")\n df = pd.read_table(f, sep=\"\\t\", index_col=0, low_memory=False)\n df_rbp = df[df.index.isin(genelist)]\n if df_rbp.columns[0] == \"Entrez_Gene_Id\":\n df_rbp.drop(columns=\"Entrez_Gene_Id\", axis=0, inplace=True)\n sample_num = df_rbp.shape[1]\n new_folder = tumor_type + \"_CNA\"\n if not os.path.exists(new_folder):\n os.mkdir(new_folder)\n outf = os.path.join(new_folder, subfolder + \"CNA.txt\")\n df_rbp.to_csv(outf, sep=\"\\t\")\n yield sample_num","def main(): \n \n # parse command line arguments\n parser = argparse.ArgumentParser(description='Runs variant calling on pileup file and stores in vfc file')\n parser.add_argument('--use-read-quality', default=False, action='store_true',\n help='tells the algorithm to estimate p from read qualities')\n parser.add_argument('--call-less-positions', default=False, action='store_true',\n help='tells the program to call less positions (not whole pileup file)')\n parser.add_argument('--input-file', default='merged-normal.pileup', type=str,\n help='path to input file in pileup format')\n parser.add_argument('--output-file', default='Make name from input name', type=str,\n help='name for the output vcf file. If not given, will be created from input file name')\n parser.add_argument('--p', default='0.99', type=float,\n help='probability estimate of one nucleotide read being correct, used by vc algorithm')\n parser.add_argument('--positions-to-call', default='10000', type=int,\n help='how many positions to call if call-less-positions set to true')\n args = parser.parse_args()\n if args.output_file == 'Make name from input name':\n args.output_file = args.input_file + '.vcf'\n \n variant_caller = VariantCaller()\n sample = 'SAMPLE1'\n \n # creates vcf file\n create_vcf_start = time.time()\n vcf = create_vcf_file(args.output_file, sample)\n create_vcf_end = time.time()\n print('Vcf header created. Elapsed time: {}'.format(create_vcf_end - create_vcf_start))\n\n main_loop_start = time.time()\n position_count = 0\n variant_caller_time = 0\n positions_with_variants = 0\n write_vcf_time = 0\n\n for pileup_line in pileup_reader(args.input_file):\n # calls variant for each pileup line\n variant_caller_start = time.time()\n variant_caller.call_variant(pileup_line, args.p, args.use_read_quality)\n if pileup_line['alts'] != '.':\n positions_with_variants += 1\n variant_caller_time += time.time() - variant_caller_start\n\n # writes line in VCF file\n write_vcf_start = time.time()\n write_vcf_line(pileup_line, vcf, sample)\n write_vcf_time = time.time() - write_vcf_start\n\n position_count += 1\n if args.call_less_positions and (position_count >= args.positions_to_call):\n break\n \n main_loop_end = time.time()\n total_running_time = main_loop_end - main_loop_start\n\n print('Processed {} positions. Found variants at {} positions.'.format(position_count, positions_with_variants))\n\n print('Total running time is {}'.format(total_running_time))\n print('Pileup reader: {}'.format(total_running_time - variant_caller_time - write_vcf_time))\n print('Variant calling: {}'.format(variant_caller_time))\n print('Vcf writing: {}'.format(write_vcf_time))","def collectInitialeccnStatistics_onefile(self, folder, databaseFilename, multiplicityFactor = 1.0, deformedNuclei = False):\n typeCollections = ((1, 'sn'), (2,'en'))\n for ecc_id, ecc_type_name in typeCollections:\n db = SqliteDB(path.join(folder, databaseFilename % ecc_type_name))\n # first write the ecc_id_lookup table, makes sure there is only one such table\n if db.createTableIfNotExists(\"ecc_id_lookup\", ((\"ecc_id\",\"integer\"), (\"ecc_type_name\",\"text\"))):\n db.insertIntoTable(\"ecc_id_lookup\", (ecc_id, ecc_type_name))\n\n # next create the eccentricities and collisionParameters table\n db.createTableIfNotExists(\"eccentricities\", ((\"event_id\",\"integer\"), (\"ecc_id\", \"integer\"), (\"n\",\"integer\"), (\"ecc_real\",\"real\"), (\"ecc_imag\",\"real\")))\n db.createTableIfNotExists(\"collisionParameters\", ((\"event_id\",\"integer\"), (\"Npart\", \"integer\"), (\"Ncoll\",\"integer\"), (\"b\",\"real\"), (\"total_entropy\",\"real\")))\n if(deformedNuclei):\n db.createTableIfNotExists(\"deformationParameters\", ((\"event_id\",\"integer\"), (\"cosTheta1\", \"real\"), (\"phi1\",\"real\"), (\"cosTheta2\",\"real\"), (\"phi2\",\"real\")))\n\n # the big loop\n data = loadtxt(path.join(folder, '%s_ecc_eccp_10.dat' %(ecc_type_name)))\n Npart = data[:, 36]\n Ncoll = data[:, 37]\n dSdy = data[:, 38]/multiplicityFactor #scale out the multiplicity factor used in superMC\n b = data[:, 39]\n for event_id in range(len(Npart)):\n db.insertIntoTable(\"collisionParameters\", (event_id, int(Npart[event_id]), int(Ncoll[event_id]), float(b[event_id]), float(dSdy[event_id])))\n if(deformedNuclei):\n cosTheta1 = data[:, 40]\n phi1 = data[:, 41]\n cosTheta2 = data[:, 42]\n phi2 = data[:, 43]\n for event_id in range(len(Npart)):\n db.insertIntoTable(\"deformationParameters\", (event_id, float(cosTheta1[event_id]), float(phi1[event_id]), float(cosTheta2[event_id]), float(phi2[event_id])))\n for iorder in range(1,10):\n eccReal = data[:, 4*iorder - 2]\n eccImag = data[:, 4*iorder - 1]\n for event_id in range(len(eccReal)):\n db.insertIntoTable(\"eccentricities\",(event_id, ecc_id, iorder, float(eccReal[event_id]), float(eccImag[event_id])))\n\n # close connection to commit changes\n db.closeConnection()","def main():\r\n\r\n #Create a list of all files that have the GPX file format\r\n fileList = glob.glob(os.path.join(inFolder,\"*.{0}\".format(inFormat)))\r\n\r\n #Create a connection to PostGIS database\r\n pgConn = createPostgisConnection(dbFormat, dbHost, dbName, dbSchema, dbUser, dbPWD)\r\n\r\n #Process each *listed* layer type from a GPS file\r\n for f in fileList:\r\n importGPX(f, gpxImportLayers, pgConn)","def setupGenotypeDirectories(self):\n self.allGenosDir = \"%s/%s_all-genotypes\" % (os.getcwd(),\n self.project2Id)\n if not os.path.exists(self.allGenosDir):\n os.mkdir(self.allGenosDir)\n for genoId,iGeno in self.dGenos.items():\n dirGeno = \"%s/%s\" % (self.allGenosDir, genoId)\n iGeno.dir = dirGeno\n if not os.path.exists(dirGeno):\n os.mkdir(dirGeno)\n if self.verbose > 0:\n msg = \"genotype directories: %s\" % self.allGenosDir\n print(msg); sys.stdout.flush()","def cli(species, output, regions, cores, kmer):\n kmer = int(kmer)\n\n count_composition = Counter()\n\n if regions is None:\n group = [n.replace('.txt', '') for n in os.listdir(bgdata.get_path('datasets', 'genomereference', species))\n if not n.startswith('.') and not n.startswith('chrM') and not n.startswith('chr23') and not n.startswith('chr24')]\n \n f = functools.partial(get_full_composition, species=species, kmer_len=kmer)\n else:\n group = read_file(regions)\n f = functools.partial(get_composition, species=species, kmer_len=kmer)\n\n with Pool(int(cores)) as pool:\n for d in tqdm(pool.imap_unordered(f, group), total=len(group)):\n count_composition = count_composition + d\n\n with gzip.open(output, 'w') as fout:\n fout.write(json.dumps(dict(count_composition)).encode('utf-8'))","def main():\n base_dir = '/home/sjimenez/imagenes_prueba'\n out_dir = '/home/sjimenez/easy_analysis'\n for _, _, files in os.walk(base_dir, topdown=False):\n for f in files:\n print('--------- {} ---------'.format(f))\n act_dir = osp.join(base_dir, f)\n act_im = cv2.imread(act_dir)\n if act_im is not None:\n get_image_stats(act_im, out_dir, f)\n else:\n print('Not able to open the image')","def get_gbk_files(self):\n\n db_name = []\n for file in os.listdir(os.getcwd()):\n if file.endswith('.db'):\n db_name.append(str(file))\n\n for G_key, G_value in self.what.tier_frame_dict.items():\n Tier = G_key\n os.chdir(self.path)\n os.mkdir(Tier)\n os.chdir(Tier)\n Tier_path = os.getcwd()\n for Gene in self.what.tier_frame_dict[Tier].T:\n os.chdir(Tier_path)\n os.mkdir(Gene)\n os.chdir(Gene)\n for Organism in self.what.org_list:\n Accession = str(self.what.gene_dict[Gene][Organism])\n Accession, Sup, Version = Accession.partition('.')\n Accession = Accession.upper()\n server_flag = False\n for name in db_name:\n if server_flag is True:\n break\n name = str(name)\n server = BioSeqDatabase.open_database(\n driver='sqlite3', db=where.VERT_MAM + ('/Databases/%s' % name))\n for sub_db_name in server.keys():\n db = server[sub_db_name]\n\n try:\n record = db.lookup(accession=Accession)\n with open('%s_%s.gbk' % (Gene, Organism), 'w') as GB_file:\n GB_file.write(record.format('genbank'))\n print(GB_file.name, 'created')\n server_flag = True\n break\n except IndexError:\n print('Index Error')\n continue","def gif_generation(orig_label_path, bound_data_path):\n for sample in os.listdir(bound_data_path):\n if not sample.startswith('.') and osp.isdir(osp.join(bound_data_path, sample)):\n sample_path = osp.join(bound_data_path, sample)\n for artery in os.listdir(sample_path):\n orig_label_pick_path = osp.join(orig_label_path, sample, artery, 'data.pkl')\n bound_pick_path = osp.join(bound_data_path, sample, artery, 'data.pkl')\n\n # function to save result of each artery into gif\n save_gif_artery(orig_label_pick_path, bound_pick_path)","def zip_imagenet100c():\n #First make sure the directory we are given is correct!\n if not os.path.isdir(DATA_SRC_ROOT):\n raise Exception(\"Bad filepath given\")\n\n #create the destiantion directories if they don't exist\n if not os.path.isdir(IMAGENET100_DIR):\n os.mkdir(IMAGENET100_DIR)\n\n #grab the subset wnids for the 100 class-subset\n with open(IMAGENET100_CLASSES) as f:\n subset_wnids = f.readlines()\n subset_wnids = [x.strip() for x in subset_wnids] #list of the 100 WNIDs we grab\n\n #Grab the names of all of the folders inside the root data source\n #Structure is distortion/sub_distortion/level/wnids\n for distortion in os.listdir(DATA_SRC_ROOT):\n if distortion != \"meta.bin\":\n print(distortion)\n\n folder_path = os.path.join(DATA_SRC_ROOT, distortion)\n\n if not os.path.isdir(folder_path):\n continue\n\n for sub_distortion in os.listdir(folder_path):\n print(sub_distortion)\n\n subfolder_path = os.path.join(folder_path, sub_distortion)\n\n if not os.path.isdir(subfolder_path):\n continue\n\n for level in os.listdir(subfolder_path):\n print(level)\n\n level_path = os.path.join(subfolder_path, level)\n\n #grab the correcrt validation d9recotires\n for wnid in os.listdir(level_path):\n wnid_path = os.path.join(level_path, wnid)\n\n if not os.path.isdir(wnid_path):\n continue\n\n if wnid in subset_wnids:\n dest_path = os.path.join(IMAGENET100_DIR, distortion, sub_distortion, level, wnid)\n\n shutil.copytree(wnid_path, dest_path)\n\n #copy the metadata bin file\n meta_file = os.path.join(DATA_SRC_ROOT, 'meta.bin')\n meta_dest = os.path.join(IMAGENET100_DIR, 'meta.bin')\n\n shutil.copy(meta_file, meta_dest)\n\n #Zip the destinatio file\n shutil.make_archive(ZIP_PATH + '/ImageNet100C', 'tar', IMAGENET100_DIR)","def processSetOfCerFiles(files):\n printHeader()\n \n k = 0\n for f in files:\n k = k + 1\n sz = get_file_size(f)\n with open(f, 'rb') as fb:\n processCerFile(k, fb, sz=sz)","def main():\n\n if args.sims[0].lower() == 'all':\n args.sims = xl.get_all_sims(args.base_dir)\n have_full_sim_dir = True\n else:\n have_full_sim_dir = False\n \n for isim in args.sims:\n\n if have_full_sim_dir:\n wdir = isim\n else:\n wdir = xl.get_sim_dir(args.base_dir, isim)\n \n print(\"\")\n print(\"====================================================================\")\n print(f\"=== Processing {wdir} ===\")\n print(\"====================================================================\") \n print(\"\")\n \n for iisnap, isnap in enumerate(args.snaps):\n\n # Account for possibly different VR numbering than (desired) output\n if args.vr_snaps is None:\n ivsnap = isnap\n else:\n ivsnap = args.vr_snaps[iisnap]\n \n process_snap(wdir, args.out_file, isnap, ivsnap)","def main():\n\n #Getthefiles\n all_fna_file_path = []\n path_to_all_info = '/Users/gustavotamasco/mdrkrp/project_MDR_KRPgenomes_parsnp'\n #path_to_all_info = argv[1]\n dirpath=os.getcwd()\n os.chdir(path_to_all_info)\n genome_files = list_directories(path_to_all_info)\n os.chdir(\"/Users/gustavotamasco/mdrkrp/plasmids\")\n plasmid_files = list_directories(\"/Users/gustavotamasco/mdrkrp/plasmids\")\n\n\n '''Genomes'''\n #for genome in genome_files:\n #if \"fna\" in genome:\n #print(genome)\n #run_plasflow(genome)\n\n '''Eval Plasmids'''\n for organism in plasmid_files:\n if \"plasflow_plasmids\" in organism:\n run_plasclass(organism)\n run_blastn(organism)\n\n '''Mining info'''\n data = {}\n blast_info_path = \"/Users/gustavotamasco/mdrkrp/plasmids/plasmid_blast\"\n blast_files = list_files_new_source(blast_info_path)\n for org_b in blast_files:\n if \"genome\" not in org_b:\n parse_blast(org_b, blast_info_path, data)","def process_ecr(cas, cas_dir, sortiefile, ncsize):\n\n xcpt = [] # try all files for full report\n # ~~ copy output files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n for key in cas.out_files:\n submit = cas.out_files[key].split(';')\n tmp_file_name = submit[1]\n file_name = cas.values[key]\n if submit[5] == 'MULTI': # POSTEL3D\n npsize = 1\n while 1: # HORIZONTAL SECTION FILES\n file_name = path.join(cas_dir,\n file_name\\\n + '_{0:03d}'.format(npsize))\n if path.isfile(file_name):\n base, ext = path.splitext(file_name)\n i = 0\n # this would be an infinite loop only if you have an\n # inifite number of files\n while 1:\n i = i + 1\n if not path.isfile(base+'_old'+str(i)+ext):\n break\n shutil.move(file_name, base+'_old'+str(i)+ext)\n tmp_file_name = tmp_file_name+\\\n '_{0:03d}'.format(npsize)\n if not path.isfile(tmp_file_name):\n break\n shutil.move(tmp_file_name, file_name)\n print(' moving: '+ path.basename(file_name))\n npsize = npsize + 1\n npsize = 1\n while 1: # VERTICAL SECTION FILES\n nptime = 1\n v_file = tmp_file_name+\\\n '_{0:03d}'.format(npsize)+'-{0:03d}'.format(nptime)\n if not path.isfile(v_file):\n break\n while 1:\n file_name = path.join(cas_dir,\n file_name+\\\n '_{0:03d}'.format(npsize)+\\\n '-{0:03d}'.format(nptime))\n if path.isfile(file_name):\n base, ext = path.splitext(file_name)\n i = 0\n # this would be an infinite loop only if you have an\n # inifite number of files\n while 1:\n i = i + 1\n if not path.isfile(base+'_old'+str(i)+ext):\n break\n shutil.move(file_name, base+'_old'+str(i)+ext)\n tmp_file_name = tmp_file_name\\\n + '_{0:03d}'.format(npsize)\\\n + '-{0:03d}'.format(nptime)\n if not path.isfile(tmp_file_name):\n break\n shutil.move(tmp_file_name, file_name)\n print(' moving: '+ path.basename(file_name))\n nptime = nptime + 1\n npsize = npsize + 1\n # MAIN MODULE\n elif submit[5] == 'PARAL' and ncsize > 1:\n npsize = 0\n c_base, c_ext = path.splitext(file_name)\n while 1:\n file_name = path.join(cas_dir,\n c_base\\\n + '{0:05d}-{1:05d}'\\\n .format(ncsize-1, npsize)\\\n + c_ext)\n if path.isfile(file_name):\n base, ext = path.splitext(file_name)\n i = 0\n # this would be an infinite loop only if you have an\n # inifite number of files\n while 1:\n i = i + 1\n if not path.isfile(base+'_old'+str(i)+ext):\n break\n shutil.move(file_name, base+'_old'+str(i)+ext)\n tmp_file_name_par = tmp_file_name+\\\n '{0:05d}-{1:05d}'.format(ncsize-1, npsize)\n if not path.isfile(tmp_file_name_par):\n break\n shutil.move(tmp_file_name_par, file_name) #shutil.copy2(tmp_file_name,file_name)\n print(' moving: '+ path.basename(file_name))\n npsize = npsize + 1\n elif submit[5] == 'MULTI2':\n for itmp_file_name in listdir('.'):\n if itmp_file_name.count(tmp_file_name) == 1:\n base, ext = path.splitext(file_name)\n new_tmp_file_name = \\\n itmp_file_name.lower()\\\n .replace(tmp_file_name.lower(),\n base)\n new_file_name = path.join(cas_dir, new_tmp_file_name) + ext\n if path.isfile(new_file_name):\n base, ext = path.splitext(new_file_name)\n i = 0\n # this would be an infinite loop only if you have an\n # inifite number of files\n while 1:\n i = i + 1\n if not path.isfile(base+'_old'+str(i)+ext):\n break\n shutil.move(new_file_name, base+'_old'+str(i)+ext)\n shutil.move(itmp_file_name, new_file_name)\n print(' moving: '+ path.basename(new_file_name))\n else:\n file_name = path.join(cas_dir, file_name)\n if path.isfile(file_name):\n base, ext = path.splitext(file_name)\n i = 0\n # this would be an infinite loop only if you have an\n # inifite number of files\n while 1:\n i = i + 1\n if not path.isfile(base+'_old'+str(i)+ext):\n break\n shutil.move(file_name, base+'_old'+str(i)+ext)\n if not path.isfile(tmp_file_name):\n xcpt.append({'name':'process_ecr',\n 'msg':'did not create outfile: '+\\\n path.basename(file_name)+' ('+tmp_file_name+')'})\n continue\n shutil.move(tmp_file_name, file_name)\n print(' moving: '+ path.basename(file_name))\n\n # ~~~ copy the sortie file(s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n sortiefiles = []\n if sortiefile != None:\n crun = path.basename(sortiefile)\n cref = path.join(cas_dir, sortiefile)\n if not path.isfile(crun):\n xcpt.append({'name':'process_ecr',\n 'msg':'did not create listing file: '+\\\n path.basename(cref)+' ('+crun+')'})\n raise TelemacException(xcpt) # raise full report\n shutil.copy(crun, cref)\n print(' copying: '+ path.basename(cref))\n sortiefiles.append(cref)\n\n # ~~~> If in parallel, also copy the slave log files\n # called PEnnnnn_xxxxx.log\n # for slave x of n but for the last one called the sortie file\n if ncsize > 1:\n for i in range(ncsize-1):\n slavefile = 'PE{0:05d}-{1:05d}.LOG'.format(ncsize-1, i+1)\n base, ext = path.splitext(sortiefile)\n slogfile = base+'_p'+'{0:05d}'.format(i+1)+ext\n crun = slavefile\n cref = path.join(cas_dir, slogfile)\n if not path.isfile(crun):\n xcpt.append({'name':'process_ecr',\n 'msg':'could not find the listing file: '\\\n + crun})\n raise TelemacException(xcpt) # raise full report\n shutil.copy(crun, cref)\n print(' copying: '+ path.basename(cref))\n sortiefiles.append(cref)\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n if xcpt != []:\n raise TelemacException(xcpt) # raise full report\n return sortiefiles","def overall_annotation_function(path_to_species_trees, path_to_gene_trees, project_name = \"myproject\", path_to_ranger_outputs = \"\", ):\r\n\r\n #initially gather the names of the datasets from the species_trees folder\r\n dataset_names = gather_dataset_names(path_to_species_trees, project_name, \"_CC\")\r\n #create an object of class PhyloData for each unique dataset\r\n phylodata_objects = []\r\n for name in dataset_names:\r\n phylodata_objects.append(annotate_classes.PhyloData(name))\r\n #for each object, have it try and assign itself the correct files\r\n print(\"populating phylodata objects\")\r\n populate_objects(phylodata_objects, project_name, path_to_species_trees, path_to_gene_trees, path_to_ranger_outputs)\r\n #run the visualizer for each object\r\n parse_and_visualize(phylodata_objects, project_name)","def test_2_fusion(install_test_files, data_dir):\n with make_workdir() as workdir:\n cl = [\"bcbio_nextgen.py\",\n get_post_process_yaml(data_dir, workdir),\n os.path.join(data_dir, os.pardir, \"test_fusion\"),\n os.path.join(data_dir, \"run_info-fusion.yaml\")]\n subprocess.check_call(cl)","def combine_netCDF_rh_cmip6(directory, model):\n\n # Make a list of the files in the above folder to loop through\n list_files = glob.glob(directory)\n list_files = np.array(list_files)\n newlist = np.sort(list_files)\n\n # Make a cubelist to add each file (cube) to\n Cubelist = iris.cube.CubeList([])\n\n # loop for each file in newlist\n for i in range(0, len(newlist)):\n\n with warnings.catch_warnings():\n warnings.simplefilter('ignore', FutureWarning)\n warnings.simplefilter('ignore', UserWarning)\n \n # Load cube\n cube = iris.load_cube(newlist[i])\n \n # matching all standard names\n cube.standard_name = 'heterotrophic_respiration_carbon_flux'\n\n # matching cube metadata\n if i == 0:\n metadata1 = cube.metadata\n else:\n cube.metadata = metadata1\n \n # creating latitude and longitude bounds\n if model=='IPSL-CM6A-LR' or model=='CNRM-ESM2-1':\n cube.coord('latitude').guess_bounds()\n cube.coord('longitude').guess_bounds()\n \n # removing time attributes\n if model=='IPSL-CM6A-LR':\n cube.coord('time').attributes.pop('time_origin')\n \n # Append this cube to the cubelist\n Cubelist.append(cube)\n\n # matching attributes\n unify_time_units(Cubelist)\n equalise_attributes(Cubelist)\n # Concatenate each cube in cubelist together to make one data file (cube)\n new_cube = Cubelist.concatenate_cube()\n \n for cube in Cubelist:\n lon_bounds = Cubelist[0].coord('longitude').bounds\n cube.coord('longitude').bounds = lon_bounds\n\n for i, cube in enumerate(Cubelist):\n if cube.coord('time').units == Cubelist[0].coord('time').units:\n pass\n else:\n print(i)\n\n return new_cube","def process_csvs(conn: Connection, basedir: Path) -> None:\n process_files(conn, basedir/\"files.csv\")\n process_notes(conn, basedir/\"notes.csv\")\n process_links(conn, basedir/\"links.csv\")\n process_clusters(conn, basedir/\"clusters.csv\")\n process_bibliography(conn, basedir/\"bibliography.csv\")\n process_citations(conn, basedir/\"citations.csv\")","def main():\n for dim in (50, 100, 200, 300):\n data_file = data.FileFinder().get_file('GLOVE_TXT_FILE').format(dim)\n output_file = data.FileFinder().get_file('GLOVE_WORD2VEC_FILE').format(dim)\n print(\"Converting {} to {}\".format(data_file, output_file))\n glove2word2vec(data_file, output_file)","def main():\n\n #Getthefiles\n all_fna_file_path = []\n path_to_all_info = '/Users/gustavotamasco/Google Drive/Shared drives/Projeto MDR KRP/Dados_Sequenciamento/'\n dirpath=os.getcwd()\n os.chdir(path_to_all_info)\n directories = list_directories(path_to_all_info)\n\n '''Genomes'''\n genomes_path = \"{}{}\".format(path_to_all_info,directories[0])\n os.chdir(genomes_path)\n genome_dir = list_directories(genomes_path)\n for organism in genome_dir:\n fna_files = list_files(all_fna_file_path,genomes_path,organism)\n print_status(fna_files)\n\n '''Building a dir of fna files'''\n genomes_fna_path = \"{}genomes_parsnp\".format(dirpath)\n create_genomes_dir(genomes_fna_path)\n os.chdir(genomes_fna_path)\n for file in fna_files:\n move_file(file, genomes_fna_path)\n\n '''Adding extra organism from a different source'''\n klebs = \"/Users/gustavotamasco/mdrkrp/klebs\"\n k_files = list_files_new_source(klebs)\n for k_file in k_files:\n if \".fna\" in k_file:\n final_k_file = \"{}/{}\".format(klebs,k_file)\n move_file(final_k_file, genomes_fna_path)\n\n\n '''Run parsnp'''\n run_parsnp(dirpath, genomes_fna_path)","def gzip_files(topDir, afni_file,localizerfile):\n\n print 'GZIPPING FILES...'\n\n command1 = \"gzip %s*BRIK\" % (topDir)\n command2 = \"gzip %s*HEAD\" % (topDir)\n command3 = \"gzip %s%s/pb00*\" % (topDir,afni_file)\n command4 = \"gzip %s%s/errts*\" % (topDir,afni_file)\n command5 = \"gzip %s%s/fitts*\" % (topDir,afni_file)\n command6 = \"gzip %s%s/*al*\" % (topDir,afni_file)\n command7 = \"gzip %s%s/*ns*\" % (topDir,afni_file)\n command8 = \"gzip %s%s/all_runs*\" % (topDir,afni_file)\n command9 = \"gzip %s%s/*mask*\" % (topDir,afni_file)\n command10 = \"gzip %s*nii\" % (topDir)\n command11 = \"gzip %s%s/pb01*\" % (topDir,afni_file)\n command12 = \"gzip %s%s/pb03*\" % (topDir,afni_file)\n command13 = \"gzip %s%s/pb02*\" % (topDir,afni_file)\n\n os.system(command1)\n os.system(command2)\n os.system(command3)\n os.system(command4)\n os.system(command5)\n os.system(command6)\n os.system(command7)\n os.system(command8)\n #os.system(command9)\n os.system(command10)\n os.system(command11)\n try:\n os.system(command12)\n except:\n print 'Could not gzip pb03 files'\n\n if localizerfile == 'facescene_loc':\n os.system(command13)","def main():\n print(\n \"\"\"\n\n ##########################################################\n # #\n # #\n # Compiling Colocalized Cyano Datasets #\n # #\n # #\n ##########################################################\n\n \n \"\"\"\n )\n cyanoFiles = glob.glob(f\"{COLOCALIZED_DIR}*.csv\")\n makedir(COMPILED_DIR)\n dfCompiled = pd.DataFrame({})\n for cyanoFile in cyanoFiles:\n print(f\"Compiling {cyanoFile}\")\n data = unify(cyanoFile)\n if len(dfCompiled ) < 1:\n dfCompiled = data\n else:\n dfCompiled = pd.concat([dfCompiled, data], ignore_index=True) \n dfCompiled.to_csv(f\"{COMPILED_DIR}compiled.csv\", index=False)","def load_cogs(self):\n\n path = \"cogs/\" # Should always have a trailing slash\n import_path = path.replace(\"/\", \".\")\n extensions: list[str] = [\n import_path + file.replace(\".py\", \"\")\n for file in os.listdir(path)\n if os.path.isfile(f\"{path}{file}\")\n ]\n\n for extension in extensions:\n try:\n self.load_extension(extension)\n except errors.ExtensionAlreadyLoaded:\n pass\n\n log.info(f\"Loaded {len(self.commands)} commands from {len(self.cogs)} cogs\")","def finalize(param, input_files='count_files'):\n\n import csv\n HELPER.writeLog('Collecting featureCount raw counts ... \\n', param)\n\n #check which of these files are actually available\n working_files = [iFile for iFile in param[input_files] if iFile != '']\n\n if len(working_files) > 0:\n #get feature ID using the first column in the first file in the list of working files\n csv_file = open(working_files[0])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n\n #For featureCount output, we want to skip the first two lines as they\n #include the featureCount call and the headers which we don't want\n next(csv_reader, None)\n next(csv_reader, None)\n\n #Now start by taking the list of identifier,\n #which is the first column in the file\n counts = [row[0] for row in csv_reader]\n csv_file.close()\n\n #get all the expression values\n header = 'ID'\n for idx in range(param['num_samples']):\n if param[input_files] != '':\n header = header+'\\t'+param['stub'][idx]\n csv_file = open(param[input_files][idx])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n\n #Here too we want to skip the first two lines, before getting the counts\n next(csv_reader, None)\n next(csv_reader, None)\n #Now start getting the counts (row[6]) and add in the ID (counts[i]) before it\n idx = 0\n for row in csv_reader:\n counts[idx] = counts[idx]+'\\t'+row[6]\n idx += 1\n csv_file.close()\n\n #output the file\n out_file = param['working_dir']+'deliverables/featureCount_raw_counts.txt'\n out_handle = open(out_file, 'w')\n out_handle.write(header+'\\n')\n\n for i in range(len(counts)):\n out_handle.write(counts[i]+'\\n')\n out_handle.close()\n\n #output_phenotype_file\n HELPER.writeLog('Writing phenotype data ... \\n', param)\n MODULE_HELPER.output_sample_info(param)\n\n #write summary stats\n #featureCount does this on its own so we can just fetch each summary file\n #check which of these files are actually available\n working_files = [iFile+'.summary' for iFile in param[input_files] if iFile != '']\n\n if len(working_files) > 0:\n #get Status column from summary file using the first column in\n #the first file in the list of working files\n csv_file = open(working_files[0])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n #Here, we want to skip the first line, as it simply points to the\n #alignment file used when running featureCount\n next(csv_reader, None)\n\n #Now start by taking the list of identifier,\n #which is the first column in the file\n entry = [row[0] for row in csv_reader]\n csv_file.close()\n\n #get all the summary stats for each sample\n header = 'Status'\n\n for idx in range(param['num_samples']):\n if param[input_files] != '':\n header = header+'\\t'+param['stub'][idx]\n #Fetch the corresponding sample's summary file\n csv_file = open(param[input_files][idx]+'.summary')\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n #Again, we want to skip the first line, as it simply points\n #to the alignment file used when running featureCount\n next(csv_reader, None)\n\n #Now start getting the stats (row[1]) and add in the Status\n # (counts[i]) before it\n i = 0\n for row in csv_reader:\n entry[i] = entry[i]+'\\t'+row[1]\n i += 1\n csv_file.close()\n #output the file\n out_handle = open(param['working_dir']+\n 'results/featureCount/featureCount_stats.txt',\n 'w')\n out_handle.write(header+'\\n')\n\n for i in range(len(entry)):\n out_handle.write(entry[i]+'\\n')\n out_handle.close()\n else:\n print 'featureCount was not run successfully on any of the files..\\n'","def voorbeeld():\n os.system(\"rm Twogs.txt cogs.txt\")\n os.system(\"touch Twogs.txt cogs.txt\")\n orgslijst = [y[:-1] for y in openfile(\"orglijst.txt\") if \"\\n\" in y]\n for x in range(len(orgslijst)):\n org1 = orgslijst[x]\n for y in range(len(orgslijst)):\n org2 = orgslijst[y]\n if x > y:\n schrijfnaartwog(org1, org2)\n print(org1)\n prots, twogs, cogs = openneededfiles(org1)\n if x >= 2:\n searchforcog(cogs, twogs, prots)","def FSC2(input_dir, num_reps=50, min_sims=100000, max_ecm=20, calc_CI=False, numcores=1, scratch_mb='200', time_scratch=\"01:50:00\", mem=\"200\", print1=False, overwrite=\"None\", fsc2_path=\"/storage/plzen1/home/holcovam/programs/fsc26_linux64/fsc26\"):\n Data_Files = []\n tpl_files = []\n est_files = []\n CI_Data_Files = []\n shlist = []\n\n if input_dir.endswith(\"/\") is False:\n input_dir += \"/\"\n\n for path in os.listdir(input_dir):\n if os.path.isdir(input_dir + path) and path.startswith(\"FSC2input\"):\n samp_name = path.split(\"_\")[1]\n #folder_name = samp_name\n if samp_name + \"_DSFS.obs\" in os.listdir(input_dir + path):\n for i in range(0, num_reps):\n new_file = open(input_dir + path + \"/\" + samp_name + str(i) + \"_DSFS.obs\", 'w')\n with open(input_dir + path + \"/\" + samp_name + \"_DSFS.obs\") as data_file:\n for line in data_file:\n new_file.write(line)\n new_file.close()\n Data_Files.append(input_dir + path + \"/\" + samp_name + str(i) + \"_DSFS.obs\")\n else:\n print(\"Did not find input data file for: \", samp_name)\n if calc_CI == \"True\":\n num_files = 0\n for file in os.listdir(input_dir + path):\n if file.endswith(\"_DSFS.obs\") and file.split(\"_\")[-2].split(\".\")[-1][0:3] == \"rep\" and file != samp_name + \"_DSFS.obs\":\n for i in range(0, num_reps):\n new_file = open(input_dir + path + \"/\" + samp_name + file.split(\"_\")[-2].split(\".\")[-1].split(\"_\")[0]+ \"_\" + str(i) + \"_DSFS.obs\", 'w')\n with open(input_dir + path + \"/\" + file) as data_file:\n for line in data_file:\n new_file.write(line)\n new_file.close()\n CI_Data_Files.append(input_dir + path + \"/\" + samp_name + file.split(\"_\")[-2].split(\".\")[-1].split(\"_\")[0]+ \"_\" + str(i) + \"_DSFS.obs\")\n num_files += 1\n if len(CI_Data_Files) < 1:\n print(\"Did not find bootstrap replicates for: \", samp_name)\n else:\n print(\"Found \", num_files, \" replicate dsfs files for CI calculation for \", samp_name)\n if path.endswith(\".tpl\"):\n tpl_files.append(path)\n est_files.append(path.split(\".\")[0])\n if len(tpl_files) == 0:\n print(\"Did not find any tpl files!! Aborting!!\")\n else:\n if calc_CI == \"True\":\n Data_Files = CI_Data_Files\n for file in Data_Files:\n name = file.split(\"_DSFS\")[0]\n samp_name = name.split(\"/\")[-1]\n folder_name = samp_name [0:11]\n for tpl in tpl_files:\n tpl_name = tpl.split(\".tpl\")[0]\n if os.path.isdir(name + \"_\" + tpl_name) is False or overwrite == \"hard\":\n new_tpl = open(name + \"_\" + tpl_name + \".tpl\", 'w')\n new_data = open(name + \"_\" + tpl_name + \"_DSFS.obs\", 'w')\n\n with open(file, 'r') as data:\n for i, line in enumerate(data):\n if i == 1:\n pop_info = line.strip(\"\\n\").strip(\"\\t\").split(\"\\t\")\n pop_num = int(pop_info[0])\n samp_nums = pop_info[-pop_num:]\n new_data.write(line)\n with open(input_dir + tpl, 'r') as template:\n samp_num_lines = pop_num + 4\n for i, line in enumerate(template):\n if i < samp_num_lines:\n new_tpl.write(line)\n elif i == samp_num_lines:\n for num in samp_nums:\n new_tpl.write(num + \"\\n\")\n elif i >= samp_num_lines + len(samp_nums):\n new_tpl.write(line)\n new_est = open(name + \"_\" + tpl_name + \".est\", 'w')\n try:\n with open(input_dir + tpl_name + \".est\") as est:\n for line in est:\n new_est.write(line)\n except FileNotFoundError:\n print(\"Did not find est file for: \", tpl)\n #folder_name = samp_name ''.join(i for i in s if not i.isdigit())\n shname = name + \"_\" + tpl_name + \".sh\"\n shfile5 = open(shname, 'w')\n shfile5.write('#!/bin/bash -e\\n' +\n '#PBS -N '+samp_name+'\\n' +\n '#PBS -l walltime='+str(time_scratch)+'\\n' +\n '#PBS -l select=1:ncpus='+str(numcores)+':mem='+str(mem)+'mb:scratch_local='+str(scratch_mb)+'mb\\n' +\n '#PBS -m abe\\n' +\n '#PBS -j oe\\n\\n' +\n 'module add python-3.4.1-gcc\\n'+\n 'module add python34-modules-gcc\\n'+\n 'trap \\'clean_scratch\\' TERM EXIT\\n'+\n 'if [ ! -d \"$SCRATCHDIR\" ] ; then echo \"Scratch not created!\" 1>&2; exit 1; fi \\n' +\n 'DATADIR=\"/storage/plzen1/home/holcovam/ScanTools\"\\n' +\n 'cp $DATADIR/'+ input_dir + \"FSC2input_\" + folder_name+ \"/\" + samp_name + \"_\" + tpl_name + '* $SCRATCHDIR || exit 1\\n'+\n 'cp '+fsc2_path+' $SCRATCHDIR || exit 1\\n'+\n 'cd $SCRATCHDIR || exit 2\\n' +\n 'echo data loaded at `date`\\n\\n' +\n 'chmod +x fsc26 \\n' +\n #'ls -l \\n' +\n './fsc26 -t ' + samp_name + \"_\" + tpl_name + '.tpl -e ' + samp_name + \"_\" + tpl_name + '.est -n ' + str(min_sims) + ' -u -d -q -L ' + str(max_ecm) + ' -M \\n' + \n 'rm seed.txt \\n'+\n 'rm fsc26\\n'+\n 'rm *DSFS.obs\\n'+\n 'rm *.sh\\n'+\n 'rm *.tpl \\n'+\n 'rm *.est \\n'+\n #'ls -l \\n' +\n 'cp $SCRATCHDIR/*.par $DATADIR/'+ input_dir + \"FSC2input_\" + folder_name+' || exit 1\\n'+\n 'rm *.par \\n'+\n 'cp -r $SCRATCHDIR/* $DATADIR/'+input_dir+' || export CLEAN_SCRATCH=false\\n'+\n 'printf \"\\\\nFinished\\\\n\\\\n\"\\n')\n shfile5.close()\n shlist.append(shname)\n\n############IF PROBLEM WITH EXCESS OF NONCONVERGED CHAINS, COPY /home/majda/alpine/fastsimcoal2/afterWPSG/scripts/notConverged.py here ###################\n\n else:\n print(\"Output for \" + samp_name + \"_\" + tpl_name + \" already exists. Use hard_overwrite = True to overwrite.\")\n return shlist","def calc_GC(filepath):\n liste=['small.exon.piRNA_2.fa', 'small.exon.piRNA_1.fa', 'small.exon.piRNA_3.fa']\n \n length=list(range(0,34))\n d={}\n for i in length:\n d[i]={'A':0, 'G':0, 'T':0, 'C':0}\n for i in liste:\n with open(filepath+'/'+i, 'r') as f:\n for line in f:\n #fasta header starts with >\n if line.startswith('>'):\n pass\n else:\n line_l=list(line)\n for el in range(len(line_l)):\n if line_l[el]=='A':\n d[el]['A']+=1\n elif line_l[el]=='T':\n d[el]['T']+=1\n elif line_l[el]== 'G':\n d[el]['G']+=1\n elif line_l[el]== 'C':\n d[el]['C']+=1\n\n df=pd.DataFrame.from_dict(d)\n df=df.transpose()\n df.index = np.arange(1, len(df) + 1)\n \n\n df['A [%]']=df['A']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\n df['G [%]']=df['G']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\n df['T [%]']=df['T']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\n df['C [%]']=df['C']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100","def coverage_files() -> Iterable[str]:\n packs_path = CONTENT_PATH / \"Packs\"\n for cov_path in packs_path.glob(\"*/Integrations/*/.coverage\"):\n yield str(cov_path)\n for cov_path in packs_path.glob(\"*/Scripts/*/.coverage\"):\n yield str(cov_path)","def per_sample_dot_files(self):\n # The output directory #\n directory = DirectoryPath(self.a.out_dir+'per_sample_ontology/')\n directory.create_if_not_exists()\n # Main loop #\n for i, sample in self.df_sample_concepts.iteritems():\n # File path #\n sanitized_name = \"\".join([c for c in sample.name if re.match(r'\\w', c)])\n dot_path = directory + sanitized_name +'.dot'\n pdf_path = directory + sanitized_name +'.pdf'\n # Counts #\n counts = sample / sample.sum()\n counts = dict(counts)\n envos = counts.keys()\n # Skip sample if it has no counts #\n if sample.sum() == 0: continue\n # Make graph #\n graph = self.a.ontology.get_subgraph(envos)\n graph = self.a.ontology.add_weights(graph, counts)\n graph = self.a.ontology.add_style(graph)\n # Write output #\n self.a.ontology.write_to_dot(graph, dot_path)\n self.a.ontology.add_legend(dot_path)\n self.a.ontology.draw_to_pdf(dot_path, pdf_path)","def processFiles(self, testDataFiles, sourceDirectory, sourceFilename, outputFile, language, IncludeSourceInOutput, maxRunTime, interpreter, maxOutputLines, AutoGraderVersion):\n \n print (\"***Start***\")\n self.sourceDirectory = sourceDirectory\n #self.TopLevelFilesFound = []\n #self.subdirs = {} #dictionary of sbudirectories\n \n\n #delete the output file\n self._removeFile(outputFile)\n\n #create the html header. Use the name of the source directory as the header text.\n self._MakeHtmlHeader(outputFile, language, \"AutoGrader\", os.path.split(sourceDirectory)[-1])\n\n #--------- C++ ---------\n if language == 'C++':\n #call _recursivelyFindFiles to generate the self.TopLevelFilesFound and self.SubDirsFound lists\n (self.TopLevelFilesFound, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, \".cpp\")\n (tlf, sd) = self._recursivelyFindFiles(sourceDirectory, \".cc\")\n\n self.TopLevelFilesFound += tlf\n self.SubDirsFound += sd\n\n exeFile = sourceDirectory + '/' + 'AG.out'\n\n\n def doInnerCppProcessing(sourceFiles, gradingTextLabel): #sourceFiles is a list of filenames\n #get/report the analytics on the source files\n self._reportFileAnalytics(sourceFiles, outputFile, language)\n \n #include source code here if selected\n if IncludeSourceInOutput == True:\n self._formatSource(sourceFiles, outputFile, language)\n\n #compile the file\n self._removeFile(exeFile)\n self._compileCppFiles(interpreter, sourceFiles, outputFile, exeFile, maxOutputLines)\n\n if os.path.isfile(exeFile): #did the compilation succeed?\n print(\"Compilation succeeded.\")\n self._reportErrorMsg(\"Compilation succeeded.<br>\", outputFile) \n if len(testDataFiles) == 0: #no input data required\n exec_time = self._shellExec('\"'+exeFile+'\"', '', '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n else:\n for dataFile in testDataFiles:\n self._reportDataFile(dataFile, outputFile)\n \n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\n _, filename = os.path.split(dataFile)\n print (\"processing '\" + filename + \"'...\")\n exec_time = self._shellExec('\"'+exeFile+'\"', '', dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n self._reportExecTime(exec_time, outputFile)\n print ()\n else:\n print(\"Executable not found. Check compiler output.\")\n self._reportErrorMsg(\"Executable not found. Check compiler output.<br>\", outputFile)\n\n self._removeFile(exeFile)\n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\n return\n\n\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\n #note that this is a list of tuples. The first element is the name of the file or directory.\n #the second element specifies if the first is a filename or directory name.\n filesAndDirs = []\n for x in self.TopLevelFilesFound:\n filesAndDirs.append((x,'file')) #add files\n\n for x in self.SubDirsFound:\n filesAndDirs.append((x,'dir')) #add directories\n\n filesAndDirs.sort() #sort the list\n\n print (\"**********************************\")\n for x in filesAndDirs:\n print (x)\n print (\"**********************************\")\n\n #now, process the sorted list depending on whether it is a single-file or directory.\n for n, x in enumerate(filesAndDirs):\n if x[1] == 'file': #this is a file\n #we will process source code in the top-level directory as single-file programs \n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n #doInnerCppProcessing([x[0]], 'student'+str(n))\n doInnerCppProcessing([x[0]], 'student')\n elif x[1] == 'dir': #this is a directory\n #we will process source code in sub-directories of the top-level directory as multi-file programs\n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n sourceFiles = []\n self._findFilesInDir(x[0], \".cpp\", sourceFiles) #add .h files to the files list\n self._findFilesInDir(x[0], \".cc\", sourceFiles) #add .h files to the files list\n self._findFilesInDir(x[0], \".h\", sourceFiles) #add .h files to the files list\n self._findFilesInDir(x[0], \".hpp\", sourceFiles) #add .hpp files to the files list\n #doInnerCppProcessing(sourceFiles, 'student'+str(n))\n doInnerCppProcessing(sourceFiles, 'student')\n else:\n print (\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\")\n \n \n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\n\n #--------- Python ---------\n elif language == 'Python':\n print ('Find sub dirs')\n #first, find all the subdirectories that contain a python source file that matches <sourceFilename>\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, sourceFilename)\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\n self.SubDirsFound = self._recursivelyFindFile(sourceDirectory, sourceFilename)\n\n print ('Find TLFs')\n #now, find all .py files in the top level directory.\n self.TopLevelFilesFound = self._findFilesInDir(sourceDirectory, \".py\", [])\n #(self.TopLevelFilesFound, _) = self._recursivelyFindFiles(sourceDirectory, \".py\")\n\n\n #does this block do anything??? 9/25/16\n #if sourceFilename == \"\" or sourceFilename == None:\n #self._recursivelyFindFiles(sourceDirectory, \".py\")\n #else:\n #self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\n\n\n def doInnerPythonProcessing(sourceFiles, topLevelModule, gradingTextLabel): #sourceFiles is a list of filenames\n #get/report the analytics on the source files\n self._reportFileAnalytics(sourceFiles, outputFile, language)\n \n #include source code here if selected\n if IncludeSourceInOutput == True:\n self._formatSource(sourceFiles, outputFile, language)\n\n\n if len(testDataFiles) == 0: #no input data required\n exec_time = self._shellExec('\"'+interpreter+'\"', topLevelModule, '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n self._reportExecTime(exec_time, outputFile)\n else:\n for dataFile in testDataFiles:\n self._reportDataFile(dataFile, outputFile)\n \n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\n _, filename = os.path.split(dataFile)\n print (\"processing '\" + filename + \"'...\")\n exec_time = self._shellExec('\"'+interpreter+'\"', topLevelModule, dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n self._reportExecTime(exec_time, outputFile)\n print ()\n \n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\n return\n\n\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\n #note that this is a list of tuples. The first element is the name of the file or directory.\n #the second element specifies if the first is a filename or directory name.\n filesAndDirs = []\n for x in self.TopLevelFilesFound:\n filesAndDirs.append((x,'file')) #add files\n\n for x in self.SubDirsFound:\n filesAndDirs.append((x,'dir')) #add directories\n\n filesAndDirs.sort() #sort the list\n\n print (\"**********************************\")\n for x in filesAndDirs:\n print (x)\n print (\"**********************************\")\n\n\n #now, process the sorted list depending on whether it is a single-file or directory\n for n, x in enumerate(filesAndDirs):\n if x[1] == 'file': #this is a file\n #we will process source code in the top-level directory as single-file programs \n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n #doInnerPythonProcessing([x[0]], x[0], 'student'+str(n))\n doInnerPythonProcessing([x[0]], x[0], 'student')\n elif x[1] == 'dir': #this is a directory\n #we will process source code in sub-directories of the top-level directory as multi-file programs\n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n #doInnerPythonProcessing(self._findFilesInDir(x[0],\".py\", []), x[0]+'/'+sourceFilename, 'student'+str(n))\n doInnerPythonProcessing(self._findFilesInDir(x[0],\".py\", []), x[0]+'/'+sourceFilename, 'student')\n else:\n print (\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\")\n \n \n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\n\n\n\n #--------- Unknown language ---------\n else:\n print ('Unknown language choice: ' + language)\n return\n\n \n self._reportErrorMsg('<br><font face=\"verdana\">', outputFile)\n self._reportErrorMsg('Report Generator: AutoGrader v' + AutoGraderVersion + '<br>', outputFile)\n if language == 'C++':\n self._reportErrorMsg('C++ Compiler: ' + interpreter + '<br>', outputFile)\n elif language == 'Python':\n self._reportErrorMsg('Python Interpreter: ' + interpreter + '<br>', outputFile)\n else:\n self._reportErrorMsg('Compiler/Interpreter: Not Specified <br>', outputFile)\n self._reportErrorMsg('<br></font>', outputFile)\n\n self._writeOutput('''<font size='+6'><input type=\"button\" style=\"font-size:20px;width:250px\" value=\"Download Feedback\" OnClick=\"download_feedback_file()\">\n <br><br></font></form>''', outputFile)\n\n\n feedback_filename = 'feedback.txt'\n download_script = '''\n <script type=\"text/javascript\">\n\n function download_feedback_file()\n {\n \n x = document.getElementsByName(\"student\").length\n msg = \"\"\n for (i=0; i<x; i++)\n {\n msg = msg + document.getElementsByName(\"student\")[i].value\n msg = msg + '\\\\n\\\\n-------------------------------------------------------\\\\n'\n }\n\n var element = document.createElement('a');\n element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(msg));\n element.setAttribute('download', \"''' + feedback_filename + '''\");\n\n element.style.display = 'none';\n document.body.appendChild(element);\n\n element.click();\n\n document.body.removeChild(element);\n }\n\n </script>\n </body></html>\n\n '''\n self._writeOutput(download_script, outputFile)\n\n self._writeOutput('</body></html>', outputFile)\n\n \n #open the output file using the default application\n cmd = 'open \"' + outputFile + '\"'\n os.system(cmd)\n \n print (\"***End***\\n\")\n\n\n\n #--------- ---------","def make_master_flats(dc):\n\n\t## Make EXTcheck: is there always the same number of extensions in each file\n\tprint \"Making master flats\"\n\t\n\t## Choose extensions you are using\n\t\n\tfor flat_type in ['FFS']: # Currently FFD is unsupported. If you have FFDs, add them to the list but you must have ONLY FFDs or ONLY FFSs in the dir. Otherwise the first element in the list will get overwritten!\n\t\t#~ print \"\\n\", flat_type, \"\\n\"\n\t\tfor i in dc:\n\t\t\tTRIM, TRIM1, VR, PS, PS1, OS, OS1 = CCD_sections((i[0], i[1]))\n\t\t\tfilelist = []\n\t\t\tfor f in glob.glob(RAW+'*'+flat_type+'*fits'):\n\t\t\t\tccd_conf = []\n\t\t\t\theader0 = fits.getheader(f)\n\t\t\t\theader1 = fits.getheader(f, ext=1)\n\t\t\t\tif header0['OBSMODE']==flat_type:\n\t\t\t\t\tfor KW in ['BINX', 'BINY']:\n\t\t\t\t\t\tccd_conf.append(header0[KW])\n\t\t\t\t\tfor KW in ['NAXIS1', 'NAXIS2']:\n\t\t\t\t\t\tccd_conf.append(header1[KW])\n\t\t\t\t\t\tif tuple(ccd_conf)==i:\n\t\t\t\t\t\t\tfilelist.append(f)\n\t\t\tlfl = len(filelist)\n\t\t\tif lfl > 0:\n\t\t\t\tBIN=CD+'/'+str(i[0])+'x'+str(i[1])+'/'\n\t\t\t\tWD=BIN+str(i[-2])+'x'+str(i[-1])+'/' # Bottom level dir with calibrated and master frames\n\t\t\t\tB=check_exist(WD, 'MF.fits', i)\n\t\t\t\tif B=='n':\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\thdul = fits.HDUList()\n\t\t\t\t\thdul.append(fits.ImageHDU())\n\t\t\t\t\t#~ MB = fits.open(WD+'MB.fits')\n\t\t\t\t\tx = np.array(range(0,i[-1]))\n\t\t\t\t\tfor EXT in (extensions):\n\t\t\t\t\t\tprint \"##################################################\"\n\t\t\t\t\t\tprint \"Stacking \"+`lfl`+' '+`i[-2]`+'x'+`i[-1]`+' channel '+`EXT`+' flat frames!'\n\t\t\t\t\t\tif EXT==1:\n\t\t\t\t\t\t\tPSC=PS1\n\t\t\t\t\t\t\tOSC=OS1\n\t\t\t\t\t\t\tTR=TRIM1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tPSC=PS1\n\t\t\t\t\t\t\tOSC=OS\n\t\t\t\t\t\t\tTR=TRIM\n\t\t\t\t\t\tsc = -1 # counts how many flats have mean>limit\n\t\t\t\t\t\tfor n, fn in enumerate(filelist):\n\t\t\t\t\t\t\tprint \"Files left:\",`lfl-n`+'/'+`lfl`\n\t\t\t\t\t\t\tim = fits.getdata(fn, ext=EXT)\n\t\t\t\t\t\t\tmeanval = np.mean(im[VR[0]:VR[1], TR[0]:TR[1]])\n\t\t\t\t\t\t\t#~ maxval = np.max(im[VR[0]:VR[1], TR[0]:TR[1]])\n\t\t\t\t\t\t\tmaxval = stats.scoreatpercentile(im[VR[0]:VR[1], TR[0]:TR[1]], 90)\n\t\t\t\t\t\t\texptime = fits.getheader(fn)['EXPTIME']\n\t\t\t\t\t\t\t#~ if meanval > 15000. and meanval < 40000. and maxval < 50000. and exptime>5.:\n\t\t\t\t\t\t\tif meanval > 16000. and meanval < 40000. and exptime>=5.:\n\t\t\t\t\t\t\t\tsc+=1\n\t\t\t\t\t\t\t\t#~ im[im<1]=1\n\t\t\t\t\t\t\t\tmscrow, sigmarow = median_row(OSC, PSC, TR, im)\n\t\t\t\t\t\t\t\tsh = np.shape(im)\n\t\t\t\t\t\t\t\tfor y in range(0, sh[0]):\n\t\t\t\t\t\t\t\t\tim[y] = im[y]-mscrow[y]\n\t\t\t\t\t\t\t\tF=im\n\t\t\t\t\t\t\t\tnorm = np.median(F[VR[0]:VR[1], TR[0]:TR[1]])\n\t\t\t\t\t\t\t\tF = F/norm #+np.min(F)+0.0001\n\t\t\t\t\t\t\t\tif sc==0:\n\t\t\t\t\t\t\t\t\tstack_arr = F\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tstack_arr = np.dstack((stack_arr, F))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tprint \"****************************************\"\n\t\t\t\t\t\t\t\tprint \"Rejected\", fn, \"AVG =\", meanval, \"EXPTIME =\", exptime\n\t\t\t\t\t\t\t\tprint \"****************************************\"\n\t\t\t\t\t\tprint 'Will stack a total of', np.shape(stack_arr)[2], 'flats'\n\t\t\t\t\t\tMF = np.median(stack_arr, axis=2)\n\t\t\t\t\t\thdul.append(fits.ImageHDU(MF))\n\t\t\t\t\t\thdul[EXT].header.set(\"NAXIS1\", np.shape(MF)[1])\n\t\t\t\t\t\thdul[EXT].header.set(\"NAXIS2\", np.shape(MF)[0])\n\t\t\t\t\thdul[0].header.set(\"CALIBR\", \"T\")\n\t\t\t\t\thdul[0].header.set(\"INSTRUME\", \"MAIA\")\n\t\t\t\t\thdul[0].header.set(\"BINX\", i[0])\n\t\t\t\t\thdul[0].header.set(\"BINY\", i[1])\n\t\t\t\t\thdul[0].header.set(\"CALMODE\", \"MASTER FLAT\")\n\t\t\t\t\thdul.writeto(WD+\"MF.fits\", clobber=True)\n\t\t\t\t\tprint \"############################################################\"\n\tprint \"Completed master flats\"","def _findFiles(self, inputfolder):\n protofile, caffemodel = None, None\n files = os.listdir(inputfolder)\n for f in files:\n name, ext = splitext(f)\n if ext == '.caffemodel':\n caffemodel = join(inputfolder, f)\n elif f == 'deploy.prototxt':\n protofile = join(inputfolder, f)\n return protofile, caffemodel","def main(root_dir=(join(Path(__file__).resolve().parents[1], 'data')), apply_masks=True):\n\n # Iterate over each volume in the root data directory\n for folder_name in os.listdir(root_dir):\n print(join(root_dir, folder_name))\n if 'results' not in folder_name and 'subj' in folder_name:\n\n # Create all of the directories and subdirectories\n create_train_test_val_dirs(join(root_dir, folder_name))\n\n # Populate the train, val, and test directories and their subdirectories\n populate_train_test_val_dirs_nonrandomly(join(root_dir, folder_name),\n val_ratio=0.00,\n test_ratio=0.00,\n preliminary_clahe=True,\n apply_masks=apply_masks)\n\n if apply_masks:\n # Apply masks to all of the images in this volume\n apply_masks_to_volume(join(root_dir, folder_name))\n\n # Get and save the residuals between ClearImages and CoregisteredBlurryImages\n create_and_populate_residual_dirs(join(root_dir, folder_name))","def generate_genome_import_files(genbank_path, output_dir):\n genbank = load_genbank(genbank_path)\n genome_path = os.path.join(output_dir, _genome_vert_name + '.json')\n write_import_file(generate_genome(genbank), genome_path)\n gene_path = os.path.join(output_dir, genbank.id, _gene_vert_name + '.json')\n write_import_file(generate_genes(genbank), gene_path)\n gene_edge_path = os.path.join(output_dir, genbank.id, _gene_edge_name + '.json')\n write_import_file(generate_gene_edges(genbank), gene_edge_path)"],"string":"[\n \"def genotype_gvcfs(gatk, xmx, cores,\\n inputs, output,\\n reference, bed_file=None):\\n commands = []\\n command = GENOTYPEGVCFS_TEMPLATE.format(xmx, gatk, reference, output)\\n command = command + ' --variant ' + ' --variant '.join(inputs)\\n if bed_file is not None:\\n command = command + \\\" -L \\\" + bed_file\\n commands.append(command)\\n output = os.path.join(os.path.dirname(output), 'all_sites.vcf')\\n command = GENOTYPEGVCFS_TEMPLATE.format(xmx, gatk, reference, output)\\n command = command + ' --variant ' + ' --variant '.join(inputs)\\n command = command + ' --includeNonVariantSites'\\n if bed_file is not None:\\n command = command + \\\" -L \\\" + bed_file\\n commands.append(command)\\n queue_jobs(commands, \\\"genotypeGVCFs\\\", cores)\",\n \"def build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, scratch=False, interval=None):\\n \\n name_batch1 = os.path.basename([item for item in combined_gvcf_files if \\\"batch1\\\" in item][0])\\n interval_name = \\\"\\\"\\n #there must be at least one batch so look for it, not elegant but works\\n if name_batch1.split(\\\"batch1\\\") != \\\".g.vcf.gz\\\":\\n interval_name = name_batch1.split(\\\"batch1\\\")[1].split(\\\".\\\")[0]\\n job_name = \\\"GenotypeGVCFs{}\\\".format(interval_name)\\n output_file = \\\"{}_joincalled{}.g.vcf.gz\\\".format(CONFIG[\\\"output_header\\\"], interval_name)\\n #create the sbatch file to analyse the current batch of samples\\n sbatch_file = os.path.join(working_dir, \\\"sbatch\\\", \\\"{}.sbatch\\\".format(job_name))\\n with open(sbatch_file, \\\"w\\\") as GenotypeGVCFs:\\n slurm = slurm_header(CONFIG[\\\"uppmax_project\\\"], job_name, working_dir)\\n GenotypeGVCFs.write(slurm)\\n GenotypeGVCFs.write(\\\"\\\\n\\\")\\n #rsync to scratch all samples\\n if scratch:\\n GenotypeGVCFs.write(\\\"mkdir -p $SNIC_TMP/{} \\\\n\\\".format(job_name)) # create tmp directory\\n GenotypeGVCFs.write(\\\"mkdir -p $SNIC_TMP/{}/VCF/ \\\\n\\\".format(job_name)) # create tmp directory\\n #now cycle over the samples, build the GATK command\\n combined_gvcf_string_input = \\\"\\\"\\n for combined_gvcf in combined_gvcf_files:\\n combined_gvcf_path_dir = combined_gvcf\\n if scratch:\\n GenotypeGVCFs.write(\\\"rsync -rptoDLv {}* $SNIC_TMP/{}/\\\\n\\\".format(combined_gvcf, job_name))\\n combined_gvcf_name = os.path.basename(combined_gvcf)\\n combined_gvcf_path_dir = \\\"$SNIC_TMP/{}/{}\\\".format(job_name, combined_gvcf_name)\\n combined_gvcf_string_input += \\\"-V {} \\\\\\\\\\\\n\\\".format(combined_gvcf_path_dir)\\n\\n GATK_command= \\\"java -Xmx250g -jar {} -T GenotypeGVCFs \\\\\\\\\\\\n\\\".format(CONFIG[\\\"GATK\\\"])\\n for option in CONFIG[\\\"walkers\\\"][\\\"GenotypeGVCFs\\\"]:\\n GATK_command += \\\"{} \\\\\\\\\\\\n\\\".format(option)\\n GATK_command += \\\"{} \\\".format(combined_gvcf_string_input)\\n if interval is not None:\\n GATK_command += \\\"-L {} \\\\\\\\\\\\n\\\".format(interval)\\n\\n if scratch:\\n GATK_command += \\\"-o $SNIC_TMP/{}/VCF/{}\\\\n\\\".format(job_name, output_file)\\n #once this is done rsync back to lupus\\n GATK_command += \\\"rsync $SNIC_TMP/{}/VCF/{}* {}/VCF/\\\\n\\\".format(job_name, output_file , working_dir)\\n else:\\n GATK_command += \\\"-o {}/VCF/{}\\\\n\\\\n\\\".format(working_dir, output_file)\\n GenotypeGVCFs.write(GATK_command)\\n #return path to sbach file\\n return sbatch_file\",\n \"def main(arguments):\\n folder_list = glob.glob(f\\\"{arguments.f}/*/\\\")\\n for d in folder_list:\\n if \\\"GALEN\\\" in d: continue\\n # get vcf file\\n try:\\n vcf = glob.glob(f\\\"{d}/*.vcf\\\")[0]\\n except:\\n raise FileNotFoundError(f\\\"{d} - vcf file not found\\\")\\n \\n print(f\\\"Processing {vcf}...\\\")\\n f = load(vcf)\\n final = find_gene(f, arguments.c)\\n output_file = vcf.replace(\\\".vcf\\\", \\\"_analyzed.csv\\\")\\n final.to_csv(output_file, index=False)\\n print(\\\"Done!\\\")\",\n \"def merge_gvcfs(gatk, xmx, cores, gvcfs, reference):\\n commands = []\\n outputs = []\\n no_groups = (len(gvcfs)/SPLIT_SIZE) + 1\\n for i in range(0, no_groups):\\n output = str(i) + '.g.vcf'\\n outputs.append(output)\\n command = MERGE_GVCFS_TEMPLATE.format(xmx, gatk, reference, output)\\n command = command + '--variant ' + ' --variant '.join(gvcfs[i:(i*SPLIT_SIZE + SPLIT_SIZE)])\\n commands.append(command)\\n queue_jobs(commands, \\\"mergeGVCFs\\\", cores)\\n return outputs\",\n \"def combine_gvcf(self, reference, gvcf_list, output, input_is_sorted=False, extension_list=[\\\"g.vcf\\\",],\\n tmp_dir=\\\"./tmp_combine_gvcf/\\\", max_files_per_merging=50, iteration=0, threads=None,\\n remove_intermediate_files=False):\\n\\n filtered_gvcf_list = []\\n for filename in gvcf_list:\\n for extension in extension_list:\\n if extension == filename[-len(extension):]:\\n filtered_gvcf_list.append(filename)\\n break\\n \\n if len(filtered_gvcf_list) <= max_files_per_merging:\\n options = self.parse_options(reference, filtered_gvcf_list, output, input_is_sorted, extension_list=extension_list)\\n self.execute(options, runtype=\\\"cp\\\")\\n if remove_intermediate_files:\\n shutil.rmtree(tmp_dir, ignore_errors=True)\\n\\n else:\\n self.safe_mkdir(tmp_dir)\\n iteration_dir = \\\"%s/iteration_%i/\\\" % (tmp_dir, iteration)\\n self.safe_mkdir(iteration_dir)\\n\\n number_of_files = len(filtered_gvcf_list)\\n\\n bins = np.arange(0, number_of_files, max_files_per_merging)\\n #print(bins)\\n if bins[-1] != number_of_files:\\n if number_of_files - bins[-1] < 2:\\n bins[-1] = number_of_files\\n else:\\n bins = np.append(bins, number_of_files)\\n\\n output_file_list = []\\n options_list = []\\n\\n merged_files = 0\\n for i in range(0, len(bins)-1):\\n output_file = \\\"%s/%i.g.vcf\\\" % (iteration_dir, i)\\n output_file_list.append(output_file)\\n #print(bins[i], bins[i+1])\\n\\n merged_files += bins[i+1] - bins[i]\\n options_list.append(self.parse_options(reference,\\n filtered_gvcf_list[bins[i]:bins[i+1]],\\n output_file,\\n input_is_sorted, extension_list=extension_list))\\n print(\\\"%i/%i files will be merged\\\" % (merged_files, number_of_files))\\n\\n self.parallel_execute(options_list, threads=threads, runtype=\\\"cp\\\")\\n\\n self.combine_gvcf(reference, output_file_list, output, input_is_sorted=input_is_sorted,\\n extension_list=extension_list,\\n tmp_dir=tmp_dir,\\n max_files_per_merging=max_files_per_merging, iteration=iteration+1)\",\n \"def main():\\n parser = argparse.ArgumentParser(description='MergeGVCFs and genotype them using the GATK')\\n parser.add_argument('-g', '--gatk', dest='gatk', help=\\\"Location of the GATK\\\", required=True)\\n parser.add_argument('-x', '--xmx', dest='xmx', help=\\\"Memory to use with JAVA\\\", required=True)\\n parser.add_argument('-c', '--cores', dest='cores', help=\\\"Number of cores to use\\\")\\n parser.add_argument('-o', '--output', dest='output', \\n help='Final output from the haplotype caller')\\n parser.add_argument('-r', '--reference', dest='reference', \\n help='Reference FASTA file')\\n parser.add_argument('-b','--bed', dest='bed_file',\\n help=\\\"Bed file for limiting the GATK\\\")\\n parser.add_argument('-p', '--ploidy', dest='ploidy', \\n help=\\\"Sample ploidy\\\", default=2)\\n parser.add_argument('-d', '--out_directory', dest='directory', help='Output director')\\n parser.add_argument('bams', nargs=\\\"*\\\", help='gVCF variant call files output from the GATK')\\n args = parser.parse_args()\\n args.cores = int(args.cores)\\n args.xmx = args.xmx.strip('\\\"')\\n print args.bams\\n genovcfs = haplotype_caller(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\\n bams=args.bams, reference=args.reference,\\n out_directory=args.directory, ploidy=args.ploidy, bed_file=args.bed_file)\\n outputs = merge_gvcfs(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\\n gvcfs=genovcfs, reference=args.reference)\\n genotype_gvcfs(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\\n inputs=outputs, output=args.output, reference=args.reference,bed_file=args.bed_file)\\n #haplotype_single(gatk=args.gatk, xmx=args.xmx, cores=args.cores,\\n # inputs=args.gvcfs, reference=args.reference)\",\n \"def main():\\n\\n # this will analyze all files in the input_files directory\\n for folder in [x for x in os.listdir(os.path.join(os.getcwd(), 'test_directory')) if os.path.isdir(os.path.join(os.getcwd(), 'test_directory', x))]:\\n try:\\n print(f'Creating GED_Repo for files in {folder}')\\n g = GED_Repo([os.path.join(os.getcwd(), 'test_directory', folder, f) for f in os.listdir(os.path.join(os.getcwd(), 'test_directory', folder)) if f.endswith('.ged')])\\n g.check_data()\\n g.print_data()\\n g.print_individuals()\\n g.print_families()\\n except ValueError as v:\\n print(v)\\n except FileNotFoundError as f:\\n print(f)\",\n \"def main():\\n\\n file_list = []\\n # this will analyze all files in the input_files directory\\n for folder in [x for x in os.listdir(os.path.join(os.getcwd(), 'test_directory')) if os.path.isdir(os.path.join(os.getcwd(), 'test_directory', x))]:\\n try:\\n # print(f'Reading files in {folder}')\\n file_list = file_list + [os.path.join(os.getcwd(), 'test_directory', folder, f) for f in os.listdir(os.path.join(os.getcwd(), 'test_directory', folder)) if f.endswith('.ged')]\\n except ValueError as v:\\n print(v)\\n except FileNotFoundError as f:\\n print(f)\\n\\n try:\\n print(f'Analyzing final cumulative file data.')\\n # print(file_list)\\n g = GED_Repo(file_list)\\n g.check_data()\\n g.print_data()\\n g.print_individuals()\\n g.print_families()\\n except ValueError as v:\\n print(v)\\n except FileNotFoundError as f:\\n print(f)\",\n \"def run(self):\\n\\n # If the specified outdir doesn't exist, make it.\\n if os.path.exists(self.outdir) == False:\\n os.mkdir(self.outdir)\\n\\n # Get occurrence data.\\n self.get_gbif_occs()\",\n \"def combineAllGraphFiles(chroms, final_out):\\n outfile = open(final_out,'w');\\n outfile.close();\\n \\n for chrom in chroms:\\n graph_file = chrom + \\\".graph\\\";\\n try:\\n if os.system('%s %s >> %s' %\\n (cat, graph_file, final_out)): raise\\n except: sys.stderr.write(\\\"cat failed at %s\\\\n\\\" % chrom)\",\n \"def run_concat_vcfs(job, context, vcf_ids, tbi_ids):\\n\\n work_dir = job.fileStore.getLocalTempDir()\\n\\n vcf_names = ['chrom_{}.vcf.gz'.format(i) for i in range(len(vcf_ids))]\\n out_name = 'genome.vcf.gz'\\n\\n for vcf_id, tbi_id, vcf_name in zip(vcf_ids, tbi_ids, vcf_names):\\n job.fileStore.readGlobalFile(vcf_id, os.path.join(work_dir, vcf_name))\\n job.fileStore.readGlobalFile(tbi_id, os.path.join(work_dir, vcf_name + '.tbi'))\\n\\n cmd = ['bcftools', 'concat'] + [vcf_name for vcf_name in vcf_names] + ['-O', 'z']\\n \\n with open(os.path.join(work_dir, out_name), 'wb') as out_file:\\n context.runner.call(job, cmd, work_dir=work_dir, outfile = out_file)\\n\\n cmd = ['tabix', '-f', '-p', 'vcf', out_name]\\n context.runner.call(job, cmd, work_dir=work_dir)\\n\\n out_vcf_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name))\\n out_tbi_id = context.write_intermediate_file(job, os.path.join(work_dir, out_name + '.tbi'))\\n\\n return out_vcf_id, out_tbi_id\",\n \"def FeaturesGen(ChopChopresults, outputDir, sgRNA_type):\\n \\n #make output Directory if it does not already exist\\n if not os.path.isdir(outputDir):\\n os.makedirs(outputDir)\\n \\n #list the directory contents \\n for i,j,k in os.walk(ChopChopresults): #use walk to go through and find all directories\\n \\n if j == []: #no subdirectories\\n saveDF = pd.DataFrame() #initiate dataframe\\n for target in k: #loop through to find the sgRNA sequences\\n if target.endswith('.offtargets'):\\n with open(os.path.join(i,target), 'r+') as f:\\n guide = f.readlines()\\n #add them to a dataframe\\n temp = pd.Series()\\n temp['guideNo'] = target.split('.')[0] + sgRNA_type\\n temp['guideSeq'] = guide.pop(0).rstrip()\\n \\n saveDF = saveDF.append(temp.to_frame().transpose())\\n saveDF['type'] = 'sgRNA'\\n \\n if sgRNA_type == 'General' or sgRNA_type == None:\\n saveDF['fwd'] = 'pink'\\n saveDF['rev'] = 'green'\\n elif sgRNA_type == 'GG':\\n saveDF['fwd'] = 'yellow'\\n saveDF['rev'] = 'plum'\\n elif sgRNA_type == 'GA':\\n saveDF['fwd'] = 'cyan'\\n saveDF['rev'] = 'cornflower blue'\\n \\n \\n #save to txt file with tab delimiter\\n saveDF.to_csv(os.path.join(outputDir, os.path.basename(i) + '_features.txt'),\\\\\\n index = False, header = False, sep = '\\\\t')\\n \\n del saveDF\",\n \"def main():\\n\\n\\n\\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\\n\\n # fetch and sort the .mnc and .tag files\\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\\n tag_files = [f for f in skulls_folder if 'tag' in f]\\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\\n \\n mnc_files.sort()\\n tag_files.sort()\\n mnc_names.sort()\\n\\n # Process and package ndarrays as tuples inside npy file\\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\\n \\n print('\\\\n' * 5)\\n\\n # Push the npy files to GCP Cloud Storage\\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)\",\n \"def prepare_subset_vcf_files_by_population():\\n if not os.path.exists(VCF_BY_POPULATION_PATH):\\n print(\\\"preparing subset vcf by population\\\")\\n os.makedirs(VCF_BY_POPULATION_PATH)\\n sleep(10)\\n vcf_tools_runner = VCFToolsDockerRunner()\\n samples = glob(f\\\"{SAMPLES_FOLDER}/*.csv\\\")\\n\\n with tqdm(total=len(samples)) as pbar:\\n for sample in samples:\\n sample = sample.replace('\\\\\\\\', '/')\\n sample_name = get_filename_from_path(sample)\\n sample_path = \\\"/\\\".join([IMAGE_SHARE_FOLDER_PATH] + sample.split('/')[1:])\\n pbar.set_description(f\\\"Processing {sample_name}\\\")\\n vcf_tools_runner(\\n f\\\"vcf-subset -c {sample_path} \\\"\\n f\\\"{IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_VCF_FILE_NAME} | fill-an-ac > \\\"\\n f\\\"{IMAGE_SHARE_FOLDER_PATH}/{VCF_BY_POPULATION_FOLDER}/{sample_name}.vcf\\\")\\n pbar.update(1)\\n else:\\n print(f\\\"Subset VCF files by population already exist in: {VCF_BY_POPULATION_PATH}\\\")\",\n \"def main():\\n file_one_path, file_two_path, output_path =\\\\\\n get_command_line_arguments(\\n ['/home/ehler002/project/groups/go/Data/Cluster_Data/Dataset.txt',\\n '/home/ehler002/project/groups/go/Data/Cluster_Data/translated_genes.fpkm_table',\\n '/home/ehler002/project/groups/go/Data/Cluster_Data/Full_fpkm_Table.txt'])\\n pattern = 'CRO_T'\\n for file_path in [file_one_path, file_two_path]:\\n assert os.path.exists(file_path), 'File %s does not exist.' % file_path\\n start_time = datetime.datetime.now()\\n print('Started concatenation at %s' % start_time)\\n file_contents, headers = get_file_contents(file_two_path)\\n file_contents = sort_file_contents(file_contents)\\n file_contents = remove_pattern(file_contents, pattern)\\n concatenate_files(file_one_path, file_contents, headers, output_path)\\n print('Finished concatenation in %s' % (datetime.datetime.now() - start_time))\",\n \"def process_cgc(path, return_dataframe=False, fusions=False):\\n # read in data\\n df = pd.read_table(path)\\n\\n # keep small somatic variants\\n if not fusions:\\n s = df['Mutation Types']\\n is_small = s.str.contains('Mis|F|N|S').fillna(False)\\n is_somatic = ~df['Tumour Types(Somatic)'].isnull()\\n df = df[is_small & is_somatic].copy()\\n\\n # label oncogenes / TSG\\n df['Is Oncogene (CGC)'] = 'No'\\n df.loc[df['Role in Cancer'].fillna('').str.contains('oncogene'), 'Is Oncogene'] = 'Yes'\\n df['Is Tumor Suppressor Gene (CGC)'] = 'No'\\n df.loc[df['Role in Cancer'].fillna('').str.contains('TSG'), 'Is Tumor Suppressor Gene'] = 'Yes'\\n df['Is Driver Gene (CGC)'] = 'Yes'\\n\\n # rename columns\\n df = df.rename(columns={'Entrez GeneId': 'Entrez Gene ID', 'Gene Symbol': 'Hugo Symbol'})\\n\\n # get gene names\\n if not return_dataframe:\\n cgc_genes = df['Gene Symbol'].tolist()\\n else:\\n cgc_genes = df\\n\\n return cgc_genes\\n else:\\n # return fusion gene information\\n has_fus_partner = ~df['Translocation Partner'].isnull()\\n output_list = []\\n for ix, row in df[has_fus_partner].iterrows():\\n g1 = row[\\\"Gene Symbol\\\"]\\n for g2 in row['Translocation Partner'].split(', '):\\n output_list.append([g1, g2])\\n output_df = pd.DataFrame(output_list, columns=[\\\"Gene1\\\", \\\"Gene2\\\"])\\n output_df['GENE_ID'] = output_df['Gene1'] + '--' + output_df['Gene2']\\n\\n if not return_dataframe:\\n cgc_genes = list(set(output_df[\\\"Gene1\\\"].unique()) | set(output_df[\\\"Gene2\\\"]))\\n else:\\n cgc_genes = output_df\\n\\n return cgc_genes\",\n \"def main():\\n\\n args = parseArgs()\\n\\n path = args.path\\n is_open_gl = args.g\\n\\n success, failure = genFiles(path, is_open_gl)\\n\\n print(\\\"Success: \\\", \\\", \\\".join(success))\\n print(\\\"Failure: \\\", \\\", \\\".join(failure))\\n\\n ratio = len(success) / (len(success) + len(failure))\\n\\n print(\\\"%% success = %.2f\\\" % (100 * ratio))\",\n \"def main():\\n\\n #Getthefiles\\n all_plasmid_path = []\\n path_to_all_info = '/Users/gustavotamasco/mdrkrp/project_MDR_KRP/all_vir_files'\\n dirpath=os.getcwd()\\n os.chdir(path_to_all_info)\\n files = list_files_simple(path_to_all_info)\\n\\n bad_files = [\\\"Hemo_536_vfdb_genome.tsv\\\", \\\"MI_119_vfdb.tsv\\\", \\\"Hemo_536_vfdb.tsv\\\",\\n \\\"MI_119_vfdb_genome.tsv\\\",\\n \\\"URO_775_vfdb_genome.tsv\\\", \\\"Hemo_825_vfdb.tsv\\\", \\\"URO_775_vfdb.tsv\\\",\\n \\\"Hemo_825_vfdb_genome.tsv\\\",\\n \\\"MI_329_vfdb.tsv\\\", \\\"MI_569_vfdb_genome.tsv\\\", \\\"MI_329_vfdb_genome.tsv\\\",\\n \\\"MI_569_vfdb.tsv\\\",\\n \\\"Hemo_989_vfdb_genome.tsv\\\", \\\"MI_78_vfdb.tsv\\\", \\\"Hemo_989_vfdb.tsv\\\",\\n \\\"MI_78_vfdb_genome.tsv\\\"]\\n\\n final_files = list([x for x in files if x not in bad_files])\\n print(len(final_files))\\n\\n\\n '''Building metadata'''\\n #All genes to each genome\\n metadata = {}\\n for file in final_files:\\n with open(file) as vir_info:\\n parse_genes_v2(file, vir_info, metadata)\\n\\n\\n #All genes that occured\\n all_genes = sorted(set(get_all_genes(metadata)))\\n print(all_genes)\\n\\n #All vir classess\\n\\n\\n '''Build dataframe for the classes plot'''\\n df_info = {}\\n df_major_classes = build_class_df(df_info, all_genes, metadata)\\n df = pd.DataFrame.from_dict(df_major_classes, orient='index', columns=['entA', 'entB', 'entE', 'entS', 'fepA', 'fepB', 'fepC', 'fepD', 'fepG', 'fimA', 'fimE', 'fyuA', 'irp1', 'irp2', 'mgtB', 'mgtC', 'ompA', 'xcpA/pilD', 'xcpR', 'yagV/ecpE', 'yagW/ecpD', 'yagX/ecpC', 'yagY/ecpB', 'yagZ/ecpA', 'ybtA', 'ybtE', 'ybtP', 'ybtQ', 'ybtS', 'ybtT', 'ybtU', 'ybtX', 'ykgK/ecpR'])\\n #df = df.transpose()\\n #df.to_csv('arg_genes.csv', sep='\\\\t', encoding='utf-8')\\n #sns.set(font_scale=0.65)\\n #Need both\\n #not_full = sns.clustermap(df, label='small', cmap=\\\"vlag\\\", standard_scale=1, linewidths=0)\\n full_plot = sns.clustermap(df, label='small', cmap=\\\"vlag\\\", linewidths=0)\\n #plt.title('Antibiotic resistance genes across 34 organism', fontsize=15)\\n #sns.set(font_scale=1)\\n plt.show()\\n full_plot.savefig(\\\"final_genome_plasmid_vir.pdf\\\", bbox_inches='tight')\\n #not_full.savefig(\\\"final_genome_plasmid_vir_scalled.pdf\\\", bbox_inches='tight')\",\n \"def GatherVcfs(\\n b: hb.Batch,\\n input_vcfs: List,\\n disk_size: int,\\n output_vcf_path: str = None,\\n) -> Job:\\n j = b.new_job('VQSR: FinalGatherVcf')\\n j.image(utils.GATK_IMAGE)\\n j.memory(f'16G')\\n j.storage(f'{disk_size}G')\\n j.declare_resource_group(\\n output_vcf={'vcf.gz': f'{NAME}_gathered.vcf.gz', 'vcf.gz.tbi': f'{NAME}_gathered.vcf.gz.tbi'}\\n )\\n\\n input_cmdl = ' '.join([f'--input {v}' for v in input_vcfs])\\n j.command(\\n f\\\"\\\"\\\"set -euo pipefail\\n # --ignore-safety-checks makes a big performance difference so we include it in \\n # our invocation. This argument disables expensive checks that the file headers \\n # contain the same set of genotyped samples and that files are in order \\n # by position of first record.\\n gatk --java-options -Xms6g \\\\\\\\\\n GatherVcfsCloud \\\\\\\\\\n --gather-type BLOCK \\\\\\\\\\n {input_cmdl} \\\\\\\\\\n --output {j.output_vcf['vcf.gz']}\\n tabix {j.output_vcf['vcf.gz']}\\\"\\\"\\\"\\n )\\n if output_vcf_path:\\n b.write_output(j.output_vcf, f'{output_vcf_path}{NAME}_gathered{LABEL}')\\n return j\",\n \"def generate_all(files, alignement_h5f, input_h5f,\\n nframes=7, vad=None):\\n def try_remove(fname):\\n try:\\n os.remove(fname)\\n except:\\n pass\\n try:\\n directory = os.path.dirname(os.path.abspath(input_h5f))\\n\\n # create temporary files:\\n _, fb_h5f = tempfile.mkstemp(dir=directory)\\n _, fb_mvn_h5f = tempfile.mkstemp(dir=directory)\\n os.remove(fb_h5f)\\n os.remove(fb_mvn_h5f)\\n\\n # generate mfccs:\\n h5features_compute(files, alignement_h5f, featfunc=do_mfccs)\\n\\n # generate stacked mvn fbanks:\\n h5features_compute(files, fb_h5f, featfunc=do_fbank)\\n mean_variance_normalisation(fb_h5f, fb_mvn_h5f, vad=vad)\\n h5features_feats2stackedfeats(fb_mvn_h5f, input_h5f, nframes=nframes)\\n finally:\\n try_remove(fb_h5f)\\n try_remove(fb_mvn_h5f)\",\n \"def main():\\n processSetOfCerFiles(sys.argv[1:])\",\n \"def main():\\n onlyfiles = [f for f in listdir(RAWDATA_PATH) if isfile(join(RAWDATA_PATH, f))]\\n for file in onlyfiles:\\n create_RCSB_fastas(file)\",\n \"def main():\\n nbin = 60\\n nbinM = 100\\n \\n maxmag = -20\\n minmag = -29\\n mag = np.linspace(minmag,maxmag,nbinM) \\n minz = 0.1\\n maxz = 5\\n z = np.linspace(minz,maxz,nbin)\\n \\n minv = 10**1.6\\n maxv = 10**2.6\\n vel = np.linspace(minv,maxv,nbin)\\n \\n # writing the script for galfic once and for all\\n gl.write_script()\\n\\n result = [[] for i in range(5)]\\n \\n for zl in z:\\n zrange = z[np.where(z > zl)]\\n for zs in zrange:\\n for v in vel:\\n gl.write_initfile(v,zl,zs)\\n os.system('./script_gl > /dev/null 2>&1')\\n sigma = gl.analyse_output(mag,zs,zl,v)\\n for i in range(nbinM):\\n result[0].append(zs)\\n result[1].append(zl)\\n result[2].append(v)\\n result[3].append(mag[i])\\n result[4].append(sigma[i])\\n\\n np.savez('crosssection.npz',x=result)\",\n \"def generate_files(self):\\n\\t\\tapply_stemmer, xml_file, query_file, expected_file = self.read_config_file()\\n\\t\\tself.generate_query_file(query_file, xml_file, apply_stemmer)\\n\\t\\tself.generate_expected_file(expected_file, xml_file)\\n\\t\\tlogging.info('FINALIZADO: MÓDULO PROCESSADOR DE CONSULTAS')\",\n \"def cleanup_sub(vg_dir):\\n\\tnew_dir = vg_dir+'_clean'\\n\\tos.mkdir(new_dir)\\n\\tphot_vg_files = filter(lambda x: '.txt' in x, os.listdir(vg_dir))\\n\\t# phot_vg_phottot_files = filter(lambda x: 'phottot' in x, phot_vg_files)\\n\\tfor f in phot_vg_files:\\n\\t\\tdf = pd.read_table(vg_dir+'/'+f,\\n\\t\\t\\tnames = ['id','ra','dec','flux','unc','x','y','flux_uncor'],\\n\\t\\t\\tdelim_whitespace=True)\\n\\t\\tstarnums, dithers = zip(*[i.split('_')[1:4:2] for i in df.id])\\n\\t\\tdf['id'] = [int(i) for i in starnums]\\n\\t\\tdf['dither'] = [int(i) for i in dithers]\\n\\t\\tsorted_df = df.sort(['id','dither'])\\n\\t\\t# new: remove the aperture correction applied by varoujan to the uncertainties\\n\\t\\tch = f.split('-')[2]\\n\\t\\tif ch == 'ch1':\\n\\t\\t\\tsorted_df['unc'] /= 1.205\\n\\t\\telif ch == 'ch2':\\n\\t\\t\\tsorted_df['unc'] /= 1.221\\n\\t\\telse:\\n\\t\\t\\traise(TypeError(\\\"unexpected channel\\\"))\\n\\t\\tfnew = '_'.join(f.split('-')[::2])+'_raw.csv'\\n\\t\\tsorted_df.to_csv(new_dir+'/'+fnew, index=False, float_format='%.8f')\\n\\t\\t# also calculate mean RA/Dec, flux, and quadrature sum uncertainty\\n\\t\\tgrouped = sorted_df.groupby('id')\\n\\t\\tagg = grouped[['ra','dec','flux']].aggregate(np.median)\\n\\t\\tquadsum = grouped['unc'].aggregate(lambda x: np.sqrt(np.sum(x**2)))\\n\\t\\tagg['unc'] = quadsum\\n\\t\\tfnew = '_'.join(f.split('-')[::2])+'_agg.csv'\\n\\t\\tagg.to_csv(new_dir+'/'+fnew, index=True, float_format='%.8f')\",\n \"def __load_cogs(self):\\n for cog in self.__cogs.get():\\n logging.info('loading %s', cog)\\n self.load_extension(cog)\",\n \"def features_from_folder(label_folder, audio_folder, output_folder):\\n print('Listing label files from folder.')\\n #scan labels folder\\n labels_list = os.listdir(label_folder)\\n label_files = []\\n for filename in labels_list:\\n #get its extension\\n file_extension = filename.split('.')[-1]\\n if file_extension != 'txt':\\n continue\\n #save to without its extension\\n label_files.append(filename[:-4])\\n\\n print('Listing audio files from folder.')\\n #scan audio folder\\n audios_list = os.listdir(audio_folder)\\n audio_files = []\\n for filename in audios_list:\\n #get its extension\\n file_extension = filename.split('.')[-1]\\n if file_extension != 'wav':\\n continue\\n #save to without its extension\\n audio_files.append(filename[:-4])\\n\\n print('Removing files without matches')\\n #use only the files with matching audio/label\\n files_to_process = []\\n for label_file in label_files:\\n if label_file in audio_files:\\n files_to_process.append(label_file)\\n\\n print('Processing each file...')\\n i = 1\\n class_count = {}\\n total_f = len(files_to_process)\\n #for each file\\n for processing in files_to_process:\\n print('File', str(i) + '/' + str(total_f))\\n i += 1\\n\\n #\\n label_file = os.path.join(label_folder, processing + \\\".txt\\\")\\n audio_file = os.path.join(audio_folder, processing + \\\".wav\\\")\\n\\n #get the segments from the corresponding label file\\n segments = get_segments(label_file)\\n\\n #\\n total_s = len(segments)\\n j = 1\\n #for each segment\\n for segment in segments:\\n print('\\\\tSegment', str(j) + '/' + str(total_s), segment['class'])\\n j += 1\\n\\n if class_count.get(segment['class']) is None:\\n class_count[segment['class']] = 1\\n else:\\n class_count[segment['class']] += 1\\n output_filename = segment['class']\\n output_filename += '-' + format(class_count[segment['class']], '04d')\\n output_filename = os.path.join(output_folder, output_filename)\\n\\n #get its features\\n segment_features = features_from_label(audio_file, segment)\\n\\n #save it to a file\\n fe.write_as_bin(output_filename, segment_features)\",\n \"def generate_megafile():\\n\\n print(\\\"\\\\nFetching testing dataset…\\\")\\n testing = get_testing()\\n\\n print(\\\"\\\\nFetching ECDC dataset…\\\")\\n ecdc = get_ecdc()\\n\\n location_mismatch = set(testing.location).difference(set(ecdc.location))\\n for loc in location_mismatch:\\n print(f\\\"<!> Location '{loc}' has testing data but is absent from ECDC data\\\")\\n\\n print(\\\"\\\\nFetching OxCGRT dataset…\\\")\\n cgrt = get_cgrt()\\n\\n all_covid = (\\n ecdc\\n .merge(testing, on=[\\\"date\\\", \\\"location\\\"], how=\\\"outer\\\")\\n .merge(cgrt, on=[\\\"date\\\", \\\"location\\\"], how=\\\"left\\\")\\n .sort_values([\\\"location\\\", \\\"date\\\"])\\n )\\n\\n # Add ISO codes\\n print(\\\"Adding ISO codes…\\\")\\n iso_codes = pd.read_csv(os.path.join(INPUT_DIR, \\\"iso/iso3166_1_alpha_3_codes.csv\\\"))\\n\\n missing_iso = set(all_covid.location).difference(set(iso_codes.location))\\n if len(missing_iso) > 0:\\n print(missing_iso)\\n raise Exception(\\\"Missing ISO code for some locations\\\")\\n\\n all_covid = iso_codes.merge(all_covid, on=\\\"location\\\")\\n\\n # Add continents\\n print(\\\"Adding continents…\\\")\\n continents = pd.read_csv(\\n os.path.join(INPUT_DIR, \\\"owid/continents.csv\\\"),\\n names=[\\\"_1\\\", \\\"iso_code\\\", \\\"_2\\\", \\\"continent\\\"],\\n usecols=[\\\"iso_code\\\", \\\"continent\\\"],\\n header=0\\n )\\n\\n all_covid = continents.merge(all_covid, on=\\\"iso_code\\\", how=\\\"right\\\")\\n\\n # Add macro variables\\n # - the key is the name of the variable of interest\\n # - the value is the path to the corresponding file\\n macro_variables = {\\n \\\"population\\\": \\\"un/population_2020.csv\\\",\\n \\\"population_density\\\": \\\"wb/population_density.csv\\\",\\n \\\"median_age\\\": \\\"un/median_age.csv\\\",\\n \\\"aged_65_older\\\": \\\"wb/aged_65_older.csv\\\",\\n \\\"aged_70_older\\\": \\\"un/aged_70_older.csv\\\",\\n \\\"gdp_per_capita\\\": \\\"wb/gdp_per_capita.csv\\\",\\n \\\"extreme_poverty\\\": \\\"wb/extreme_poverty.csv\\\",\\n \\\"cardiovasc_death_rate\\\": \\\"gbd/cardiovasc_death_rate.csv\\\",\\n \\\"diabetes_prevalence\\\": \\\"wb/diabetes_prevalence.csv\\\",\\n \\\"female_smokers\\\": \\\"wb/female_smokers.csv\\\",\\n \\\"male_smokers\\\": \\\"wb/male_smokers.csv\\\",\\n \\\"handwashing_facilities\\\": \\\"un/handwashing_facilities.csv\\\",\\n \\\"hospital_beds_per_thousand\\\": \\\"owid/hospital_beds.csv\\\",\\n \\\"life_expectancy\\\": \\\"owid/life_expectancy.csv\\\",\\n \\\"human_development_index\\\": \\\"un/human_development_index.csv\\\",\\n }\\n all_covid = add_macro_variables(all_covid, macro_variables)\\n\\n print(\\\"Writing to CSV…\\\")\\n all_covid.to_csv(os.path.join(DATA_DIR, \\\"owid-covid-data.csv\\\"), index=False)\\n\\n print(\\\"Writing to XLSX…\\\")\\n all_covid.to_excel(os.path.join(DATA_DIR, \\\"owid-covid-data.xlsx\\\"), index=False)\\n\\n print(\\\"Writing to JSON…\\\")\\n df_to_json(all_covid, os.path.join(DATA_DIR, \\\"owid-covid-data.json\\\"), macro_variables.keys())\\n\\n # Store the last updated time\\n timestamp_filename = os.path.join(DATA_DIR, \\\"owid-covid-data-last-updated-timestamp.txt\\\")\\n with open(timestamp_filename, \\\"w\\\") as timestamp_file:\\n timestamp_file.write(datetime.utcnow().replace(microsecond=0).isoformat())\\n\\n print(\\\"All done!\\\")\",\n \"def gci(path):\\n parents = os.listdir(path)\\n for parent in parents:\\n if parent == \\\"forgifs\\\" or parent == \\\"hilariousgifs\\\":\\n pass\\n else:\\n child = os.path.join(path,parent)\\n #print(child)\\n if os.path.isdir(child):\\n gci(child)\\n else:\\n filepath.append(child)\\n #print(child)\",\n \"def main():\\n args = get_args()\\n out_dir = args.outdir\\n pct_gc = args.pct_gc\\n\\n if not os.path.isdir(out_dir):\\n os.makedirs(out_dir)\\n\\n if not 0 < pct_gc <= 100:\\n die('--pct_gc \\\"{}\\\" must be between 0 and 100'.format(pct_gc))\\n\\n num_seqs = 0\\n for i, file in enumerate(args.fasta, start=1):\\n if not os.path.isfile(file):\\n warn('\\\"{}\\\" is not a file'.format(file))\\n continue\\n\\n print('{:3}: {}'.format(i, os.path.basename(file)))\\n\\n base, ext = os.path.splitext(os.path.basename(file))\\n high_file = os.path.join(out_dir, ''.join([base, '_high', ext]))\\n low_file = os.path.join(out_dir, ''.join([base, '_low', ext]))\\n\\n high_fh = open(high_file, 'wt')\\n low_fh = open(low_file, 'wt')\\n\\n for rec in SeqIO.parse(file, 'fasta'):\\n num_seqs += 1\\n bases = Counter(rec.seq.upper())\\n gc = bases.get('G', 0) + bases.get('C', 0)\\n pct = int((gc / len(rec.seq)) * 100)\\n SeqIO.write(rec, low_fh if pct < pct_gc else high_fh, 'fasta')\\n\\n print('Done, wrote {} sequence{} to out dir \\\"{}\\\"'.format(\\n num_seqs, '' if num_seqs == 1 else 's', out_dir))\",\n \"def main():\\n\\n\\t\\\"\\\"\\\"\\n\\tArguments:\\n\\t\\troot_directory: The absolute path to the root directory of the BIOS source code\\n\\t\\tsearch_directory: The absolute path to the directory from the code will start searching for .inf files\\n\\t\\\"\\\"\\\"\\n\\n\\t\\\"\\\"\\\"\\n\\tReturns:\\n\\t\\tBy the end of the execution of this program a new file called unused_components.txt\\n\\t\\tis created in root_directory\\n\\t\\\"\\\"\\\"\\n\\n\\tcurrent_directory = '';\\n\\troot_directory = '';\\n\\n\\tif len(sys.argv) < 2:\\n\\t\\tprint('Use: gc.py root_directory [search_directory]');\\n\\t\\tquit();\\n\\n\\troot_directory = str(sys.argv[1]);\\n\\n\\tif len(sys.argv) < 3:\\n\\t\\tsearch_directory = os.curdir;\\n\\telse:\\n\\t\\tsearch_directory = str(sys.argv[2]);\\n\\n\\tgc = GarbageCorrection()\\n\\tgc.detect_unused_elements(root_directory, search_directory);\",\n \"def gos_files_creation(annotation_file, go_namespace_studied):\\n go_ontology = pronto.Ontology('http://purl.obolibrary.org/obo/go/go-basic.obo')\\n\\n # For each GO terms look to the namespaces associated with them.\\n go_namespaces = {}\\n for go_term in go_ontology:\\n go_namespaces[go_term.id] = go_term.other['namespace'][0]\\n\\n # For each GO terms look if there is an alternative ID fo them.\\n go_alt_ids = {}\\n for go_term in go_ontology:\\n if 'alt_id' in go_term.other:\\n for go_alt in go_term.other['alt_id']:\\n go_alt_ids[go_alt] = go_term.id\\n\\n # Genome file with genes associated with GO terms.\\n df = pa.read_csv(annotation_file, sep='\\\\t', header=None)\\n df.columns = ['Gene_Name', 'GOs']\\n df.replace(np.nan, '', inplace=True)\\n\\n gos_in_df = []\\n for gos in df['GOs']:\\n for go in gos.split(','):\\n if go not in gos_in_df:\\n gos_in_df.append(go)\\n\\n df.set_index('Gene_Name', inplace=True)\\n\\n gene_gos = []\\n for gene, row in df.iterrows():\\n for go in row['GOs'].split(','):\\n gene_gos.append((go, gene))\\n\\n dic_go_genes = {}\\n for go in tqdm(gos_in_df):\\n genes = []\\n for gene_go in gene_gos:\\n if go != '' and go not in go_namespaces:\\n go = go_alt_ids[go]\\n if gene_go[0] == go and go != '' and go_namespaces[go] == go_namespace_studied:\\n genes.append(gene_go[1])\\n if go != '':\\n dic_go_genes[go] = genes\\n\\n print(len(dic_go_genes))\\n\\n delete_keys = []\\n for go in dic_go_genes:\\n if len(dic_go_genes[go]) < 4:\\n delete_keys.append(go)\\n\\n for key in delete_keys:\\n del dic_go_genes[key]\\n print(len(dic_go_genes))\\n\\n df_go = pa.DataFrame.from_dict(dic_go_genes, orient='index')\\n df_go.insert(0, 'Description', 'GO_terms')\\n\\n df_go.to_csv('go_gene.gmt', sep='\\\\t', header=False)\\n\\n df.reset_index(inplace=True)\\n df_query_go = pa.concat([pa.Series(row['Gene_Name'], row['GOs'].split(','))\\n for _, row in df.iterrows()]).reset_index()\\n df_query_go.columns = ['GOs', 'Gene_Name']\\n df_query_go = df_query_go[['Gene_Name', 'GOs']]\\n df_query_go.to_csv('query_go.tsv', sep='\\\\t', index=False)\",\n \"def main():\\n # checking the directory\\n cwd = os.getcwd()\\n print(f'The working directory: {cwd}')\\n # counting time \\n start_time = time.process_time()\\n # passing args\\n arg = parse_arguments()\\n sub_dir = arg.sub_dir\\n dir_out = arg.dir_out\\n file_amb = 'csv_to_clean'\\n names_ambigous = defaultdict(str)\\n with open(file_amb, 'r') as fh:\\n for line in fh:\\n name = line.strip().split('/')[2]\\n names_ambigous[name] = names_ambigous.get(name, '')\\n names_ambigous[name] += line.strip()\\n print(f'number files: {len(names_ambigous)}')\\n # checking if the output directory exist\\n # if not make it\\n f_pwd = os.path.join('Results', 'kmer_counts')\\n # get the genus names\\n cnt = 0\\n for name, filename in names_ambigous.items():\\n cleaned = get_csv_clean(filename)\\n full_path = os.path.join(f_pwd, name)\\n if os.path.exists(full_path):\\n print(f'The path {full_path} exist')\\n pass\\n else:\\n os.makedirs(full_path)\\n csv_name = f'{full_path}/{name}_k2_8_chr.csv'\\n print(f'Checking the full path {csv_name}')\\n with open(csv_name, 'w') as fout:\\n for km, cn in cleaned.items():\\n fout.write(f'{km},{cn}\\\\n')\\n cnt += 1\\n # get final time of the script\\n end = time.process_time()\\n total_time = end - start_time\\n print(f'The script takes {total_time} to finish!')\\n print(f'Where read and manipulated {cnt} files')\\n print('Done!')\",\n \"def searchforcog(sfc_cogs, sfc_twogs, sfc_prots):\\n cogupdate(sfc_cogs, sfc_twogs, sfc_prots)\\n newcogfind(sfc_cogs, sfc_twogs, sfc_prots)\\n newtwogfile(sfc_twogs)\",\n \"def main(input_folder, output_images_folder, output_files_folder, bb_file,\\n archive_folder, name_mapping):\\n\\n output_images_folder = Path(output_images_folder)\\n output_files_folder = Path(output_files_folder)\\n archive_folder = Path(archive_folder)\\n output_images_folder.mkdir(exist_ok=True)\\n archive_folder.mkdir(exist_ok=True)\\n logger.info(\\\"Converting Dicom to Nifty - START\\\")\\n converter = NiftiConverter(\\n padding=\\\"whole_image\\\",\\n resampling_spacing=-1,\\n list_labels=[\\\"GTVt\\\"],\\n cores=10,\\n )\\n _ = converter(input_folder, output_folder=output_images_folder)\\n\\n logger.info(\\\"Converting Dicom to Nifty - END\\\")\\n logger.info(\\\"Removing extra VOI - START\\\")\\n move_extra_vois(output_images_folder, archive_folder)\\n logger.info(\\\"Removing extra VOI - END\\\")\\n logger.info(\\\"Renaming files- START\\\")\\n correct_names(output_images_folder, name_mapping)\\n logger.info(\\\"Renaming files- END\\\")\\n logger.info(\\\"Cleaning the VOIs - START\\\")\\n clean_vois(output_images_folder)\\n logger.info(\\\"Cleaning the VOIs - END\\\")\\n\\n logger.info(\\\"Computing the bounding boxes - START\\\")\\n bb_df = compute_bbs(output_images_folder)\\n bb_df.to_csv(bb_file)\\n logger.info(\\\"Computing the bounding boxes - END\\\")\",\n \"def convert(self):\\n \\n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\\n splitAt = len(self.fullPath) + 1\\n \\n if len(vrtlist)!=0:\\n for i in range(0,len(vrtlist)):\\n prefix = str(vrtlist[i].split(\\\".vrt\\\")[0])\\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \\n prefix = prefix, subset = self.subset, res = self.resolution, \\n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\\n ct.run()\\n mosdel = glob.glob(self.fullPath + '/*mos.tif')\\n for f in mosdel:\\n os.remove(f)\\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \\n for f in xmldel:\\n os.remove(f)\\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\\n for f in vrtdel:\\n os.remove(f)\\n tifCount = len(glob.glob(self.fullPath + '/*.tif'))\\n dataCount = self.subset.count('1')\\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d mosaicked images were successfully converted to %d %s files.' % (dataCount, len(vrtlist), tifCount, str(self.outformat)))\\n \\n \\n if len(vrtlist)==0: \\n \\n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\\n for i in range(len(hdflist)):\\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i]], subset = self.subset, outformat = 'GTiff')\\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\\n\\n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\\n splitAt = len(self.fullPath) + 1\\n \\n for i in range(0,len(vrtlist)):\\n prefix = str(vrtlist[i].split(\\\".vrt\\\")[0])\\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \\n prefix = prefix, subset = self.subset, res = self.resolution, \\n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\\n ct.run()\\n \\n mosdel = glob.glob(self.fullPath + '/*mos.tif')\\n for f in mosdel:\\n os.remove(f)\\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \\n for f in xmldel:\\n os.remove(f)\\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\\n for f in vrtdel:\\n os.remove(f)\\n tifCount = len(glob.glob(self.fullPath + '/full*.tif'))\\n dataCount = self.subset.count('1')\\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d HDF files were successfully converted to %d %s files.' % (dataCount, len(hdflist), tifCount, str(self.outformat)))\",\n \"def getAllVCFs(inputFolder):\\n listOfFiles = []\\n numErrors = 0\\n for folder in os.listdir(inputFolder): #Loop through all folders\\n # print(folder)\\n try: # using a try-xcept block to avoid errors with other files \\n # not following this structure\\n # TODO: make this cleaner\\n vcfLoc = os.path.join(inputFolder, os.path.join(folder, \\\"pilon/\\\"))\\n # print(vcfLoc)\\n for potentialFile in os.listdir(vcfLoc):\\n # print(potentialFile)\\n if(potentialFile.endswith(\\\".vcf.gz\\\")):\\n listOfFiles.append(os.path.join(vcfLoc, potentialFile))\\n except:\\n # print(\\\"error at \\\" + folder)\\n numErrors += 1\\n print(numErrors)\\n return listOfFiles\",\n \"def main():\\n arg = parse_args()\\n print('Start.')\\n arg.folder = Path(arg.folder)\\n trees = list(arg.folder.glob('*'))\\n trees = [i.absolute() for i in trees]\\n info = parse_info(arg)\\n types = [arg.folder/i for i in info.keys()]\\n types_dict = dict(zip(info.keys(), types))\\n for i in types:\\n i.mkdir()\\n result = divide_trees(trees, info, types)\\n result_csv = arg.folder / 'result.csv'\\n with open(result_csv, 'w') as out:\\n out.write('Tree,Type,Confidence\\\\n')\\n for i in result:\\n out.write('{},{},{}\\\\n'.format(*i))\\n Path(types_dict[i[1]]/i[0].name).write_text(i[0].read_text())\\n print('Done.')\",\n \"def genImgVallCCD(filename=None,Nstar=None,seeing=[0.9,0.,0.],npix=None,zenith=0,filter='g', theta=0.,phi=0, corrector='corrector',x=0.,y=0.,z=0.,suband=None,regular=False, moffat=False):\\n hduList = pf.HDUList()\\n hdu = pf.PrimaryHDU(np.array([0]))\\n hdu.header.set('RAYPATT',raypattern)\\n hdu.header.set('NPIX',npix)\\n hdu.header.set('SCALE',scale)\\n hdu.header.set('FWHM',diffusionfwhm)\\n hdu.header.set('ZENITH',zenith)\\n hdu.header.set('FILTER',filter)\\n hdu.header.set('THETA',theta)\\n hdu.header.set('CORRT',corrector)\\n hdu.header.set('PHI',phi)\\n if x != None:\\n hdu.header.set('X',x)\\n if y != None:\\n hdu.header.set('Y',y)\\n if z != None:\\n hdu.header.set('Z',z)\\n if seeing != 0.:\\n hdu.header.set('s_fwhm',seeing[0])\\n hdu.header.set('e1',seeing[1])\\n hdu.header.set('e2',seeing[2])\\n hduList.append(hdu)\\n for ccd in N[1:]+S[1:]:\\n print ccd\\n res = genImgV(filename=filename,Nstar=Nstar,ccd=ccd,seeing=seeing,npix=npix,zenith=zenith,filter=filter, theta=theta, phi=phi,corrector=corrector,x=x,y=y,z=z,suband=suband,regular=regular,moffat=moffat)\\n hdu = pf.PrimaryHDU(res[0])\\n hdu.header.set('ccdPos',ccd[0])\\n hdu.header.set('ccdXcen',ccd[1])\\n hdu.header.set('ccdYcen',ccd[2])\\n hduList.append(hdu)\\n if filename != None:\\n if os.path.exists(filename):\\n os.system('rm '+filename)\\n hduList.writeto(filename)\\n else:\\n hduList.writeto(filename)\\n os.system('gzip '+filename)\\n return hduList\",\n \"def datamerge_run(filenames, outdir, roc_cols):\\n \\n tbldict = collect2dict(filenames, outdir)\\n tbldict = cogtest_manipulation(tbldict, roc_cols)\\n \\n #count number of tps\\n tbldict['cogtests'] = count_instances(tbldict['cogtests'], 'codeb', 'NP_NoTps')\\n tbldict['aseg_change'] = count_instances(tbldict['aseg_change'], 'codea', 'MRI_NoTps')\\n tbldict['pibparams'] = count_instances(tbldict['pibparams'], 'codea', 'PIB_NoTps')\\n \\n new_tbldict = {}\\n for key, tbl in tbldict.iteritems():\\n tpcol = [s for s in tbl.columns if ('_Tp' in s)]\\n if tpcol:\\n tpcol = tpcol[0]\\n tblflat, tblflatnm = flatten(tbl, tpcol, key, [1, '1'])\\n new_tbldict[tblflatnm] = tblflat\\n tbldict.update(new_tbldict)\\n \\n #make sure each table contains SubjID and BAC# fields\\n for key, tbl in tbldict.iteritems():\\n tbl = addcodes(tbl, tbldict['codetranslator'])\\n tbldict[key] = tbl\\n \\n #merge tables\\n tblstojoin = ['cogtests_flat','pibparams_flat','aseg_change_flat','fdg_metaroi_flat','subjinfo']\\n joincol = ['codea','codeb']\\n subjtbl = mergelots(tbldict, tblstojoin, joincol)\\n \\n #merge tables\\n tblstojoin = ['cogtests','subjinfo','pibparams_flat','aseg_change_flat','fdg_metaroi_flat']\\n joincol = ['codea','codeb']\\n NPtbl = mergelots(tbldict, tblstojoin, joincol)\\n \\n cf.save_xls_and_pkl(subjtbl, 'subjtbl', outdir)\\n cf.save_xls_and_pkl(NPtbl, 'NPtbl', outdir)\\n \\n return tbldict, NPtbl, subjtbl\",\n \"def gtiff(location):\\n\\n for src_asc in os.listdir(location):\\n if \\\".asc\\\" in src_asc:\\n dest_gtif = src_asc[:-4] + '.gtiff'\\n command = \\\"gdal_translate -of GTiff -ot Float32 \\\" + location +'\\\\\\\\' \\\\\\n + src_asc + ' ' + location +'\\\\\\\\' + dest_gtif\\n os.system(command)\",\n \"def main():\\n print('Calculating gas compositions.')\\n resf = 'results/cell_gas' # results folder\\n if not os.path.isdir(resf):\\n os.makedirs(resf)\\n sizex = 0.03 # sample size\\n sizey = 0.02 # sample size\\n sizez = 0.02 # sample size\\n volume = sizex**3 # sample volume\\n volume = sizex * sizey * sizez # sample volume\\n # polymer density\\n rhop = INPUTS['polymer_density']\\n # molecular weight\\n mw_ba = INPUTS['molar_mass']\\n # foam density\\n rhof = INPUTS['foam_density']\\n # cell size for Kelvin effect on saturated vapour pressure\\n dcell = INPUTS['cell_size']\\n # initial weight fraction of BA\\n w_ba_ini = INPUTS['initial_weight_fraction']\\n names = w_ba_ini.keys()\\n if 'H2O' in w_ba_ini:\\n if 'CO2' in w_ba_ini:\\n print(\\\"WARNING: H2O and CO2 are both in initial_weight_fraction.\\\",\\n \\\"We will sum these contributions.\\\")\\n else:\\n w_ba_ini['CO2'] = 0\\n w_ba_ini['CO2'] += w_ba_ini['H2O'] * mw_ba['CO2'] / mw_ba['H2O']\\n names.append('CO2')\\n names.remove('H2O')\\n temps = linspace(\\n INPUTS['temperature']['min'],\\n INPUTS['temperature']['max'],\\n INPUTS['temperature']['points']\\n )\\n por = 1 - rhof / rhop # porosity\\n m_foam = rhof * volume # foam sample weight\\n m_pol = m_foam * (1 - sum(w_ba_ini.values())) # weight of polymer\\n if ARGS['--verbose']:\\n print('Foam weight {0:.3f} g'.format(m_foam * 1e3))\\n args = [por, mw_ba, m_foam, m_pol, volume, dcell]\\n for name in names:\\n with open(os.path.join(resf, 'cell_gas_{0}.csv'.format(name)),\\n 'w') as csvfile:\\n fieldnames = ['temp', 'pres_ba', 'w_ba_g', 'w_ba_d', 'w_ba_c']\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n writer.writeheader()\\n for temp in temps:\\n pres_ba, w_ba_g, w_ba_d, w_ba_c = initial_pressure(\\n name, w_ba_ini[name], temp, args)\\n writer.writerow(\\n {'temp': temp, 'pres_ba': pres_ba, 'w_ba_g': w_ba_g,\\n 'w_ba_d': w_ba_d, 'w_ba_c': w_ba_c})\\n print('End.')\",\n \"def main(inCL=None):\\n headList = [] # Stores header of coronavirus sequences in fasta file\\n orfList = [] # Stores sequences containing ORFs of coronavirus sequences in fasta file\\n validNucs = ['A', 'C', 'G', 'T']\\n myReader = FastAreader('Combined-ALL-SARS-CoV.fasta') \\n for head, seq in myReader.readFasta(): # Using fastAreader to read in .fasta files\\n headList.append(head)\\n for i in seq:\\n if i not in validNucs: # Removing non-valid bases\\n seq = seq.replace(i,\\\"\\\") \\n orf = OrfFinder(seq, 300, True) # Includes the largest ORF greater than 300 nucleotides within a stop codon\\n geneOrfList = orf.getOrfs()\\n geneSeq = [] # Stores ORF sequences\\n for openFrame in geneOrfList:\\n geneSeq.append(seq[openFrame[1]-1:openFrame[2]-1])\\n orfList.append(geneSeq)\\n # Calls methods to create SeqRecords and then .py file to print gene trees\\n myPhylo = GeneTree() \\n for i in range(0,4,1): # Loops to print the first four gene trees of every sequence\\n records = myPhylo.geneSpecificRecord(orfList, headList, i) # Creates list of SeqRecords that represent a sequence\\n # alignments = myPhylo.fastaToPhylip(records) # Makes a .phy file using a .fasta file\\n print(\\\"GENE \\\" + str(i+1) + \\\":\\\")\\n # printTree = myPhylo.printGeneTree() # Prints Gene Trees\\n x = 0\\n print('\\\\n\\\\n============================================ K E Y ============================================\\\\n')\\n for header in headList: # Loops through headers to print key\\n header = header.split(',')\\n header = header[0]\\n print(\\\"{} = {}\\\" .format(x, header)) # Prints each line containing the header\\n x += 1\",\n \"def merge_wrapper(processdir, basedir, starglob, superstarglob, calibrootglob, njobs=2, invert=False):\\n for glob in [starglob, superstarglob, calibrootglob]:\\n assert path.dirname(glob), \\\\\\n f\\\"Glob : {glob} should be/contain a subdirectory\\\"\\n\\n superstarGlobNew = get_glob_strings(superstarglob)\\n calibrootGlob1, calibrootGlob2 = get_glob_strings(calibrootglob)\\n superstardir = get_dir_from_glob(processdir, superstarglob)\\n calibdir = get_dir_from_glob(basedir, calibrootglob)\\n starglob = processdir + starglob\\n\\n # ssmcolfnames = converter(superstardir,\\n # globstr1=superstarGlobNew,\\n # globstr2=superstarGlobNew,\\n # njobs=42,\\n # mergecolsonly=True)\\n # yecho(\\\"SuperStarfiles done.\\\")\\n # tofiltercalibglob = converter(processdir,\\n # globstr1=calibrootGlob1,\\n # globstr2=calibrootGlob2,\\n # njobs=42,\\n # mergecolsonly=False)\\n # yecho(\\\"Extracting done.\\\")\\n tofiltercalibglob = \\\"./csv/*.csv\\\"\\n ssmcolfnames = glob_and_check(\\\"./superstar/mergecols/*.csv\\\")\\n\\n yecho(\\\"Removing events.\\\")\\n if njobs > 1:\\n splitcalib = split_by_dates(tofiltercalibglob)\\n splitstar = split_by_dates(starglob)\\n splitss = split_by_dates(ssmcolfnames)\\n # needs filename output\\n assert len(splitcalib) == len(splitstar) == len(splitss), \\\"only works the first time when no calibfiles got moved, for everything else this needs a new function with more logic\\\"\\n Parallel(n_jobs=njobs)\\\\\\n (delayed(single_remove_events)(calibglob, starglob, ssglob, njobs, invert)\\n for calibglob, starglob, ssglob in zip(splitcalib, splitstar, splitss))\\n # filteredFiles = [f for arr in filteredFiles for f in arr]\\n else:\\n check_telescope_files(rootdir=None, globstr1=ssmcolfnames,\\n globstr2=calibmcolfnames, replacer=(\\\"_Y_\\\", \\\"_I_\\\"))\\n remover = EventRemover(tofiltercalibglob=tofiltercalibglob,\\n starglob=starglob,\\n superstarmcolglob=ssmcolfnames)\\n remover.remove_events()\\n filteredFiles = remover.outfilenames\\n yecho(\\\"Removed events that get thrown out during image cleaning and superstar processing and wrote the merged runs to:\\\")\\n yecho(f\\\"{path.basename(filteredFiles[0])}\\\")\\n # return filteredFiles\",\n \"def main():\\n glob_pattern = \\\"{root}/{child}/*.xml\\\".format(root=MANCHESTER_ROOT, child=TARGET_CHILD)\\n corpus_files = glob(glob_pattern)\\n for filename in corpus_files:\\n print(filename)\\n to_csv(filtered_parent_freq_count([filename], 2))\",\n \"def _main_extract_CDS(args = None, stdout = None, stderr = None,\\n gb_record_fmtdict = None,\\n gb_cds_fmtdict = None) :\\n if stdout is None :\\n stdout = sys.stdout\\n if stderr is None :\\n stderr = sys.stderr\\n if gb_record_fmtdict is None :\\n gb_record_fmtdict = _GB_RECORD_FMTDICT\\n if gb_cds_fmtdict is None :\\n gb_cds_fmtdict = _GB_CDS_FMTDICT\\n # Process arguments\\n if args is None :\\n parser = _makeParser_extract_CDS()\\n args = parser.parse_args()\\n args = _processArgsToLogic_extract_CDS(args, stdout, stderr,\\n gb_record_fmtdict, gb_cds_fmtdict)\\n # Go through the input files\\n uniqueSeq = dict()\\n i_file = 0\\n for fi in args.genbank_records :\\n i_file += 1\\n if args.verbose :\\n stderr.write(time.asctime() + \\\" - \\\" +\\n \\\"Processing file \\\" + str(i_file) + \\\" : \\\" +\\n os.path.basename(fi) + \\\" - \\\" +\\n \\\"N unique seq : \\\" + str(len(uniqueSeq.keys())) + \\\"\\\\n\\\")\\n record = SeqIO.parse(fi, \\\"genbank\\\")\\n for r in record :\\n if not args.actionFlags.get(\\\"DoCount\\\", False) :\\n (summaryString, uniqueSeq, newSeq) = (\\n _summarizeRecord(r, args.outfmt, args.hash, uniqueSeq))\\n stdout.write(summaryString)\\n else :\\n count = len([x for x in r.features if x.type == \\\"CDS\\\"])\\n stdout.write(r.annotations[\\\"gi\\\"] + \\\"\\\\t\\\" + str(count) + \\\"\\\\n\\\")\\n # Write unique sequences\\n if args.actionFlags.get(\\\"DoUniqueSequences\\\", False) :\\n with open(args.unique, \\\"w\\\") as fo :\\n for (k, v) in uniqueSeq.items() :\\n fo.write(\\\">\\\" + k + \\\"\\\\n\\\")\\n fo.write(v + \\\"\\\\n\\\")\",\n \"def b_cosmic(folder_name, PATH, CCD):\\n\\n list_files = search_files(location=folder_name, keyword='*.fits')\\n # print list_files\\n\\n obj_list, obj_l_gr7, obj_l_gr8, pas_list = list_object(list_files, PATH)\\n flat_list, f_l_gr7, flat_l_gr8, pas_list = list_flat(list_files, PATH)\\n # cosmic_curr_list = list(set(obj_list).union(flat_list))\\n # file which needed to correct for cosmic ray\\n\\n cosmic_curr_list = obj_list # file which needed to correct for cosmic ray\\n cosmic_curr_list_flats = flat_list\\n print(len(cosmic_curr_list))\\n write_list(file_list=cosmic_curr_list, file_name='cosmic_curr_list',\\n location=PATH)\\n\\n cr_check_list = cosmic_correction(cosmic_curr_list_flats, location=PATH)\\n for file in cr_check_list:\\n remove_file(str(file))\\n\\n # cosmic-ray correction manually for individual files\\n # or all files automatically\\n message = \\\"How do you like to proceed Cosmic ray correction?\\\"\\n choices = ['Default', 'Manually']\\n input = options(message, choices)\\n\\n if input.lower() == 'manually':\\n cr_check_list = cosmic_correction_individual(cosmic_curr_list,\\n CCD=CCD, location=PATH)\\n else:\\n cr_check_list = cosmic_correction_batch(cosmic_curr_list, CCD=CCD,\\n location=PATH)\\n print(len(cr_check_list))\\n # Stop running code for checking the cosmic ray corrected files\\n message = \\\"\\\"\\\"Cosmic ray correction is done.\\nDo you want to check chk files then continue?\\\"\\\"\\\"\\n choices = ['Yes', 'No']\\n value = options(message, choices)\\n\\n if value == \\\"Yes\\\":\\n display_co(image_list=cosmic_curr_list, location=PATH)\\n # for file in cr_check_list:\\n # remove_file(str(file))\\n elif value == \\\"No\\\":\\n pass\\n # for file in cr_check_list:\\n # remove_file(str(file))\",\n \"def get_result_files(self):\\n name_pattern = \\\"{mapper}.{ngs_library.name}\\\"\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"out\\\", name_pattern + \\\"{ext}\\\"), ext=EXT_VALUES\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"log\\\", \\\"{mapper}.{ngs_library.name}.{ext}\\\"),\\n ext=(\\n \\\"log\\\",\\n \\\"conda_info.txt\\\",\\n \\\"conda_list.txt\\\",\\n \\\"log.md5\\\",\\n \\\"conda_info.txt.md5\\\",\\n \\\"conda_list.txt.md5\\\",\\n ),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.{report}.txt\\\"\\n ),\\n report=(\\\"bamstats\\\", \\\"flagstats\\\", \\\"idxstats\\\"),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.{report}.txt.md5\\\"\\n ),\\n report=(\\\"bamstats\\\", \\\"flagstats\\\", \\\"idxstats\\\"),\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.bamstats.html\\\"\\n )\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"bam_qc\\\", name_pattern + \\\".bam.bamstats.html.md5\\\"\\n )\\n )\\n\\n for sheet in self.shortcut_sheets:\\n for ngs_library in sheet.all_ngs_libraries:\\n if ngs_library.name in self.ngs_library_to_kit:\\n extraction_type = ngs_library.test_sample.extra_infos[\\\"extractionType\\\"]\\n suffix = (\\n \\\"_long\\\"\\n if ngs_library.extra_infos[\\\"seqPlatform\\\"] in (\\\"PacBio\\\", \\\"ONP\\\")\\n else \\\"\\\"\\n )\\n # Per-sample target coverage report.\\n yield from expand(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"cov_qc\\\", name_pattern + \\\".{ext}\\\"\\n ),\\n mapper=self.config[\\\"tools\\\"][extraction_type.lower() + suffix],\\n ngs_library=[ngs_library],\\n ext=[\\\"txt\\\", \\\"txt.md5\\\"],\\n )\\n yield \\\"output/target_cov_report/out/target_cov_report.txt\\\"\\n yield \\\"output/target_cov_report/out/target_cov_report.txt.md5\\\"\\n if (\\n self.config[\\\"picard_hs_metrics\\\"][\\\"path_targets_interval_list\\\"]\\n and self.config[\\\"picard_hs_metrics\\\"][\\\"path_baits_interval_list\\\"]\\n ):\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"picard_hs_metrics\\\", name_pattern + \\\".txt\\\"\\n )\\n )\\n yield from self._yield_result_files(\\n os.path.join(\\n \\\"output\\\", name_pattern, \\\"report\\\", \\\"picard_hs_metrics\\\", name_pattern + \\\".txt.md5\\\"\\n )\\n )\\n if self.config[\\\"compute_coverage_bed\\\"]:\\n yield from self._yield_result_files(\\n os.path.join(\\\"output\\\", name_pattern, \\\"report\\\", \\\"coverage\\\", name_pattern + \\\"{ext}\\\"),\\n ext=(\\\".bed.gz\\\", \\\".bed.gz.tbi\\\"),\\n )\\n else:\\n print(\\n \\\"Genome-wide coverage BED generation disabled\\\", file=sys.stderr\\n ) # pragma: no cover\",\n \"def dir_resolution(self, src_path, frag_length=128):\\n src_path = os.path.join(self.root_path, src_path)\\n files = os.listdir(src_path)\\n\\n MFCCs = None\\n labels = None\\n cnt = 1\\n total_num = len(files)\\n for wav in files:\\n wav_path = os.path.join(src_path, wav)\\n MFCCs_each, labels_each = self.features_and_labels(wav_path, frag_length)\\n if MFCCs is not None:\\n MFCCs = torch.cat((MFCCs, MFCCs_each))\\n labels = torch.cat((labels, labels_each))\\n else:\\n MFCCs, labels = MFCCs_each, labels_each\\n\\n if cnt % 1000 == 0:\\n print('{} data pieces have been loaded in and {} are left'.format(cnt, total_num-cnt))\\n cnt += 1\\n\\n np.save(self.feature_file, MFCCs.numpy()) \\n np.save(self.label_file, labels.numpy())\\n print('Loading into files finished!')\",\n \"def create_merged_genotype_file(snps_file_path):\\n print(\\\"creating merged genotype file\\\")\\n plink_runner = Plink2DockerRunner()\\n shutil.copyfile(snps_file_path, f\\\"{GENOTYPE_DATA_PATH}/{SNP_LIST_FILE_NAME}\\\")\\n plink_runner(f\\\"./plink2 --pfile {IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_GENOTYPE_FILE} vzs \\\"\\n f\\\"--extract {IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{SNP_LIST_FILE_NAME} --export vcf \\\"\\n f\\\"--out {IMAGE_SHARE_FOLDER_PATH}/{GENOTYPE_DATA_FOLDER}/{MERGED_GENOTYPE_FILE}\\\")\",\n \"def split_per(folderin, folderout, split_col='ECO_ID', colNms=['i_h100','i_cd',\\n 'doy','i_wflen','i_acqdate','b1','vcf','ECO_NAME','ECO_ID','BIOME','geometry']):\\n\\n split_files = glob.glob(folderin + '*.shp')\\n\\n for filename in split_files:\\n print(filename)\\n basename = os.path.splitext(os.path.basename(filename))[0]\\n dfa = gpd.read_file(filename)\\n df = dfa.astype({split_col: 'int32'}) \\n ecoNames = list(np.unique(df[split_col]))#get list of unique ecoregions \\n \\n for eco in ecoNames:\\n #create new df with just columns I want\\n df2 = gpd.GeoDataFrame(df, columns=colNms)\\n ID = str(eco)\\n df_eco = df.loc[df2[split_col]==eco, colNms]\\n df_eco.to_file(folderout + '/{}_eco_{}.shp'.format(basename, ID))\",\n \"def __concatonate_files_controller(self):\\n\\n # find all barcode file paths\\n barcode_directories = []\\n for root, directory, files in os.walk(self.input_directory):\\n for name in directory:\\n barcode_directories.append( os.path.join(root, name) )\\n\\n # iterate through each barcode directory, item is the file path\\n for item in barcode_directories:\\n file = os.listdir(item)[0]\\n path = item\\n\\n new_file_name = self.__return_new_file_name(file_name=file, file_path=path)\\n self.__concatonate_files(new_file_name=new_file_name, parent_folder=path)\\n self.__write_logs_to_file(new_file_name)\",\n \"def rawvc_gatk_genotype_gvcfs_input(wildcards):\\n ref = cfg['db']['ref']\\n db = \\\"{results}/{group}/rawvc/gatkhc/genomicsdb/{population}{dot}{region}.{target}.db\\\".format(**dict(wildcards))\\n faext = wildcards_or(ext[\\\"fa\\\"])\\n d = re.sub(faext, \\\".dict\\\", ref)\\n targets = os.path.join(\\n os.path.dirname(ref), \\\"gatkhc\\\", f\\\"{wildcards.region}.{wildcards.target}.bed\\\")\\n return {'ref': ref, 'db': db, 'dict': d, 'targets': targets}\",\n \"def TextureFiles():\\n import shutil\\n\\n # first convert the .psd files to .png\\n\\n FbmDir = glo.outputFolder + '.fbm'\\n\\n for d1, d2, filenames in os.walk(FbmDir):\\n for filename in filenames:\\n \\\"\\\"\\\"filename: vitrin_diffuse.psd\\n \\\"\\\"\\\"\\n # print \\\"TextureFiles():\\\", filename\\n if filename[-4:].upper() == '.PSD':\\n #print \\\" -- FbmDir:\\\" , FbmDir\\n #print \\\" -- in the if clause with filename:\\\" , filename\\n #print \\\" -- glo.outputFolder\\\" , glo.outputFolder\\n # FbmDir = '../fbx/simplelifeembedmedia.fbm'\\n # filename = 'shelves_light.PSD'\\n PsdToPngConverter(FbmDir, filename)\\n\\n # Move only the .png file to the ../png/ directory\\n filename = filename[:-4] + '.png'\\n src = os.path.join(FbmDir, filename)\\n elif filename[0] != '.':\\n src = os.path.join(FbmDir, filename)\\n pass\\n\\n shutil.copy(src, glo.outputFolder)\\n print os.path.join(glo.outputFolder, filename), \\\"\\\\n\\\"\\n sys.stdout.flush()\\n # for d1, d2, files in os.walk(glo.outputFolder):\\n # if not filename in files:\\n # #print \\\"moving: \\\", files, filename, not filename in files\\n # shutil.copy(src, glo.outputFolder)\\n # print os.path.join(glo.outputFolder, filename), \\\"\\\\n\\\"\\n # else:\\n # print \\\"%s/%s already exists. File not moved\\\" % (glo.outputFolder,filename)\",\n \"def main(data_dir):\\n\\n face2face_dir = '{}/manipulated_sequences/Face2Face/c0/videos'.format(data_dir)\\n orig_dir = '{}/original_sequences/c0/videos'.format(data_dir)\\n base_dir = '{}/manipulated_sequences/GANnotation'.format(data_dir)\\n output_enc_dir = '{}/encodings'.format(base_dir)\\n output_vid_dir = '{}/{}/videos'.format(base_dir, COMPRESSION_LEVEL)\\n\\n pairs = get_seq_combos(face2face_dir)\\n\\n # Compute all video encodings and save them to disk.\\n # We precompute these because they take roughly 10 times as long to compute\\n # as the reenactments, and we may want to recompute the reenactments with\\n # different images later.\\n print('Computing video encodings...')\\n if not os.path.exists(output_enc_dir):\\n os.makedirs(output_enc_dir)\\n enc_count = 0\\n for source_id, _ in pairs:\\n encoding_path = get_encoding_path(output_enc_dir, source_id)\\n if os.path.exists(encoding_path):\\n continue # Encoding already calculated for this video sequence.\\n print('Computing encoding for sequence {}...'.format(source_id))\\n video_path = '{}/{}.mp4'.format(orig_dir, source_id)\\n cap = cv2.VideoCapture(video_path)\\n points = compute_video_encoding(cap)\\n cap.release()\\n try:\\n np.savetxt(encoding_path, points.reshape((132,-1)).transpose())\\n except KeyboardInterrupt as e:\\n # Safely handle premature termination.\\n # Remove unfinished file.\\n if os.exists(encoding_path):\\n os.remove(encoding_path)\\n raise e\\n enc_count += 1\\n\\n if enc_count == 0:\\n print('No encodings were calculated')\\n else:\\n print('{} video sequences encoded'.format(enc_count))\\n\\n print()\\n print('Computing reenactments...')\\n\\n # Load pre-trained model.\\n gann_path = os.path.join(dirname, 'models/myGEN.pth')\\n my_gann = GANnotation.GANnotation(path_to_model=gann_path)\\n\\n image_dir = '{}/original_sequences_images/{}/images'.format(data_dir, COMPRESSION_LEVEL)\\n if not os.path.exists(output_vid_dir):\\n os.makedirs(output_vid_dir)\\n reenact_count = 0\\n for source_id, driver_id in pairs:\\n output_path = '{}/{}_{}.mp4'.format(output_vid_dir, source_id, driver_id)\\n if os.path.exists(output_path):\\n # Do not recreate a video if it already exists.\\n # If the user wants to recreated a video\\n # the existing video must be deleted first.\\n continue\\n\\n print('Computing reenactment for {} onto {}...'.format(driver_id, source_id))\\n # Validate that input files exist.\\n encoding_path = get_encoding_path(output_enc_dir, driver_id)\\n if not os.path.isfile(encoding_path):\\n print('Failed to find encoding for video sequence {}'.format(driver_id),\\n file=stderr)\\n continue\\n image_path = '{}/{}.png'.format(image_dir, source_id)\\n if not os.path.isfile(image_path):\\n print('Failed to find image for sequence {}'.format(source_id),\\n file=stderr)\\n continue\\n\\n points = np.loadtxt(encoding_path).transpose().reshape(66, 2, -1)\\n\\n # Load and transform image for inputting.\\n image = cv2.imread(image_path)\\n cropped = get_gann_cropped_face(image)\\n\\n # Compute reenactment.\\n frames, _ = my_gann.reenactment(cropped, points)\\n\\n output_path = os.path.abspath(output_path)\\n print('Writing video to \\\"{}\\\"'.format(output_path))\\n try:\\n write_video(frames, FPS, (128, 128), output_path)\\n except KeyboardInterrupt as e:\\n # Safely handle premature termination.\\n # Remove unfinished file.\\n if os.exists(output_path):\\n os.remove(output_path)\\n raise e\\n reenact_count += 1\\n\\n if reenact_count == 0:\\n print('No reenactments were created')\\n else:\\n print('{} reenactments created'.format(reenact_count))\",\n \"def gtf_processing(genome=None, prefix='gencov'):\\n all_bed = prefix + \\\".all.bed\\\"\\n\\n if not os.path.exists(all_bed) or os.stat(all_bed).st_size == 0:\\n log.info(\\\"Preprocessing annotation...\\\")\\n features = ('exon', 'gene', 'intron', 'intergenic')\\n merged_exons, merged_genes = map(preprocess, features[:2])\\n ins = {\\n 'intron': [merged_genes, merged_exons],\\n 'intergenic': [merged_genes, genome]\\n }\\n intron_bed, intergenic_bed = map(preprocess, features[2:], [ins, ins])\\n\\n log.info(\\\"Concatenate bed files for all elements...\\\")\\n with open(all_bed, 'w') as out_bed:\\n cat_all(merged_exons, merged_genes, intron_bed, intergenic_bed, out_bed=out_bed)\\n\\n for f in (merged_exons, merged_genes, intron_bed, intergenic_bed):\\n os.remove(f)\\n\\n return all_bed\",\n \"def FileGen(TargetDir, extension = \\\".gbk\\\",subset_tag = \\\"final\\\"):\\n \\n for F in next(os.walk(TargetDir))[2]:\\n print(F)\\n if F[-1*len(extension):] == extension:\\n if F.split(\\\".\\\")[-2][:len(subset_tag)] == subset_tag:\\n yield(TargetDir + '/' + F)\",\n \"def generate_filename(self):\\n file_pattern = os.path.join(self.path, \\\"TCGA-*\\\")\\n for f in glob(file_pattern):\\n organ = get_organ(f)\\n for raw_f in glob(os.path.join(f, \\\"*.tif\\\")):\\n gt_f = raw_f.replace(\\\".tif\\\", \\\".xml\\\")\\n yield raw_f, gt_f, organ\",\n \"def generate_gazetteer(data_dir):\\n gaz_dir = os.path.join(data_dir, 'gazetteers/')\\n sources = ['full']\\n\\n # Cognitive Atlas gazetteer\\n weights = {'isSelf': 1,\\n 'isKindOf': 1,\\n 'inCategory': 1}\\n vocab_df = pd.read_csv(join(ec.utils.get_resource_path(), 'ontology',\\n 'unstemmed_cogat_vocabulary.csv'))\\n weight_df = pd.read_csv(join(ec.utils.get_resource_path(), 'ontology',\\n 'unstemmed_cogat_weights.csv'), index_col='id')\\n for source in sources:\\n text_folder = join(data_dir, 'text/cleaned_{0}/'.format(source))\\n out_dir = join(data_dir, 'text/cogat_cleaned_{0}/'.format(source))\\n count_df = ec.extract.extract_folder(text_folder, vocab_df, stem=False,\\n subs_folder=out_dir, abbrev=True)\\n count_df.to_csv(join(data_dir, 'features/cogat_counts_{0}.csv'.format(source)), index_label='pmid')\\n\\n weighted_df = ec.extract.expand(count_df, weight_df)\\n weighted_df.to_csv(join(data_dir, 'features/cogat_{0}.csv'.format(source)), index_label='pmid')\\n\\n print('Completed cogat extraction.')\",\n \"def main():\\n args = setup_args()\\n header_info = extract_header_info_from_probes(args.probe)\\n\\n for gene in header_info.keys():\\n # check there is a folder for gene, else create it\\n gene_out_dir = os.path.join(args.output_path, gene)\\n if not os.path.exists(gene_out_dir):\\n os.mkdir(gene_out_dir)\\n\\n gene_msa_fname = os.path.join(gene_out_dir, '{}_msa.fa'.format(gene))\\n gene_ref = os.path.join(os.path.abspath(args.gene_refs), gene + '.fa')\\n generate_msa_for_gene(gene, header_info[gene], gene_ref, gene_msa_fname)\",\n \"def gatherfiles(self):\\n\\t\\tfrom subprocess import Popen,PIPE\\n\\t\\timport os\\n\\t\\timport tarfile\\n\\t\\timport glob\\n\\t\\t\\n\\t\\tprint \\\"=== \\\",self.nameID,\\\": Joining all the files in one\\\"\\n\\t\\t# FIXME: Only there are 1 file, not needed the hadd\\n\\t\\tfinalfile = os.path.join(\\\"Results\\\",self.outputfile)\\n\\t\\t# FIXED BUG: just cp when there is only one file, otherwise\\n\\t\\t# there are problems with the TTree\\n\\t\\tif len(self.outputfiles) == 1:\\n\\t\\t\\t# Note that when there is only 1 file, always its #task=1\\n\\t\\t\\tcommand = [ 'cp', self.outputfiles[1], finalfile ]\\n\\t\\telse:\\n\\t\\t\\tcommand = [ 'haddPlus', finalfile ]\\n\\t\\t\\tfor f in self.outputfiles.itervalues():\\n\\t\\t\\t\\tcommand.append( f )\\n\\t\\tp = Popen( command ,stdout=PIPE,stderr=PIPE ).communicate()\\n\\t\\t# Checking if everything was allright\\n\\t\\ttotalevts = self.getevents(finalfile,True)\\n\\t\\tif totalevts != self.nevents:\\n\\t\\t\\tmessage = \\\"\\\\033[33;1mclustermanager.gatherfiles: WARNING\\\\033[0m the total file\\\"\\n\\t\\t\\tmessage += \\\"'\\\"+finalfile+\\\"' do not contain all the events:\\\\n\\\"\\n\\t\\t\\tmessage += \\\"Total events to be processed:\\\"+str(self.nevents)+\\\"\\\\n\\\"\\n\\t\\t\\tmessage += \\\"Total events in '\\\"+finalfile+\\\"':\\\"+str(totalevts)+\\\"\\\\n\\\"\\n\\t\\t\\tprint message\\n\\t\\t\\treturn \\n\\t\\t# If everything was fine, deleting the files \\n\\t\\t# and cleaning the directory\\n\\t\\tfor f in self.outputfiles.itervalues():\\n\\t\\t\\tos.remove( f )\\n\\t\\t# Taring and compressing\\n\\t\\tfilestotar = glob.glob(\\\"./*.*\\\")\\n\\t\\tfilestotar.append( \\\".storedmanager\\\")\\n\\t\\ttar = tarfile.open(os.path.basename(self.cwd)+\\\".tar.gz\\\",\\\"w:gz\\\")\\n\\t\\tfor f in filestotar:\\n\\t\\t\\ttar.add(f)\\n\\t\\ttar.close()\\n\\t\\t# if everything was fine, deleting the files\\n\\t\\tif os.path.exists(os.path.basename(self.cwd)+\\\".tar.gz\\\"):\\n\\t\\t\\tfor f in filestotar:\\n\\t\\t\\t\\tos.remove(f)\\n\\t\\telse:\\n\\t\\t\\tmessage = \\\"\\\\033[33;1mclustermanager.gatherfiles: WARNING\\\\033[0m I can't manage\\\\n\\\"\\n\\t\\t\\tmessage += \\\"to create the backup .tar.gz file\\\\n\\\"\\n\\t\\t\\tprint message\\n\\n\\t\\tprint \\\"Created \\\"+finalfile\\n\\t\\tprint \\\"========= Process Completed =========\\\"\",\n \"def run_pipeline(directory):\\n\\n # io = IO(path)\\n # df = io.load_cleaned_file(download_always=False)\\n # df = add_choke_events(df)\\n\\n # Add calls to features.Xxx here\\n\\n #directory = main_directory\\n site=os.listdir(directory)\\n site_dicom={}\\n site_dicom_sub={}\\n site_sub_files={}\\n i,k,j=0,0,0\\n for filename in site:\\n site_dicom[i]=directory+'/'+filename+'/DICOM-raw'\\n temporary_path=os.listdir(site_dicom[i])\\n\\n for another_file in temporary_path:\\n site_dicom_sub[j]=site_dicom[i]+'/'+another_file+'/scans'\\n temporary_path_1 = os.listdir(site_dicom_sub[j])\\n for another_file_1 in temporary_path_1:\\n site_sub_files[k]=site_dicom_sub[j]+'/'+another_file_1+'/'\\n k=k+1\\n j = j + 1\\n i=i+1\\n splitted={}\\n output_mif={}\\n for i in range (len(site_sub_files)):\\n splitted[i]=site_sub_files[i].split('/')\\n output_mif[i]=directory+'/'+splitted[i][5]+'/MIF-raw/'+splitted[i][5]+'_'+splitted[i][7]+'_'+splitted[i][9]+'.mif'\\n\\n\\n # save (or return) dataframe here?\\n return site_sub_files,output_mif\",\n \"def calculate_mixture_features(args):\\n workspace = args.workspace\\n speech_dir = args.speech_dir\\n noise_dir = args.noise_dir\\n data_type = args.data_type\\n fs = cfg.sample_rate\\n dir_name = args.dir_name\\n\\n fid_clean = open(speech_dir, 'r')\\n lines_clean = fid_clean.readlines()\\n fid_clean.close()\\n\\n fid_reverb = open(noise_dir, 'r')\\n lines_reverb = fid_reverb.readlines()\\n fid_reverb.close()\\n\\n for files_clean, files_reverb in zip(lines_clean, lines_reverb):\\n\\n files_clean = files_clean.strip('\\\\n')\\n files_reverb = files_reverb.strip('\\\\n')\\n\\n fid = open(files_clean,'r')\\n wavLines_clean = fid.readlines()\\n fid.close()\\n fid = open(files_reverb,'r')\\n wavLines_reverb = fid.readlines()\\n fid.close()\\n\\n cnt = 0 \\n\\n for wavs_clean, wavs_reverb in zip(wavLines_clean, wavLines_reverb):\\n \\n t1 = time.time()\\n # cnt = 0\\n\\n wav_name_clean, wav_path_clean = wavs_clean.split()\\n wav_name_reverb, wav_path_reverb = wavs_reverb.split()\\n \\n # Read clean speech audio. \\n (speech_audio, _) = read_audio(wav_path_clean, target_fs=fs)\\n \\n # Read reverb speech audio. \\n (noise_audio, _) = read_audio(wav_path_reverb, target_fs=fs)\\n \\n # Cut reverb speech to the same length as clean speech. \\n if len(noise_audio) > len(speech_audio):\\n noise_audio = noise_audio[0: len(speech_audio)]\\n \\n # Extract spectrogram. \\n mixed_complx_x = calc_sp(noise_audio, mode='complex')\\n speech_x = calc_sp(speech_audio, mode='magnitude')\\n\\n # Write out features. \\n out_feat_path = os.path.join(workspace, \\\"features\\\", \\\"spectrogram\\\", \\n data_type, dir_name, \\\"%s.p\\\" % wav_name_reverb)\\n create_folder(os.path.dirname(out_feat_path))\\n data = [mixed_complx_x, speech_x, wav_name_reverb]\\n pickle.dump(data, open(out_feat_path, 'wb'), protocol=pickle.HIGHEST_PROTOCOL)\\n \\n # Print. \\n if cnt % 100 == 0:\\n print(cnt)\\n # print(mixed_complx_x)\\n # print(speech_x)\\n \\n cnt += 1\\n\\n print(\\\"Extracting feature time: %s\\\" % (time.time() - t1))\",\n \"def _iterate_over_files(self):\\n stats = Statistics()\\n\\n args = arguments.Args()\\n\\n for file in args.files:\\n\\n if isimage(file):\\n before_size = stats.calculate_before_optimization(file)\\n\\n puts(\\\"%s %s\\\" % (\\n e(\\\"==>\\\"),\\n os.path.basename(file))\\n )\\n\\n if \\\"--lossy\\\" in args.flags:\\n Optimize.lossy(file)\\n if \\\"--lossless\\\" in args.flags:\\n Optimize.lossless(file)\\n after_size = stats.calculate_after_optimization(file)\\n\\n puts(\\\"%s %s (%s)\\\" % (\\n p(\\\"<==\\\"),\\n os.path.basename(file),\\n s(after_size) if after_size < before_size else after_size\\n ))\\n\\n stats.show_statistics()\",\n \"def gc(args):\\n p = OptionParser(gc.__doc__)\\n p.add_option(\\\"--binsize\\\", default=500, type=\\\"int\\\", help=\\\"Bin size to use\\\")\\n opts, args = p.parse_args(args)\\n\\n if len(args) != 1:\\n sys.exit(not p.print_help())\\n\\n (fastafile,) = args\\n binsize = opts.binsize\\n allbins = []\\n for name, seq in parse_fasta(fastafile):\\n for i in range(len(seq) / binsize):\\n atcnt = gccnt = 0\\n for c in seq[i * binsize : (i + 1) * binsize].upper():\\n if c in \\\"AT\\\":\\n atcnt += 1\\n elif c in \\\"GC\\\":\\n gccnt += 1\\n totalcnt = atcnt + gccnt\\n if totalcnt == 0:\\n continue\\n gcpct = gccnt * 100 / totalcnt\\n allbins.append(gcpct)\\n\\n from jcvi.graphics.base import asciiplot\\n from collections import Counter\\n\\n title = \\\"Total number of bins={}\\\".format(len(allbins))\\n c = Counter(allbins)\\n x, y = zip(*sorted(c.items()))\\n asciiplot(x, y, title=title)\",\n \"def multiple_files_analysis(lower_wavelength, upper_wavelength, CCD_height, CCD_width, CCD_height_corr, CCD_width_corr, file_paths, file_path_corr_data, progress_update):\\r\\n \\r\\n all_files_data = []\\r\\n FWHM_data = []\\r\\n central_energy_data = []\\r\\n counter = 1\\r\\n\\r\\n for file_path in file_paths:\\r\\n analysis = QDot_Spectroscopy(file_path=r\\\"{}\\\".format(file_path), meas_corr_curve_file_path=r\\\"{}\\\".format(file_path_corr_data), lower_wavelength=lower_wavelength, upper_wavelength=upper_wavelength, CCD_height=CCD_height, CCD_width=CCD_width, CCD_height_corr=CCD_height_corr , CCD_width_corr=CCD_width_corr)\\r\\n\\r\\n twod_map_raw = analysis.matrix_map(bkg_reduction=False, data_correction=False)\\r\\n twod_map_no_bkg = analysis.matrix_map(bkg_reduction=True, data_correction=False)\\r\\n twod_map_raw_corr = analysis.matrix_map(bkg_reduction=False, data_correction=True)\\r\\n twod_map_no_bkg_corr = analysis.matrix_map(bkg_reduction=True, data_correction=True)\\r\\n Q_Dot_plot_data, fit_statistics = analysis.gaussian_fit()\\r\\n\\r\\n file_analysis = [twod_map_raw, twod_map_no_bkg, twod_map_raw_corr, twod_map_no_bkg_corr, Q_Dot_plot_data, fit_statistics]\\r\\n all_files_data.append(file_analysis)\\r\\n\\r\\n # Creates a histogram from the collected FWHM and central energy data from all the analyzed datafales containing quantumdots\\r\\n for FWHM_value in fit_statistics['FWHM'].to_numpy():\\r\\n FWHM_data.append(FWHM_value)\\r\\n for CE_value in fit_statistics['Centre'].to_numpy():\\r\\n central_energy_data.append(CE_value)\\r\\n\\r\\n progress_update.emit(counter * 100/len(file_paths))\\r\\n counter += 1\\r\\n \\r\\n return all_files_data, FWHM_data, central_energy_data\",\n \"def rbp_cna(tumor_type, genelist):\\n subdir_list = os.listdir(tumor_type+\\\"/\\\")\\n if '.DS_Store' in subdir_list:\\n subdir_list.remove('.DS_Store')\\n for subfolder in subdir_list:\\n f = os.path.join(tumor_type, subfolder, \\\"data_CNA.txt\\\")\\n df = pd.read_table(f, sep=\\\"\\\\t\\\", index_col=0, low_memory=False)\\n df_rbp = df[df.index.isin(genelist)]\\n if df_rbp.columns[0] == \\\"Entrez_Gene_Id\\\":\\n df_rbp.drop(columns=\\\"Entrez_Gene_Id\\\", axis=0, inplace=True)\\n sample_num = df_rbp.shape[1]\\n new_folder = tumor_type + \\\"_CNA\\\"\\n if not os.path.exists(new_folder):\\n os.mkdir(new_folder)\\n outf = os.path.join(new_folder, subfolder + \\\"CNA.txt\\\")\\n df_rbp.to_csv(outf, sep=\\\"\\\\t\\\")\\n yield sample_num\",\n \"def main(): \\n \\n # parse command line arguments\\n parser = argparse.ArgumentParser(description='Runs variant calling on pileup file and stores in vfc file')\\n parser.add_argument('--use-read-quality', default=False, action='store_true',\\n help='tells the algorithm to estimate p from read qualities')\\n parser.add_argument('--call-less-positions', default=False, action='store_true',\\n help='tells the program to call less positions (not whole pileup file)')\\n parser.add_argument('--input-file', default='merged-normal.pileup', type=str,\\n help='path to input file in pileup format')\\n parser.add_argument('--output-file', default='Make name from input name', type=str,\\n help='name for the output vcf file. If not given, will be created from input file name')\\n parser.add_argument('--p', default='0.99', type=float,\\n help='probability estimate of one nucleotide read being correct, used by vc algorithm')\\n parser.add_argument('--positions-to-call', default='10000', type=int,\\n help='how many positions to call if call-less-positions set to true')\\n args = parser.parse_args()\\n if args.output_file == 'Make name from input name':\\n args.output_file = args.input_file + '.vcf'\\n \\n variant_caller = VariantCaller()\\n sample = 'SAMPLE1'\\n \\n # creates vcf file\\n create_vcf_start = time.time()\\n vcf = create_vcf_file(args.output_file, sample)\\n create_vcf_end = time.time()\\n print('Vcf header created. Elapsed time: {}'.format(create_vcf_end - create_vcf_start))\\n\\n main_loop_start = time.time()\\n position_count = 0\\n variant_caller_time = 0\\n positions_with_variants = 0\\n write_vcf_time = 0\\n\\n for pileup_line in pileup_reader(args.input_file):\\n # calls variant for each pileup line\\n variant_caller_start = time.time()\\n variant_caller.call_variant(pileup_line, args.p, args.use_read_quality)\\n if pileup_line['alts'] != '.':\\n positions_with_variants += 1\\n variant_caller_time += time.time() - variant_caller_start\\n\\n # writes line in VCF file\\n write_vcf_start = time.time()\\n write_vcf_line(pileup_line, vcf, sample)\\n write_vcf_time = time.time() - write_vcf_start\\n\\n position_count += 1\\n if args.call_less_positions and (position_count >= args.positions_to_call):\\n break\\n \\n main_loop_end = time.time()\\n total_running_time = main_loop_end - main_loop_start\\n\\n print('Processed {} positions. Found variants at {} positions.'.format(position_count, positions_with_variants))\\n\\n print('Total running time is {}'.format(total_running_time))\\n print('Pileup reader: {}'.format(total_running_time - variant_caller_time - write_vcf_time))\\n print('Variant calling: {}'.format(variant_caller_time))\\n print('Vcf writing: {}'.format(write_vcf_time))\",\n \"def collectInitialeccnStatistics_onefile(self, folder, databaseFilename, multiplicityFactor = 1.0, deformedNuclei = False):\\n typeCollections = ((1, 'sn'), (2,'en'))\\n for ecc_id, ecc_type_name in typeCollections:\\n db = SqliteDB(path.join(folder, databaseFilename % ecc_type_name))\\n # first write the ecc_id_lookup table, makes sure there is only one such table\\n if db.createTableIfNotExists(\\\"ecc_id_lookup\\\", ((\\\"ecc_id\\\",\\\"integer\\\"), (\\\"ecc_type_name\\\",\\\"text\\\"))):\\n db.insertIntoTable(\\\"ecc_id_lookup\\\", (ecc_id, ecc_type_name))\\n\\n # next create the eccentricities and collisionParameters table\\n db.createTableIfNotExists(\\\"eccentricities\\\", ((\\\"event_id\\\",\\\"integer\\\"), (\\\"ecc_id\\\", \\\"integer\\\"), (\\\"n\\\",\\\"integer\\\"), (\\\"ecc_real\\\",\\\"real\\\"), (\\\"ecc_imag\\\",\\\"real\\\")))\\n db.createTableIfNotExists(\\\"collisionParameters\\\", ((\\\"event_id\\\",\\\"integer\\\"), (\\\"Npart\\\", \\\"integer\\\"), (\\\"Ncoll\\\",\\\"integer\\\"), (\\\"b\\\",\\\"real\\\"), (\\\"total_entropy\\\",\\\"real\\\")))\\n if(deformedNuclei):\\n db.createTableIfNotExists(\\\"deformationParameters\\\", ((\\\"event_id\\\",\\\"integer\\\"), (\\\"cosTheta1\\\", \\\"real\\\"), (\\\"phi1\\\",\\\"real\\\"), (\\\"cosTheta2\\\",\\\"real\\\"), (\\\"phi2\\\",\\\"real\\\")))\\n\\n # the big loop\\n data = loadtxt(path.join(folder, '%s_ecc_eccp_10.dat' %(ecc_type_name)))\\n Npart = data[:, 36]\\n Ncoll = data[:, 37]\\n dSdy = data[:, 38]/multiplicityFactor #scale out the multiplicity factor used in superMC\\n b = data[:, 39]\\n for event_id in range(len(Npart)):\\n db.insertIntoTable(\\\"collisionParameters\\\", (event_id, int(Npart[event_id]), int(Ncoll[event_id]), float(b[event_id]), float(dSdy[event_id])))\\n if(deformedNuclei):\\n cosTheta1 = data[:, 40]\\n phi1 = data[:, 41]\\n cosTheta2 = data[:, 42]\\n phi2 = data[:, 43]\\n for event_id in range(len(Npart)):\\n db.insertIntoTable(\\\"deformationParameters\\\", (event_id, float(cosTheta1[event_id]), float(phi1[event_id]), float(cosTheta2[event_id]), float(phi2[event_id])))\\n for iorder in range(1,10):\\n eccReal = data[:, 4*iorder - 2]\\n eccImag = data[:, 4*iorder - 1]\\n for event_id in range(len(eccReal)):\\n db.insertIntoTable(\\\"eccentricities\\\",(event_id, ecc_id, iorder, float(eccReal[event_id]), float(eccImag[event_id])))\\n\\n # close connection to commit changes\\n db.closeConnection()\",\n \"def main():\\r\\n\\r\\n #Create a list of all files that have the GPX file format\\r\\n fileList = glob.glob(os.path.join(inFolder,\\\"*.{0}\\\".format(inFormat)))\\r\\n\\r\\n #Create a connection to PostGIS database\\r\\n pgConn = createPostgisConnection(dbFormat, dbHost, dbName, dbSchema, dbUser, dbPWD)\\r\\n\\r\\n #Process each *listed* layer type from a GPS file\\r\\n for f in fileList:\\r\\n importGPX(f, gpxImportLayers, pgConn)\",\n \"def setupGenotypeDirectories(self):\\n self.allGenosDir = \\\"%s/%s_all-genotypes\\\" % (os.getcwd(),\\n self.project2Id)\\n if not os.path.exists(self.allGenosDir):\\n os.mkdir(self.allGenosDir)\\n for genoId,iGeno in self.dGenos.items():\\n dirGeno = \\\"%s/%s\\\" % (self.allGenosDir, genoId)\\n iGeno.dir = dirGeno\\n if not os.path.exists(dirGeno):\\n os.mkdir(dirGeno)\\n if self.verbose > 0:\\n msg = \\\"genotype directories: %s\\\" % self.allGenosDir\\n print(msg); sys.stdout.flush()\",\n \"def cli(species, output, regions, cores, kmer):\\n kmer = int(kmer)\\n\\n count_composition = Counter()\\n\\n if regions is None:\\n group = [n.replace('.txt', '') for n in os.listdir(bgdata.get_path('datasets', 'genomereference', species))\\n if not n.startswith('.') and not n.startswith('chrM') and not n.startswith('chr23') and not n.startswith('chr24')]\\n \\n f = functools.partial(get_full_composition, species=species, kmer_len=kmer)\\n else:\\n group = read_file(regions)\\n f = functools.partial(get_composition, species=species, kmer_len=kmer)\\n\\n with Pool(int(cores)) as pool:\\n for d in tqdm(pool.imap_unordered(f, group), total=len(group)):\\n count_composition = count_composition + d\\n\\n with gzip.open(output, 'w') as fout:\\n fout.write(json.dumps(dict(count_composition)).encode('utf-8'))\",\n \"def main():\\n base_dir = '/home/sjimenez/imagenes_prueba'\\n out_dir = '/home/sjimenez/easy_analysis'\\n for _, _, files in os.walk(base_dir, topdown=False):\\n for f in files:\\n print('--------- {} ---------'.format(f))\\n act_dir = osp.join(base_dir, f)\\n act_im = cv2.imread(act_dir)\\n if act_im is not None:\\n get_image_stats(act_im, out_dir, f)\\n else:\\n print('Not able to open the image')\",\n \"def get_gbk_files(self):\\n\\n db_name = []\\n for file in os.listdir(os.getcwd()):\\n if file.endswith('.db'):\\n db_name.append(str(file))\\n\\n for G_key, G_value in self.what.tier_frame_dict.items():\\n Tier = G_key\\n os.chdir(self.path)\\n os.mkdir(Tier)\\n os.chdir(Tier)\\n Tier_path = os.getcwd()\\n for Gene in self.what.tier_frame_dict[Tier].T:\\n os.chdir(Tier_path)\\n os.mkdir(Gene)\\n os.chdir(Gene)\\n for Organism in self.what.org_list:\\n Accession = str(self.what.gene_dict[Gene][Organism])\\n Accession, Sup, Version = Accession.partition('.')\\n Accession = Accession.upper()\\n server_flag = False\\n for name in db_name:\\n if server_flag is True:\\n break\\n name = str(name)\\n server = BioSeqDatabase.open_database(\\n driver='sqlite3', db=where.VERT_MAM + ('/Databases/%s' % name))\\n for sub_db_name in server.keys():\\n db = server[sub_db_name]\\n\\n try:\\n record = db.lookup(accession=Accession)\\n with open('%s_%s.gbk' % (Gene, Organism), 'w') as GB_file:\\n GB_file.write(record.format('genbank'))\\n print(GB_file.name, 'created')\\n server_flag = True\\n break\\n except IndexError:\\n print('Index Error')\\n continue\",\n \"def gif_generation(orig_label_path, bound_data_path):\\n for sample in os.listdir(bound_data_path):\\n if not sample.startswith('.') and osp.isdir(osp.join(bound_data_path, sample)):\\n sample_path = osp.join(bound_data_path, sample)\\n for artery in os.listdir(sample_path):\\n orig_label_pick_path = osp.join(orig_label_path, sample, artery, 'data.pkl')\\n bound_pick_path = osp.join(bound_data_path, sample, artery, 'data.pkl')\\n\\n # function to save result of each artery into gif\\n save_gif_artery(orig_label_pick_path, bound_pick_path)\",\n \"def zip_imagenet100c():\\n #First make sure the directory we are given is correct!\\n if not os.path.isdir(DATA_SRC_ROOT):\\n raise Exception(\\\"Bad filepath given\\\")\\n\\n #create the destiantion directories if they don't exist\\n if not os.path.isdir(IMAGENET100_DIR):\\n os.mkdir(IMAGENET100_DIR)\\n\\n #grab the subset wnids for the 100 class-subset\\n with open(IMAGENET100_CLASSES) as f:\\n subset_wnids = f.readlines()\\n subset_wnids = [x.strip() for x in subset_wnids] #list of the 100 WNIDs we grab\\n\\n #Grab the names of all of the folders inside the root data source\\n #Structure is distortion/sub_distortion/level/wnids\\n for distortion in os.listdir(DATA_SRC_ROOT):\\n if distortion != \\\"meta.bin\\\":\\n print(distortion)\\n\\n folder_path = os.path.join(DATA_SRC_ROOT, distortion)\\n\\n if not os.path.isdir(folder_path):\\n continue\\n\\n for sub_distortion in os.listdir(folder_path):\\n print(sub_distortion)\\n\\n subfolder_path = os.path.join(folder_path, sub_distortion)\\n\\n if not os.path.isdir(subfolder_path):\\n continue\\n\\n for level in os.listdir(subfolder_path):\\n print(level)\\n\\n level_path = os.path.join(subfolder_path, level)\\n\\n #grab the correcrt validation d9recotires\\n for wnid in os.listdir(level_path):\\n wnid_path = os.path.join(level_path, wnid)\\n\\n if not os.path.isdir(wnid_path):\\n continue\\n\\n if wnid in subset_wnids:\\n dest_path = os.path.join(IMAGENET100_DIR, distortion, sub_distortion, level, wnid)\\n\\n shutil.copytree(wnid_path, dest_path)\\n\\n #copy the metadata bin file\\n meta_file = os.path.join(DATA_SRC_ROOT, 'meta.bin')\\n meta_dest = os.path.join(IMAGENET100_DIR, 'meta.bin')\\n\\n shutil.copy(meta_file, meta_dest)\\n\\n #Zip the destinatio file\\n shutil.make_archive(ZIP_PATH + '/ImageNet100C', 'tar', IMAGENET100_DIR)\",\n \"def processSetOfCerFiles(files):\\n printHeader()\\n \\n k = 0\\n for f in files:\\n k = k + 1\\n sz = get_file_size(f)\\n with open(f, 'rb') as fb:\\n processCerFile(k, fb, sz=sz)\",\n \"def main():\\n\\n if args.sims[0].lower() == 'all':\\n args.sims = xl.get_all_sims(args.base_dir)\\n have_full_sim_dir = True\\n else:\\n have_full_sim_dir = False\\n \\n for isim in args.sims:\\n\\n if have_full_sim_dir:\\n wdir = isim\\n else:\\n wdir = xl.get_sim_dir(args.base_dir, isim)\\n \\n print(\\\"\\\")\\n print(\\\"====================================================================\\\")\\n print(f\\\"=== Processing {wdir} ===\\\")\\n print(\\\"====================================================================\\\") \\n print(\\\"\\\")\\n \\n for iisnap, isnap in enumerate(args.snaps):\\n\\n # Account for possibly different VR numbering than (desired) output\\n if args.vr_snaps is None:\\n ivsnap = isnap\\n else:\\n ivsnap = args.vr_snaps[iisnap]\\n \\n process_snap(wdir, args.out_file, isnap, ivsnap)\",\n \"def main():\\n\\n #Getthefiles\\n all_fna_file_path = []\\n path_to_all_info = '/Users/gustavotamasco/mdrkrp/project_MDR_KRPgenomes_parsnp'\\n #path_to_all_info = argv[1]\\n dirpath=os.getcwd()\\n os.chdir(path_to_all_info)\\n genome_files = list_directories(path_to_all_info)\\n os.chdir(\\\"/Users/gustavotamasco/mdrkrp/plasmids\\\")\\n plasmid_files = list_directories(\\\"/Users/gustavotamasco/mdrkrp/plasmids\\\")\\n\\n\\n '''Genomes'''\\n #for genome in genome_files:\\n #if \\\"fna\\\" in genome:\\n #print(genome)\\n #run_plasflow(genome)\\n\\n '''Eval Plasmids'''\\n for organism in plasmid_files:\\n if \\\"plasflow_plasmids\\\" in organism:\\n run_plasclass(organism)\\n run_blastn(organism)\\n\\n '''Mining info'''\\n data = {}\\n blast_info_path = \\\"/Users/gustavotamasco/mdrkrp/plasmids/plasmid_blast\\\"\\n blast_files = list_files_new_source(blast_info_path)\\n for org_b in blast_files:\\n if \\\"genome\\\" not in org_b:\\n parse_blast(org_b, blast_info_path, data)\",\n \"def process_ecr(cas, cas_dir, sortiefile, ncsize):\\n\\n xcpt = [] # try all files for full report\\n # ~~ copy output files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n for key in cas.out_files:\\n submit = cas.out_files[key].split(';')\\n tmp_file_name = submit[1]\\n file_name = cas.values[key]\\n if submit[5] == 'MULTI': # POSTEL3D\\n npsize = 1\\n while 1: # HORIZONTAL SECTION FILES\\n file_name = path.join(cas_dir,\\n file_name\\\\\\n + '_{0:03d}'.format(npsize))\\n if path.isfile(file_name):\\n base, ext = path.splitext(file_name)\\n i = 0\\n # this would be an infinite loop only if you have an\\n # inifite number of files\\n while 1:\\n i = i + 1\\n if not path.isfile(base+'_old'+str(i)+ext):\\n break\\n shutil.move(file_name, base+'_old'+str(i)+ext)\\n tmp_file_name = tmp_file_name+\\\\\\n '_{0:03d}'.format(npsize)\\n if not path.isfile(tmp_file_name):\\n break\\n shutil.move(tmp_file_name, file_name)\\n print(' moving: '+ path.basename(file_name))\\n npsize = npsize + 1\\n npsize = 1\\n while 1: # VERTICAL SECTION FILES\\n nptime = 1\\n v_file = tmp_file_name+\\\\\\n '_{0:03d}'.format(npsize)+'-{0:03d}'.format(nptime)\\n if not path.isfile(v_file):\\n break\\n while 1:\\n file_name = path.join(cas_dir,\\n file_name+\\\\\\n '_{0:03d}'.format(npsize)+\\\\\\n '-{0:03d}'.format(nptime))\\n if path.isfile(file_name):\\n base, ext = path.splitext(file_name)\\n i = 0\\n # this would be an infinite loop only if you have an\\n # inifite number of files\\n while 1:\\n i = i + 1\\n if not path.isfile(base+'_old'+str(i)+ext):\\n break\\n shutil.move(file_name, base+'_old'+str(i)+ext)\\n tmp_file_name = tmp_file_name\\\\\\n + '_{0:03d}'.format(npsize)\\\\\\n + '-{0:03d}'.format(nptime)\\n if not path.isfile(tmp_file_name):\\n break\\n shutil.move(tmp_file_name, file_name)\\n print(' moving: '+ path.basename(file_name))\\n nptime = nptime + 1\\n npsize = npsize + 1\\n # MAIN MODULE\\n elif submit[5] == 'PARAL' and ncsize > 1:\\n npsize = 0\\n c_base, c_ext = path.splitext(file_name)\\n while 1:\\n file_name = path.join(cas_dir,\\n c_base\\\\\\n + '{0:05d}-{1:05d}'\\\\\\n .format(ncsize-1, npsize)\\\\\\n + c_ext)\\n if path.isfile(file_name):\\n base, ext = path.splitext(file_name)\\n i = 0\\n # this would be an infinite loop only if you have an\\n # inifite number of files\\n while 1:\\n i = i + 1\\n if not path.isfile(base+'_old'+str(i)+ext):\\n break\\n shutil.move(file_name, base+'_old'+str(i)+ext)\\n tmp_file_name_par = tmp_file_name+\\\\\\n '{0:05d}-{1:05d}'.format(ncsize-1, npsize)\\n if not path.isfile(tmp_file_name_par):\\n break\\n shutil.move(tmp_file_name_par, file_name) #shutil.copy2(tmp_file_name,file_name)\\n print(' moving: '+ path.basename(file_name))\\n npsize = npsize + 1\\n elif submit[5] == 'MULTI2':\\n for itmp_file_name in listdir('.'):\\n if itmp_file_name.count(tmp_file_name) == 1:\\n base, ext = path.splitext(file_name)\\n new_tmp_file_name = \\\\\\n itmp_file_name.lower()\\\\\\n .replace(tmp_file_name.lower(),\\n base)\\n new_file_name = path.join(cas_dir, new_tmp_file_name) + ext\\n if path.isfile(new_file_name):\\n base, ext = path.splitext(new_file_name)\\n i = 0\\n # this would be an infinite loop only if you have an\\n # inifite number of files\\n while 1:\\n i = i + 1\\n if not path.isfile(base+'_old'+str(i)+ext):\\n break\\n shutil.move(new_file_name, base+'_old'+str(i)+ext)\\n shutil.move(itmp_file_name, new_file_name)\\n print(' moving: '+ path.basename(new_file_name))\\n else:\\n file_name = path.join(cas_dir, file_name)\\n if path.isfile(file_name):\\n base, ext = path.splitext(file_name)\\n i = 0\\n # this would be an infinite loop only if you have an\\n # inifite number of files\\n while 1:\\n i = i + 1\\n if not path.isfile(base+'_old'+str(i)+ext):\\n break\\n shutil.move(file_name, base+'_old'+str(i)+ext)\\n if not path.isfile(tmp_file_name):\\n xcpt.append({'name':'process_ecr',\\n 'msg':'did not create outfile: '+\\\\\\n path.basename(file_name)+' ('+tmp_file_name+')'})\\n continue\\n shutil.move(tmp_file_name, file_name)\\n print(' moving: '+ path.basename(file_name))\\n\\n # ~~~ copy the sortie file(s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n sortiefiles = []\\n if sortiefile != None:\\n crun = path.basename(sortiefile)\\n cref = path.join(cas_dir, sortiefile)\\n if not path.isfile(crun):\\n xcpt.append({'name':'process_ecr',\\n 'msg':'did not create listing file: '+\\\\\\n path.basename(cref)+' ('+crun+')'})\\n raise TelemacException(xcpt) # raise full report\\n shutil.copy(crun, cref)\\n print(' copying: '+ path.basename(cref))\\n sortiefiles.append(cref)\\n\\n # ~~~> If in parallel, also copy the slave log files\\n # called PEnnnnn_xxxxx.log\\n # for slave x of n but for the last one called the sortie file\\n if ncsize > 1:\\n for i in range(ncsize-1):\\n slavefile = 'PE{0:05d}-{1:05d}.LOG'.format(ncsize-1, i+1)\\n base, ext = path.splitext(sortiefile)\\n slogfile = base+'_p'+'{0:05d}'.format(i+1)+ext\\n crun = slavefile\\n cref = path.join(cas_dir, slogfile)\\n if not path.isfile(crun):\\n xcpt.append({'name':'process_ecr',\\n 'msg':'could not find the listing file: '\\\\\\n + crun})\\n raise TelemacException(xcpt) # raise full report\\n shutil.copy(crun, cref)\\n print(' copying: '+ path.basename(cref))\\n sortiefiles.append(cref)\\n # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n if xcpt != []:\\n raise TelemacException(xcpt) # raise full report\\n return sortiefiles\",\n \"def overall_annotation_function(path_to_species_trees, path_to_gene_trees, project_name = \\\"myproject\\\", path_to_ranger_outputs = \\\"\\\", ):\\r\\n\\r\\n #initially gather the names of the datasets from the species_trees folder\\r\\n dataset_names = gather_dataset_names(path_to_species_trees, project_name, \\\"_CC\\\")\\r\\n #create an object of class PhyloData for each unique dataset\\r\\n phylodata_objects = []\\r\\n for name in dataset_names:\\r\\n phylodata_objects.append(annotate_classes.PhyloData(name))\\r\\n #for each object, have it try and assign itself the correct files\\r\\n print(\\\"populating phylodata objects\\\")\\r\\n populate_objects(phylodata_objects, project_name, path_to_species_trees, path_to_gene_trees, path_to_ranger_outputs)\\r\\n #run the visualizer for each object\\r\\n parse_and_visualize(phylodata_objects, project_name)\",\n \"def test_2_fusion(install_test_files, data_dir):\\n with make_workdir() as workdir:\\n cl = [\\\"bcbio_nextgen.py\\\",\\n get_post_process_yaml(data_dir, workdir),\\n os.path.join(data_dir, os.pardir, \\\"test_fusion\\\"),\\n os.path.join(data_dir, \\\"run_info-fusion.yaml\\\")]\\n subprocess.check_call(cl)\",\n \"def combine_netCDF_rh_cmip6(directory, model):\\n\\n # Make a list of the files in the above folder to loop through\\n list_files = glob.glob(directory)\\n list_files = np.array(list_files)\\n newlist = np.sort(list_files)\\n\\n # Make a cubelist to add each file (cube) to\\n Cubelist = iris.cube.CubeList([])\\n\\n # loop for each file in newlist\\n for i in range(0, len(newlist)):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore', FutureWarning)\\n warnings.simplefilter('ignore', UserWarning)\\n \\n # Load cube\\n cube = iris.load_cube(newlist[i])\\n \\n # matching all standard names\\n cube.standard_name = 'heterotrophic_respiration_carbon_flux'\\n\\n # matching cube metadata\\n if i == 0:\\n metadata1 = cube.metadata\\n else:\\n cube.metadata = metadata1\\n \\n # creating latitude and longitude bounds\\n if model=='IPSL-CM6A-LR' or model=='CNRM-ESM2-1':\\n cube.coord('latitude').guess_bounds()\\n cube.coord('longitude').guess_bounds()\\n \\n # removing time attributes\\n if model=='IPSL-CM6A-LR':\\n cube.coord('time').attributes.pop('time_origin')\\n \\n # Append this cube to the cubelist\\n Cubelist.append(cube)\\n\\n # matching attributes\\n unify_time_units(Cubelist)\\n equalise_attributes(Cubelist)\\n # Concatenate each cube in cubelist together to make one data file (cube)\\n new_cube = Cubelist.concatenate_cube()\\n \\n for cube in Cubelist:\\n lon_bounds = Cubelist[0].coord('longitude').bounds\\n cube.coord('longitude').bounds = lon_bounds\\n\\n for i, cube in enumerate(Cubelist):\\n if cube.coord('time').units == Cubelist[0].coord('time').units:\\n pass\\n else:\\n print(i)\\n\\n return new_cube\",\n \"def process_csvs(conn: Connection, basedir: Path) -> None:\\n process_files(conn, basedir/\\\"files.csv\\\")\\n process_notes(conn, basedir/\\\"notes.csv\\\")\\n process_links(conn, basedir/\\\"links.csv\\\")\\n process_clusters(conn, basedir/\\\"clusters.csv\\\")\\n process_bibliography(conn, basedir/\\\"bibliography.csv\\\")\\n process_citations(conn, basedir/\\\"citations.csv\\\")\",\n \"def main():\\n for dim in (50, 100, 200, 300):\\n data_file = data.FileFinder().get_file('GLOVE_TXT_FILE').format(dim)\\n output_file = data.FileFinder().get_file('GLOVE_WORD2VEC_FILE').format(dim)\\n print(\\\"Converting {} to {}\\\".format(data_file, output_file))\\n glove2word2vec(data_file, output_file)\",\n \"def main():\\n\\n #Getthefiles\\n all_fna_file_path = []\\n path_to_all_info = '/Users/gustavotamasco/Google Drive/Shared drives/Projeto MDR KRP/Dados_Sequenciamento/'\\n dirpath=os.getcwd()\\n os.chdir(path_to_all_info)\\n directories = list_directories(path_to_all_info)\\n\\n '''Genomes'''\\n genomes_path = \\\"{}{}\\\".format(path_to_all_info,directories[0])\\n os.chdir(genomes_path)\\n genome_dir = list_directories(genomes_path)\\n for organism in genome_dir:\\n fna_files = list_files(all_fna_file_path,genomes_path,organism)\\n print_status(fna_files)\\n\\n '''Building a dir of fna files'''\\n genomes_fna_path = \\\"{}genomes_parsnp\\\".format(dirpath)\\n create_genomes_dir(genomes_fna_path)\\n os.chdir(genomes_fna_path)\\n for file in fna_files:\\n move_file(file, genomes_fna_path)\\n\\n '''Adding extra organism from a different source'''\\n klebs = \\\"/Users/gustavotamasco/mdrkrp/klebs\\\"\\n k_files = list_files_new_source(klebs)\\n for k_file in k_files:\\n if \\\".fna\\\" in k_file:\\n final_k_file = \\\"{}/{}\\\".format(klebs,k_file)\\n move_file(final_k_file, genomes_fna_path)\\n\\n\\n '''Run parsnp'''\\n run_parsnp(dirpath, genomes_fna_path)\",\n \"def gzip_files(topDir, afni_file,localizerfile):\\n\\n print 'GZIPPING FILES...'\\n\\n command1 = \\\"gzip %s*BRIK\\\" % (topDir)\\n command2 = \\\"gzip %s*HEAD\\\" % (topDir)\\n command3 = \\\"gzip %s%s/pb00*\\\" % (topDir,afni_file)\\n command4 = \\\"gzip %s%s/errts*\\\" % (topDir,afni_file)\\n command5 = \\\"gzip %s%s/fitts*\\\" % (topDir,afni_file)\\n command6 = \\\"gzip %s%s/*al*\\\" % (topDir,afni_file)\\n command7 = \\\"gzip %s%s/*ns*\\\" % (topDir,afni_file)\\n command8 = \\\"gzip %s%s/all_runs*\\\" % (topDir,afni_file)\\n command9 = \\\"gzip %s%s/*mask*\\\" % (topDir,afni_file)\\n command10 = \\\"gzip %s*nii\\\" % (topDir)\\n command11 = \\\"gzip %s%s/pb01*\\\" % (topDir,afni_file)\\n command12 = \\\"gzip %s%s/pb03*\\\" % (topDir,afni_file)\\n command13 = \\\"gzip %s%s/pb02*\\\" % (topDir,afni_file)\\n\\n os.system(command1)\\n os.system(command2)\\n os.system(command3)\\n os.system(command4)\\n os.system(command5)\\n os.system(command6)\\n os.system(command7)\\n os.system(command8)\\n #os.system(command9)\\n os.system(command10)\\n os.system(command11)\\n try:\\n os.system(command12)\\n except:\\n print 'Could not gzip pb03 files'\\n\\n if localizerfile == 'facescene_loc':\\n os.system(command13)\",\n \"def main():\\n print(\\n \\\"\\\"\\\"\\n\\n ##########################################################\\n # #\\n # #\\n # Compiling Colocalized Cyano Datasets #\\n # #\\n # #\\n ##########################################################\\n\\n \\n \\\"\\\"\\\"\\n )\\n cyanoFiles = glob.glob(f\\\"{COLOCALIZED_DIR}*.csv\\\")\\n makedir(COMPILED_DIR)\\n dfCompiled = pd.DataFrame({})\\n for cyanoFile in cyanoFiles:\\n print(f\\\"Compiling {cyanoFile}\\\")\\n data = unify(cyanoFile)\\n if len(dfCompiled ) < 1:\\n dfCompiled = data\\n else:\\n dfCompiled = pd.concat([dfCompiled, data], ignore_index=True) \\n dfCompiled.to_csv(f\\\"{COMPILED_DIR}compiled.csv\\\", index=False)\",\n \"def load_cogs(self):\\n\\n path = \\\"cogs/\\\" # Should always have a trailing slash\\n import_path = path.replace(\\\"/\\\", \\\".\\\")\\n extensions: list[str] = [\\n import_path + file.replace(\\\".py\\\", \\\"\\\")\\n for file in os.listdir(path)\\n if os.path.isfile(f\\\"{path}{file}\\\")\\n ]\\n\\n for extension in extensions:\\n try:\\n self.load_extension(extension)\\n except errors.ExtensionAlreadyLoaded:\\n pass\\n\\n log.info(f\\\"Loaded {len(self.commands)} commands from {len(self.cogs)} cogs\\\")\",\n \"def finalize(param, input_files='count_files'):\\n\\n import csv\\n HELPER.writeLog('Collecting featureCount raw counts ... \\\\n', param)\\n\\n #check which of these files are actually available\\n working_files = [iFile for iFile in param[input_files] if iFile != '']\\n\\n if len(working_files) > 0:\\n #get feature ID using the first column in the first file in the list of working files\\n csv_file = open(working_files[0])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n\\n #For featureCount output, we want to skip the first two lines as they\\n #include the featureCount call and the headers which we don't want\\n next(csv_reader, None)\\n next(csv_reader, None)\\n\\n #Now start by taking the list of identifier,\\n #which is the first column in the file\\n counts = [row[0] for row in csv_reader]\\n csv_file.close()\\n\\n #get all the expression values\\n header = 'ID'\\n for idx in range(param['num_samples']):\\n if param[input_files] != '':\\n header = header+'\\\\t'+param['stub'][idx]\\n csv_file = open(param[input_files][idx])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n\\n #Here too we want to skip the first two lines, before getting the counts\\n next(csv_reader, None)\\n next(csv_reader, None)\\n #Now start getting the counts (row[6]) and add in the ID (counts[i]) before it\\n idx = 0\\n for row in csv_reader:\\n counts[idx] = counts[idx]+'\\\\t'+row[6]\\n idx += 1\\n csv_file.close()\\n\\n #output the file\\n out_file = param['working_dir']+'deliverables/featureCount_raw_counts.txt'\\n out_handle = open(out_file, 'w')\\n out_handle.write(header+'\\\\n')\\n\\n for i in range(len(counts)):\\n out_handle.write(counts[i]+'\\\\n')\\n out_handle.close()\\n\\n #output_phenotype_file\\n HELPER.writeLog('Writing phenotype data ... \\\\n', param)\\n MODULE_HELPER.output_sample_info(param)\\n\\n #write summary stats\\n #featureCount does this on its own so we can just fetch each summary file\\n #check which of these files are actually available\\n working_files = [iFile+'.summary' for iFile in param[input_files] if iFile != '']\\n\\n if len(working_files) > 0:\\n #get Status column from summary file using the first column in\\n #the first file in the list of working files\\n csv_file = open(working_files[0])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n #Here, we want to skip the first line, as it simply points to the\\n #alignment file used when running featureCount\\n next(csv_reader, None)\\n\\n #Now start by taking the list of identifier,\\n #which is the first column in the file\\n entry = [row[0] for row in csv_reader]\\n csv_file.close()\\n\\n #get all the summary stats for each sample\\n header = 'Status'\\n\\n for idx in range(param['num_samples']):\\n if param[input_files] != '':\\n header = header+'\\\\t'+param['stub'][idx]\\n #Fetch the corresponding sample's summary file\\n csv_file = open(param[input_files][idx]+'.summary')\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n #Again, we want to skip the first line, as it simply points\\n #to the alignment file used when running featureCount\\n next(csv_reader, None)\\n\\n #Now start getting the stats (row[1]) and add in the Status\\n # (counts[i]) before it\\n i = 0\\n for row in csv_reader:\\n entry[i] = entry[i]+'\\\\t'+row[1]\\n i += 1\\n csv_file.close()\\n #output the file\\n out_handle = open(param['working_dir']+\\n 'results/featureCount/featureCount_stats.txt',\\n 'w')\\n out_handle.write(header+'\\\\n')\\n\\n for i in range(len(entry)):\\n out_handle.write(entry[i]+'\\\\n')\\n out_handle.close()\\n else:\\n print 'featureCount was not run successfully on any of the files..\\\\n'\",\n \"def voorbeeld():\\n os.system(\\\"rm Twogs.txt cogs.txt\\\")\\n os.system(\\\"touch Twogs.txt cogs.txt\\\")\\n orgslijst = [y[:-1] for y in openfile(\\\"orglijst.txt\\\") if \\\"\\\\n\\\" in y]\\n for x in range(len(orgslijst)):\\n org1 = orgslijst[x]\\n for y in range(len(orgslijst)):\\n org2 = orgslijst[y]\\n if x > y:\\n schrijfnaartwog(org1, org2)\\n print(org1)\\n prots, twogs, cogs = openneededfiles(org1)\\n if x >= 2:\\n searchforcog(cogs, twogs, prots)\",\n \"def FSC2(input_dir, num_reps=50, min_sims=100000, max_ecm=20, calc_CI=False, numcores=1, scratch_mb='200', time_scratch=\\\"01:50:00\\\", mem=\\\"200\\\", print1=False, overwrite=\\\"None\\\", fsc2_path=\\\"/storage/plzen1/home/holcovam/programs/fsc26_linux64/fsc26\\\"):\\n Data_Files = []\\n tpl_files = []\\n est_files = []\\n CI_Data_Files = []\\n shlist = []\\n\\n if input_dir.endswith(\\\"/\\\") is False:\\n input_dir += \\\"/\\\"\\n\\n for path in os.listdir(input_dir):\\n if os.path.isdir(input_dir + path) and path.startswith(\\\"FSC2input\\\"):\\n samp_name = path.split(\\\"_\\\")[1]\\n #folder_name = samp_name\\n if samp_name + \\\"_DSFS.obs\\\" in os.listdir(input_dir + path):\\n for i in range(0, num_reps):\\n new_file = open(input_dir + path + \\\"/\\\" + samp_name + str(i) + \\\"_DSFS.obs\\\", 'w')\\n with open(input_dir + path + \\\"/\\\" + samp_name + \\\"_DSFS.obs\\\") as data_file:\\n for line in data_file:\\n new_file.write(line)\\n new_file.close()\\n Data_Files.append(input_dir + path + \\\"/\\\" + samp_name + str(i) + \\\"_DSFS.obs\\\")\\n else:\\n print(\\\"Did not find input data file for: \\\", samp_name)\\n if calc_CI == \\\"True\\\":\\n num_files = 0\\n for file in os.listdir(input_dir + path):\\n if file.endswith(\\\"_DSFS.obs\\\") and file.split(\\\"_\\\")[-2].split(\\\".\\\")[-1][0:3] == \\\"rep\\\" and file != samp_name + \\\"_DSFS.obs\\\":\\n for i in range(0, num_reps):\\n new_file = open(input_dir + path + \\\"/\\\" + samp_name + file.split(\\\"_\\\")[-2].split(\\\".\\\")[-1].split(\\\"_\\\")[0]+ \\\"_\\\" + str(i) + \\\"_DSFS.obs\\\", 'w')\\n with open(input_dir + path + \\\"/\\\" + file) as data_file:\\n for line in data_file:\\n new_file.write(line)\\n new_file.close()\\n CI_Data_Files.append(input_dir + path + \\\"/\\\" + samp_name + file.split(\\\"_\\\")[-2].split(\\\".\\\")[-1].split(\\\"_\\\")[0]+ \\\"_\\\" + str(i) + \\\"_DSFS.obs\\\")\\n num_files += 1\\n if len(CI_Data_Files) < 1:\\n print(\\\"Did not find bootstrap replicates for: \\\", samp_name)\\n else:\\n print(\\\"Found \\\", num_files, \\\" replicate dsfs files for CI calculation for \\\", samp_name)\\n if path.endswith(\\\".tpl\\\"):\\n tpl_files.append(path)\\n est_files.append(path.split(\\\".\\\")[0])\\n if len(tpl_files) == 0:\\n print(\\\"Did not find any tpl files!! Aborting!!\\\")\\n else:\\n if calc_CI == \\\"True\\\":\\n Data_Files = CI_Data_Files\\n for file in Data_Files:\\n name = file.split(\\\"_DSFS\\\")[0]\\n samp_name = name.split(\\\"/\\\")[-1]\\n folder_name = samp_name [0:11]\\n for tpl in tpl_files:\\n tpl_name = tpl.split(\\\".tpl\\\")[0]\\n if os.path.isdir(name + \\\"_\\\" + tpl_name) is False or overwrite == \\\"hard\\\":\\n new_tpl = open(name + \\\"_\\\" + tpl_name + \\\".tpl\\\", 'w')\\n new_data = open(name + \\\"_\\\" + tpl_name + \\\"_DSFS.obs\\\", 'w')\\n\\n with open(file, 'r') as data:\\n for i, line in enumerate(data):\\n if i == 1:\\n pop_info = line.strip(\\\"\\\\n\\\").strip(\\\"\\\\t\\\").split(\\\"\\\\t\\\")\\n pop_num = int(pop_info[0])\\n samp_nums = pop_info[-pop_num:]\\n new_data.write(line)\\n with open(input_dir + tpl, 'r') as template:\\n samp_num_lines = pop_num + 4\\n for i, line in enumerate(template):\\n if i < samp_num_lines:\\n new_tpl.write(line)\\n elif i == samp_num_lines:\\n for num in samp_nums:\\n new_tpl.write(num + \\\"\\\\n\\\")\\n elif i >= samp_num_lines + len(samp_nums):\\n new_tpl.write(line)\\n new_est = open(name + \\\"_\\\" + tpl_name + \\\".est\\\", 'w')\\n try:\\n with open(input_dir + tpl_name + \\\".est\\\") as est:\\n for line in est:\\n new_est.write(line)\\n except FileNotFoundError:\\n print(\\\"Did not find est file for: \\\", tpl)\\n #folder_name = samp_name ''.join(i for i in s if not i.isdigit())\\n shname = name + \\\"_\\\" + tpl_name + \\\".sh\\\"\\n shfile5 = open(shname, 'w')\\n shfile5.write('#!/bin/bash -e\\\\n' +\\n '#PBS -N '+samp_name+'\\\\n' +\\n '#PBS -l walltime='+str(time_scratch)+'\\\\n' +\\n '#PBS -l select=1:ncpus='+str(numcores)+':mem='+str(mem)+'mb:scratch_local='+str(scratch_mb)+'mb\\\\n' +\\n '#PBS -m abe\\\\n' +\\n '#PBS -j oe\\\\n\\\\n' +\\n 'module add python-3.4.1-gcc\\\\n'+\\n 'module add python34-modules-gcc\\\\n'+\\n 'trap \\\\'clean_scratch\\\\' TERM EXIT\\\\n'+\\n 'if [ ! -d \\\"$SCRATCHDIR\\\" ] ; then echo \\\"Scratch not created!\\\" 1>&2; exit 1; fi \\\\n' +\\n 'DATADIR=\\\"/storage/plzen1/home/holcovam/ScanTools\\\"\\\\n' +\\n 'cp $DATADIR/'+ input_dir + \\\"FSC2input_\\\" + folder_name+ \\\"/\\\" + samp_name + \\\"_\\\" + tpl_name + '* $SCRATCHDIR || exit 1\\\\n'+\\n 'cp '+fsc2_path+' $SCRATCHDIR || exit 1\\\\n'+\\n 'cd $SCRATCHDIR || exit 2\\\\n' +\\n 'echo data loaded at `date`\\\\n\\\\n' +\\n 'chmod +x fsc26 \\\\n' +\\n #'ls -l \\\\n' +\\n './fsc26 -t ' + samp_name + \\\"_\\\" + tpl_name + '.tpl -e ' + samp_name + \\\"_\\\" + tpl_name + '.est -n ' + str(min_sims) + ' -u -d -q -L ' + str(max_ecm) + ' -M \\\\n' + \\n 'rm seed.txt \\\\n'+\\n 'rm fsc26\\\\n'+\\n 'rm *DSFS.obs\\\\n'+\\n 'rm *.sh\\\\n'+\\n 'rm *.tpl \\\\n'+\\n 'rm *.est \\\\n'+\\n #'ls -l \\\\n' +\\n 'cp $SCRATCHDIR/*.par $DATADIR/'+ input_dir + \\\"FSC2input_\\\" + folder_name+' || exit 1\\\\n'+\\n 'rm *.par \\\\n'+\\n 'cp -r $SCRATCHDIR/* $DATADIR/'+input_dir+' || export CLEAN_SCRATCH=false\\\\n'+\\n 'printf \\\"\\\\\\\\nFinished\\\\\\\\n\\\\\\\\n\\\"\\\\n')\\n shfile5.close()\\n shlist.append(shname)\\n\\n############IF PROBLEM WITH EXCESS OF NONCONVERGED CHAINS, COPY /home/majda/alpine/fastsimcoal2/afterWPSG/scripts/notConverged.py here ###################\\n\\n else:\\n print(\\\"Output for \\\" + samp_name + \\\"_\\\" + tpl_name + \\\" already exists. Use hard_overwrite = True to overwrite.\\\")\\n return shlist\",\n \"def calc_GC(filepath):\\n liste=['small.exon.piRNA_2.fa', 'small.exon.piRNA_1.fa', 'small.exon.piRNA_3.fa']\\n \\n length=list(range(0,34))\\n d={}\\n for i in length:\\n d[i]={'A':0, 'G':0, 'T':0, 'C':0}\\n for i in liste:\\n with open(filepath+'/'+i, 'r') as f:\\n for line in f:\\n #fasta header starts with >\\n if line.startswith('>'):\\n pass\\n else:\\n line_l=list(line)\\n for el in range(len(line_l)):\\n if line_l[el]=='A':\\n d[el]['A']+=1\\n elif line_l[el]=='T':\\n d[el]['T']+=1\\n elif line_l[el]== 'G':\\n d[el]['G']+=1\\n elif line_l[el]== 'C':\\n d[el]['C']+=1\\n\\n df=pd.DataFrame.from_dict(d)\\n df=df.transpose()\\n df.index = np.arange(1, len(df) + 1)\\n \\n\\n df['A [%]']=df['A']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\\n df['G [%]']=df['G']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\\n df['T [%]']=df['T']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\\n df['C [%]']=df['C']/(df['A'].sum()+df['G'].sum()+df['C'].sum()+df['T'].sum())*100\",\n \"def coverage_files() -> Iterable[str]:\\n packs_path = CONTENT_PATH / \\\"Packs\\\"\\n for cov_path in packs_path.glob(\\\"*/Integrations/*/.coverage\\\"):\\n yield str(cov_path)\\n for cov_path in packs_path.glob(\\\"*/Scripts/*/.coverage\\\"):\\n yield str(cov_path)\",\n \"def per_sample_dot_files(self):\\n # The output directory #\\n directory = DirectoryPath(self.a.out_dir+'per_sample_ontology/')\\n directory.create_if_not_exists()\\n # Main loop #\\n for i, sample in self.df_sample_concepts.iteritems():\\n # File path #\\n sanitized_name = \\\"\\\".join([c for c in sample.name if re.match(r'\\\\w', c)])\\n dot_path = directory + sanitized_name +'.dot'\\n pdf_path = directory + sanitized_name +'.pdf'\\n # Counts #\\n counts = sample / sample.sum()\\n counts = dict(counts)\\n envos = counts.keys()\\n # Skip sample if it has no counts #\\n if sample.sum() == 0: continue\\n # Make graph #\\n graph = self.a.ontology.get_subgraph(envos)\\n graph = self.a.ontology.add_weights(graph, counts)\\n graph = self.a.ontology.add_style(graph)\\n # Write output #\\n self.a.ontology.write_to_dot(graph, dot_path)\\n self.a.ontology.add_legend(dot_path)\\n self.a.ontology.draw_to_pdf(dot_path, pdf_path)\",\n \"def processFiles(self, testDataFiles, sourceDirectory, sourceFilename, outputFile, language, IncludeSourceInOutput, maxRunTime, interpreter, maxOutputLines, AutoGraderVersion):\\n \\n print (\\\"***Start***\\\")\\n self.sourceDirectory = sourceDirectory\\n #self.TopLevelFilesFound = []\\n #self.subdirs = {} #dictionary of sbudirectories\\n \\n\\n #delete the output file\\n self._removeFile(outputFile)\\n\\n #create the html header. Use the name of the source directory as the header text.\\n self._MakeHtmlHeader(outputFile, language, \\\"AutoGrader\\\", os.path.split(sourceDirectory)[-1])\\n\\n #--------- C++ ---------\\n if language == 'C++':\\n #call _recursivelyFindFiles to generate the self.TopLevelFilesFound and self.SubDirsFound lists\\n (self.TopLevelFilesFound, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, \\\".cpp\\\")\\n (tlf, sd) = self._recursivelyFindFiles(sourceDirectory, \\\".cc\\\")\\n\\n self.TopLevelFilesFound += tlf\\n self.SubDirsFound += sd\\n\\n exeFile = sourceDirectory + '/' + 'AG.out'\\n\\n\\n def doInnerCppProcessing(sourceFiles, gradingTextLabel): #sourceFiles is a list of filenames\\n #get/report the analytics on the source files\\n self._reportFileAnalytics(sourceFiles, outputFile, language)\\n \\n #include source code here if selected\\n if IncludeSourceInOutput == True:\\n self._formatSource(sourceFiles, outputFile, language)\\n\\n #compile the file\\n self._removeFile(exeFile)\\n self._compileCppFiles(interpreter, sourceFiles, outputFile, exeFile, maxOutputLines)\\n\\n if os.path.isfile(exeFile): #did the compilation succeed?\\n print(\\\"Compilation succeeded.\\\")\\n self._reportErrorMsg(\\\"Compilation succeeded.<br>\\\", outputFile) \\n if len(testDataFiles) == 0: #no input data required\\n exec_time = self._shellExec('\\\"'+exeFile+'\\\"', '', '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n else:\\n for dataFile in testDataFiles:\\n self._reportDataFile(dataFile, outputFile)\\n \\n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\\n _, filename = os.path.split(dataFile)\\n print (\\\"processing '\\\" + filename + \\\"'...\\\")\\n exec_time = self._shellExec('\\\"'+exeFile+'\\\"', '', dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n self._reportExecTime(exec_time, outputFile)\\n print ()\\n else:\\n print(\\\"Executable not found. Check compiler output.\\\")\\n self._reportErrorMsg(\\\"Executable not found. Check compiler output.<br>\\\", outputFile)\\n\\n self._removeFile(exeFile)\\n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\\n return\\n\\n\\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\\n #note that this is a list of tuples. The first element is the name of the file or directory.\\n #the second element specifies if the first is a filename or directory name.\\n filesAndDirs = []\\n for x in self.TopLevelFilesFound:\\n filesAndDirs.append((x,'file')) #add files\\n\\n for x in self.SubDirsFound:\\n filesAndDirs.append((x,'dir')) #add directories\\n\\n filesAndDirs.sort() #sort the list\\n\\n print (\\\"**********************************\\\")\\n for x in filesAndDirs:\\n print (x)\\n print (\\\"**********************************\\\")\\n\\n #now, process the sorted list depending on whether it is a single-file or directory.\\n for n, x in enumerate(filesAndDirs):\\n if x[1] == 'file': #this is a file\\n #we will process source code in the top-level directory as single-file programs \\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n #doInnerCppProcessing([x[0]], 'student'+str(n))\\n doInnerCppProcessing([x[0]], 'student')\\n elif x[1] == 'dir': #this is a directory\\n #we will process source code in sub-directories of the top-level directory as multi-file programs\\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n sourceFiles = []\\n self._findFilesInDir(x[0], \\\".cpp\\\", sourceFiles) #add .h files to the files list\\n self._findFilesInDir(x[0], \\\".cc\\\", sourceFiles) #add .h files to the files list\\n self._findFilesInDir(x[0], \\\".h\\\", sourceFiles) #add .h files to the files list\\n self._findFilesInDir(x[0], \\\".hpp\\\", sourceFiles) #add .hpp files to the files list\\n #doInnerCppProcessing(sourceFiles, 'student'+str(n))\\n doInnerCppProcessing(sourceFiles, 'student')\\n else:\\n print (\\\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\\\")\\n \\n \\n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\\n\\n #--------- Python ---------\\n elif language == 'Python':\\n print ('Find sub dirs')\\n #first, find all the subdirectories that contain a python source file that matches <sourceFilename>\\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, sourceFilename)\\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\\n self.SubDirsFound = self._recursivelyFindFile(sourceDirectory, sourceFilename)\\n\\n print ('Find TLFs')\\n #now, find all .py files in the top level directory.\\n self.TopLevelFilesFound = self._findFilesInDir(sourceDirectory, \\\".py\\\", [])\\n #(self.TopLevelFilesFound, _) = self._recursivelyFindFiles(sourceDirectory, \\\".py\\\")\\n\\n\\n #does this block do anything??? 9/25/16\\n #if sourceFilename == \\\"\\\" or sourceFilename == None:\\n #self._recursivelyFindFiles(sourceDirectory, \\\".py\\\")\\n #else:\\n #self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\\n\\n\\n def doInnerPythonProcessing(sourceFiles, topLevelModule, gradingTextLabel): #sourceFiles is a list of filenames\\n #get/report the analytics on the source files\\n self._reportFileAnalytics(sourceFiles, outputFile, language)\\n \\n #include source code here if selected\\n if IncludeSourceInOutput == True:\\n self._formatSource(sourceFiles, outputFile, language)\\n\\n\\n if len(testDataFiles) == 0: #no input data required\\n exec_time = self._shellExec('\\\"'+interpreter+'\\\"', topLevelModule, '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n self._reportExecTime(exec_time, outputFile)\\n else:\\n for dataFile in testDataFiles:\\n self._reportDataFile(dataFile, outputFile)\\n \\n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\\n _, filename = os.path.split(dataFile)\\n print (\\\"processing '\\\" + filename + \\\"'...\\\")\\n exec_time = self._shellExec('\\\"'+interpreter+'\\\"', topLevelModule, dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n self._reportExecTime(exec_time, outputFile)\\n print ()\\n \\n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\\n return\\n\\n\\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\\n #note that this is a list of tuples. The first element is the name of the file or directory.\\n #the second element specifies if the first is a filename or directory name.\\n filesAndDirs = []\\n for x in self.TopLevelFilesFound:\\n filesAndDirs.append((x,'file')) #add files\\n\\n for x in self.SubDirsFound:\\n filesAndDirs.append((x,'dir')) #add directories\\n\\n filesAndDirs.sort() #sort the list\\n\\n print (\\\"**********************************\\\")\\n for x in filesAndDirs:\\n print (x)\\n print (\\\"**********************************\\\")\\n\\n\\n #now, process the sorted list depending on whether it is a single-file or directory\\n for n, x in enumerate(filesAndDirs):\\n if x[1] == 'file': #this is a file\\n #we will process source code in the top-level directory as single-file programs \\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n #doInnerPythonProcessing([x[0]], x[0], 'student'+str(n))\\n doInnerPythonProcessing([x[0]], x[0], 'student')\\n elif x[1] == 'dir': #this is a directory\\n #we will process source code in sub-directories of the top-level directory as multi-file programs\\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n #doInnerPythonProcessing(self._findFilesInDir(x[0],\\\".py\\\", []), x[0]+'/'+sourceFilename, 'student'+str(n))\\n doInnerPythonProcessing(self._findFilesInDir(x[0],\\\".py\\\", []), x[0]+'/'+sourceFilename, 'student')\\n else:\\n print (\\\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\\\")\\n \\n \\n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\\n\\n\\n\\n #--------- Unknown language ---------\\n else:\\n print ('Unknown language choice: ' + language)\\n return\\n\\n \\n self._reportErrorMsg('<br><font face=\\\"verdana\\\">', outputFile)\\n self._reportErrorMsg('Report Generator: AutoGrader v' + AutoGraderVersion + '<br>', outputFile)\\n if language == 'C++':\\n self._reportErrorMsg('C++ Compiler: ' + interpreter + '<br>', outputFile)\\n elif language == 'Python':\\n self._reportErrorMsg('Python Interpreter: ' + interpreter + '<br>', outputFile)\\n else:\\n self._reportErrorMsg('Compiler/Interpreter: Not Specified <br>', outputFile)\\n self._reportErrorMsg('<br></font>', outputFile)\\n\\n self._writeOutput('''<font size='+6'><input type=\\\"button\\\" style=\\\"font-size:20px;width:250px\\\" value=\\\"Download Feedback\\\" OnClick=\\\"download_feedback_file()\\\">\\n <br><br></font></form>''', outputFile)\\n\\n\\n feedback_filename = 'feedback.txt'\\n download_script = '''\\n <script type=\\\"text/javascript\\\">\\n\\n function download_feedback_file()\\n {\\n \\n x = document.getElementsByName(\\\"student\\\").length\\n msg = \\\"\\\"\\n for (i=0; i<x; i++)\\n {\\n msg = msg + document.getElementsByName(\\\"student\\\")[i].value\\n msg = msg + '\\\\\\\\n\\\\\\\\n-------------------------------------------------------\\\\\\\\n'\\n }\\n\\n var element = document.createElement('a');\\n element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(msg));\\n element.setAttribute('download', \\\"''' + feedback_filename + '''\\\");\\n\\n element.style.display = 'none';\\n document.body.appendChild(element);\\n\\n element.click();\\n\\n document.body.removeChild(element);\\n }\\n\\n </script>\\n </body></html>\\n\\n '''\\n self._writeOutput(download_script, outputFile)\\n\\n self._writeOutput('</body></html>', outputFile)\\n\\n \\n #open the output file using the default application\\n cmd = 'open \\\"' + outputFile + '\\\"'\\n os.system(cmd)\\n \\n print (\\\"***End***\\\\n\\\")\\n\\n\\n\\n #--------- ---------\",\n \"def make_master_flats(dc):\\n\\n\\t## Make EXTcheck: is there always the same number of extensions in each file\\n\\tprint \\\"Making master flats\\\"\\n\\t\\n\\t## Choose extensions you are using\\n\\t\\n\\tfor flat_type in ['FFS']: # Currently FFD is unsupported. If you have FFDs, add them to the list but you must have ONLY FFDs or ONLY FFSs in the dir. Otherwise the first element in the list will get overwritten!\\n\\t\\t#~ print \\\"\\\\n\\\", flat_type, \\\"\\\\n\\\"\\n\\t\\tfor i in dc:\\n\\t\\t\\tTRIM, TRIM1, VR, PS, PS1, OS, OS1 = CCD_sections((i[0], i[1]))\\n\\t\\t\\tfilelist = []\\n\\t\\t\\tfor f in glob.glob(RAW+'*'+flat_type+'*fits'):\\n\\t\\t\\t\\tccd_conf = []\\n\\t\\t\\t\\theader0 = fits.getheader(f)\\n\\t\\t\\t\\theader1 = fits.getheader(f, ext=1)\\n\\t\\t\\t\\tif header0['OBSMODE']==flat_type:\\n\\t\\t\\t\\t\\tfor KW in ['BINX', 'BINY']:\\n\\t\\t\\t\\t\\t\\tccd_conf.append(header0[KW])\\n\\t\\t\\t\\t\\tfor KW in ['NAXIS1', 'NAXIS2']:\\n\\t\\t\\t\\t\\t\\tccd_conf.append(header1[KW])\\n\\t\\t\\t\\t\\t\\tif tuple(ccd_conf)==i:\\n\\t\\t\\t\\t\\t\\t\\tfilelist.append(f)\\n\\t\\t\\tlfl = len(filelist)\\n\\t\\t\\tif lfl > 0:\\n\\t\\t\\t\\tBIN=CD+'/'+str(i[0])+'x'+str(i[1])+'/'\\n\\t\\t\\t\\tWD=BIN+str(i[-2])+'x'+str(i[-1])+'/' # Bottom level dir with calibrated and master frames\\n\\t\\t\\t\\tB=check_exist(WD, 'MF.fits', i)\\n\\t\\t\\t\\tif B=='n':\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\thdul = fits.HDUList()\\n\\t\\t\\t\\t\\thdul.append(fits.ImageHDU())\\n\\t\\t\\t\\t\\t#~ MB = fits.open(WD+'MB.fits')\\n\\t\\t\\t\\t\\tx = np.array(range(0,i[-1]))\\n\\t\\t\\t\\t\\tfor EXT in (extensions):\\n\\t\\t\\t\\t\\t\\tprint \\\"##################################################\\\"\\n\\t\\t\\t\\t\\t\\tprint \\\"Stacking \\\"+`lfl`+' '+`i[-2]`+'x'+`i[-1]`+' channel '+`EXT`+' flat frames!'\\n\\t\\t\\t\\t\\t\\tif EXT==1:\\n\\t\\t\\t\\t\\t\\t\\tPSC=PS1\\n\\t\\t\\t\\t\\t\\t\\tOSC=OS1\\n\\t\\t\\t\\t\\t\\t\\tTR=TRIM1\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tPSC=PS1\\n\\t\\t\\t\\t\\t\\t\\tOSC=OS\\n\\t\\t\\t\\t\\t\\t\\tTR=TRIM\\n\\t\\t\\t\\t\\t\\tsc = -1 # counts how many flats have mean>limit\\n\\t\\t\\t\\t\\t\\tfor n, fn in enumerate(filelist):\\n\\t\\t\\t\\t\\t\\t\\tprint \\\"Files left:\\\",`lfl-n`+'/'+`lfl`\\n\\t\\t\\t\\t\\t\\t\\tim = fits.getdata(fn, ext=EXT)\\n\\t\\t\\t\\t\\t\\t\\tmeanval = np.mean(im[VR[0]:VR[1], TR[0]:TR[1]])\\n\\t\\t\\t\\t\\t\\t\\t#~ maxval = np.max(im[VR[0]:VR[1], TR[0]:TR[1]])\\n\\t\\t\\t\\t\\t\\t\\tmaxval = stats.scoreatpercentile(im[VR[0]:VR[1], TR[0]:TR[1]], 90)\\n\\t\\t\\t\\t\\t\\t\\texptime = fits.getheader(fn)['EXPTIME']\\n\\t\\t\\t\\t\\t\\t\\t#~ if meanval > 15000. and meanval < 40000. and maxval < 50000. and exptime>5.:\\n\\t\\t\\t\\t\\t\\t\\tif meanval > 16000. and meanval < 40000. and exptime>=5.:\\n\\t\\t\\t\\t\\t\\t\\t\\tsc+=1\\n\\t\\t\\t\\t\\t\\t\\t\\t#~ im[im<1]=1\\n\\t\\t\\t\\t\\t\\t\\t\\tmscrow, sigmarow = median_row(OSC, PSC, TR, im)\\n\\t\\t\\t\\t\\t\\t\\t\\tsh = np.shape(im)\\n\\t\\t\\t\\t\\t\\t\\t\\tfor y in range(0, sh[0]):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tim[y] = im[y]-mscrow[y]\\n\\t\\t\\t\\t\\t\\t\\t\\tF=im\\n\\t\\t\\t\\t\\t\\t\\t\\tnorm = np.median(F[VR[0]:VR[1], TR[0]:TR[1]])\\n\\t\\t\\t\\t\\t\\t\\t\\tF = F/norm #+np.min(F)+0.0001\\n\\t\\t\\t\\t\\t\\t\\t\\tif sc==0:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tstack_arr = F\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tstack_arr = np.dstack((stack_arr, F))\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"****************************************\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"Rejected\\\", fn, \\\"AVG =\\\", meanval, \\\"EXPTIME =\\\", exptime\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"****************************************\\\"\\n\\t\\t\\t\\t\\t\\tprint 'Will stack a total of', np.shape(stack_arr)[2], 'flats'\\n\\t\\t\\t\\t\\t\\tMF = np.median(stack_arr, axis=2)\\n\\t\\t\\t\\t\\t\\thdul.append(fits.ImageHDU(MF))\\n\\t\\t\\t\\t\\t\\thdul[EXT].header.set(\\\"NAXIS1\\\", np.shape(MF)[1])\\n\\t\\t\\t\\t\\t\\thdul[EXT].header.set(\\\"NAXIS2\\\", np.shape(MF)[0])\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"CALIBR\\\", \\\"T\\\")\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"INSTRUME\\\", \\\"MAIA\\\")\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"BINX\\\", i[0])\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"BINY\\\", i[1])\\n\\t\\t\\t\\t\\thdul[0].header.set(\\\"CALMODE\\\", \\\"MASTER FLAT\\\")\\n\\t\\t\\t\\t\\thdul.writeto(WD+\\\"MF.fits\\\", clobber=True)\\n\\t\\t\\t\\t\\tprint \\\"############################################################\\\"\\n\\tprint \\\"Completed master flats\\\"\",\n \"def _findFiles(self, inputfolder):\\n protofile, caffemodel = None, None\\n files = os.listdir(inputfolder)\\n for f in files:\\n name, ext = splitext(f)\\n if ext == '.caffemodel':\\n caffemodel = join(inputfolder, f)\\n elif f == 'deploy.prototxt':\\n protofile = join(inputfolder, f)\\n return protofile, caffemodel\",\n \"def main(root_dir=(join(Path(__file__).resolve().parents[1], 'data')), apply_masks=True):\\n\\n # Iterate over each volume in the root data directory\\n for folder_name in os.listdir(root_dir):\\n print(join(root_dir, folder_name))\\n if 'results' not in folder_name and 'subj' in folder_name:\\n\\n # Create all of the directories and subdirectories\\n create_train_test_val_dirs(join(root_dir, folder_name))\\n\\n # Populate the train, val, and test directories and their subdirectories\\n populate_train_test_val_dirs_nonrandomly(join(root_dir, folder_name),\\n val_ratio=0.00,\\n test_ratio=0.00,\\n preliminary_clahe=True,\\n apply_masks=apply_masks)\\n\\n if apply_masks:\\n # Apply masks to all of the images in this volume\\n apply_masks_to_volume(join(root_dir, folder_name))\\n\\n # Get and save the residuals between ClearImages and CoregisteredBlurryImages\\n create_and_populate_residual_dirs(join(root_dir, folder_name))\",\n \"def generate_genome_import_files(genbank_path, output_dir):\\n genbank = load_genbank(genbank_path)\\n genome_path = os.path.join(output_dir, _genome_vert_name + '.json')\\n write_import_file(generate_genome(genbank), genome_path)\\n gene_path = os.path.join(output_dir, genbank.id, _gene_vert_name + '.json')\\n write_import_file(generate_genes(genbank), gene_path)\\n gene_edge_path = os.path.join(output_dir, genbank.id, _gene_edge_name + '.json')\\n write_import_file(generate_gene_edges(genbank), gene_edge_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6848684","0.6842689","0.66581684","0.65295035","0.6394109","0.6241394","0.62080246","0.6191771","0.6087738","0.60208416","0.5983195","0.59366655","0.5929118","0.5920719","0.5885988","0.58807164","0.5872283","0.5867078","0.5824099","0.5782577","0.57666737","0.5722888","0.56962204","0.56616205","0.56566554","0.5621083","0.56126213","0.5597145","0.5577475","0.5570402","0.55627275","0.5528377","0.55039704","0.54837775","0.547456","0.54612726","0.5457609","0.54464394","0.54436547","0.5441416","0.5435527","0.5433664","0.54252404","0.54125893","0.539843","0.53972656","0.5396881","0.5394739","0.53886515","0.53848255","0.5383162","0.53722036","0.5367461","0.53665584","0.53638357","0.5358909","0.535608","0.53557724","0.53548694","0.53517705","0.53480506","0.5336603","0.5336005","0.53338337","0.5325856","0.53194374","0.53036016","0.52919257","0.5286383","0.5285372","0.527834","0.52773297","0.52704686","0.52693206","0.52686054","0.52636945","0.526151","0.525328","0.5242555","0.5241334","0.5240415","0.52382755","0.523368","0.5229418","0.52278596","0.5225254","0.520859","0.52069634","0.5206938","0.52036697","0.5201952","0.5201873","0.5194689","0.5189544","0.5185051","0.5184929","0.51840305","0.51794267","0.51735425","0.5164184"],"string":"[\n \"0.6848684\",\n \"0.6842689\",\n \"0.66581684\",\n \"0.65295035\",\n \"0.6394109\",\n \"0.6241394\",\n \"0.62080246\",\n \"0.6191771\",\n \"0.6087738\",\n \"0.60208416\",\n \"0.5983195\",\n \"0.59366655\",\n \"0.5929118\",\n \"0.5920719\",\n \"0.5885988\",\n \"0.58807164\",\n \"0.5872283\",\n \"0.5867078\",\n \"0.5824099\",\n \"0.5782577\",\n \"0.57666737\",\n \"0.5722888\",\n \"0.56962204\",\n \"0.56616205\",\n \"0.56566554\",\n \"0.5621083\",\n \"0.56126213\",\n \"0.5597145\",\n \"0.5577475\",\n \"0.5570402\",\n \"0.55627275\",\n \"0.5528377\",\n \"0.55039704\",\n \"0.54837775\",\n \"0.547456\",\n \"0.54612726\",\n \"0.5457609\",\n \"0.54464394\",\n \"0.54436547\",\n \"0.5441416\",\n \"0.5435527\",\n \"0.5433664\",\n \"0.54252404\",\n \"0.54125893\",\n \"0.539843\",\n \"0.53972656\",\n \"0.5396881\",\n \"0.5394739\",\n \"0.53886515\",\n \"0.53848255\",\n \"0.5383162\",\n \"0.53722036\",\n \"0.5367461\",\n \"0.53665584\",\n \"0.53638357\",\n \"0.5358909\",\n \"0.535608\",\n \"0.53557724\",\n \"0.53548694\",\n \"0.53517705\",\n \"0.53480506\",\n \"0.5336603\",\n \"0.5336005\",\n \"0.53338337\",\n \"0.5325856\",\n \"0.53194374\",\n \"0.53036016\",\n \"0.52919257\",\n \"0.5286383\",\n \"0.5285372\",\n \"0.527834\",\n \"0.52773297\",\n \"0.52704686\",\n \"0.52693206\",\n \"0.52686054\",\n \"0.52636945\",\n \"0.526151\",\n \"0.525328\",\n \"0.5242555\",\n \"0.5241334\",\n \"0.5240415\",\n \"0.52382755\",\n \"0.523368\",\n \"0.5229418\",\n \"0.52278596\",\n \"0.5225254\",\n \"0.520859\",\n \"0.52069634\",\n \"0.5206938\",\n \"0.52036697\",\n \"0.5201952\",\n \"0.5201873\",\n \"0.5194689\",\n \"0.5189544\",\n \"0.5185051\",\n \"0.5184929\",\n \"0.51840305\",\n \"0.51794267\",\n \"0.51735425\",\n \"0.5164184\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77105707"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11582,"cells":{"query":{"kind":"string","value":"Returns a duplicate of the profile instance."},"document":{"kind":"string","value":"def duplicate(self):\r\n duplicate = Profile()\r\n \r\n for i in self.__dict__:\r\n if type(getattr(self, i)) is dict:\r\n setattr(duplicate, i, getattr(self, i).copy())\r\n else:\r\n setattr(duplicate, i, getattr(self, i))\r\n\r\n return duplicate"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def copy(self):\n return Population(self)","def copy(self):\n return self.__class__(dict(self))","def copy(self):\n return self.__class__(self)","def copy(self):\n return self.__class__(self)","def strip_copy(self):\n return strip_profiles_copy(self)","def get_full_profile(self) -> Profile:\n return Profile(**{**self.profile, **self.contact})","def copy(self):\n out = type(self).__new__(self.__class__)\n out.__dict__.update(self.__dict__)\n # make sure the copy has its own unique random number generator\n seed_seq = self.rng._bit_generator._seed_seq.spawn(1)[0]\n out.__dict__['rng'] = get_generator(seed_seq)\n return out","def copy(self):\n new = object.__new__(type(self))\n new.avatar_hash = self.avatar_hash\n new.avatar_type = self.avatar_type\n new.boosts_since = self.boosts_since\n new.flags = self.flags\n new.joined_at = self.joined_at\n new.nick = self.nick\n new.pending = self.pending\n role_ids = self.role_ids\n if (role_ids is not None):\n role_ids = (*role_ids,)\n new.role_ids = role_ids\n new.timed_out_until = self.timed_out_until\n return new","def clone(self):\n return self.__class__(self, self.spectrum, wallet=self.wallet)","def copy(self):\n cls = self.__class__\n result = cls.__new__(cls)\n result.__dict__.update(self.__dict__)\n return result","def clone(self):\n return self.copy()","def clone(self):\n return self","def copy(self):\n new = self\n return new","def copy(self):\n return self.__class__(**vars(self))","def copy(self):\n return object.__new__(type(self))","def copy(self):\r\n return copy.copy(self)","def copy (self):\n return self.__class__(self.name, self[:])","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n return self.__copy__()","def copy(self):\n\t\ttemp = self.__class__()\n\t\ttemp.copy_from(self)\n\t\treturn temp","def copy(self):\n return copy(self)","def copy(self):\n return copy(self)","def __copy__(self):\n return self.copy()","def copy(self):\n return self.mutate().simple_copy()","def copy(self):\n return self.__class__(self.value, self.is_cloud)","def copy(self):\n return super().copy()","def clone(self) -> Any:\n clone = super().clone()\n clone.clear()\n return clone","def copy(self):\n return self.from_dict(self.to_dict(True))","def clone(self):\n return deepcopy(self)","def clone(self):\n return deepcopy(self)","def clone(self):\n return deepcopy(self)","def clone(self):\n return deepcopy(self)","def clone(self):\n return self.__class__(self.name, *self)","def clone(self):\n from copy import deepcopy\n return deepcopy(self)","def _copy_(self):\n return copy.copy(self)","def copy(self):\n new = object.__new__(type(self))\n new.approximate_online_count = self.approximate_online_count\n new.approximate_user_count = self.approximate_user_count\n new.description = self.description\n new.discovery_splash_hash = self.discovery_splash_hash\n new.discovery_splash_type = self.discovery_splash_type\n new.emojis = self.emojis.copy()\n features = self.features\n if (features is not None):\n features = (*features,)\n new.features = features\n new.icon_hash = self.icon_hash\n new.icon_type = self.icon_type\n new.id = self.id\n new.invite_splash_hash = self.invite_splash_hash\n new.invite_splash_type = self.invite_splash_type\n new.stickers = self.stickers.copy()\n new.name = self.name\n return new","def copy (self):\n import copy\n return copy.copy(self)","def clone(self):\n return None","def copy(self):\r\n return copy.deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n return deepcopy(self)","def copy(self):\n try:\n return self.__class__(self, copy=True)\n except TypeError:\n new = self.__class__(copy.deepcopy(self))\n return new","def clone(self):\n memo = dict()\n c = self._clone(memo)\n c._clone_rip(memo)\n return c","def duplicate(self):\n\t\treturn Graph(self.vertices[:], self.edges[:])","def copy(self):\n copied = super().copy()\n copied.anonymize()\n return copied","def __deepcopy__(self, memodict=None):\n return self.copy()","def duplicate(self, delayed):\n return self.__class__(delayed)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n newVertices = [v.copy() for v in self.vertices]\n return face(newVertices)","def deepcopy(self):\n return self.copy()","def copy_with(self):\n return self.copy()","def copy(self):\n return properties.copy(self)","def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)","def copy(self):\n copy = self.__class__()\n copy.a = self.a\n copy.b = self.b\n copy.peak = self.peak\n copy.orientation = self.orientation\n copy.i = self.i\n copy.coords = self.coords.copy()\n return copy","def clone(self):\n return copy.deepcopy(self)","def clone(self):\n return type(self)(self.endpoint)","def clone(self):\n return type(self)(iterator=self)","def duplicate(self):\n return Sample(self.solutes, self.quantities, self.volume, self.endless)","def copy(self):\n \n return deepcopy(self)","def clone(self) -> Any:\n return cp.copy(self)","def copy(self):\n\t\treturn pythoncopy.deepcopy(self)","def copy(self):\n from copy import deepcopy\n return deepcopy(self)","def copy(self):\n return self._new_rep(self._func(self.rep))","def profile(self) -> Profile:\n return self._profile","def profile(self) -> Profile:\n return self._profile","def copy(self):\n new_game = Game(self.name, *self.agents, independent_update=self.independent_update, default_run_kwargs=self.default_run_kwargs, _set_defaults=False)\n new_game.i = self.i\n new_game.env = self.env_copy()\n new_game.env[\"game\"] = new_game\n return new_game","def clone(self):\n return shallow_clone(self)"],"string":"[\n \"def copy(self):\\n return Population(self)\",\n \"def copy(self):\\n return self.__class__(dict(self))\",\n \"def copy(self):\\n return self.__class__(self)\",\n \"def copy(self):\\n return self.__class__(self)\",\n \"def strip_copy(self):\\n return strip_profiles_copy(self)\",\n \"def get_full_profile(self) -> Profile:\\n return Profile(**{**self.profile, **self.contact})\",\n \"def copy(self):\\n out = type(self).__new__(self.__class__)\\n out.__dict__.update(self.__dict__)\\n # make sure the copy has its own unique random number generator\\n seed_seq = self.rng._bit_generator._seed_seq.spawn(1)[0]\\n out.__dict__['rng'] = get_generator(seed_seq)\\n return out\",\n \"def copy(self):\\n new = object.__new__(type(self))\\n new.avatar_hash = self.avatar_hash\\n new.avatar_type = self.avatar_type\\n new.boosts_since = self.boosts_since\\n new.flags = self.flags\\n new.joined_at = self.joined_at\\n new.nick = self.nick\\n new.pending = self.pending\\n role_ids = self.role_ids\\n if (role_ids is not None):\\n role_ids = (*role_ids,)\\n new.role_ids = role_ids\\n new.timed_out_until = self.timed_out_until\\n return new\",\n \"def clone(self):\\n return self.__class__(self, self.spectrum, wallet=self.wallet)\",\n \"def copy(self):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n result.__dict__.update(self.__dict__)\\n return result\",\n \"def clone(self):\\n return self.copy()\",\n \"def clone(self):\\n return self\",\n \"def copy(self):\\n new = self\\n return new\",\n \"def copy(self):\\n return self.__class__(**vars(self))\",\n \"def copy(self):\\n return object.__new__(type(self))\",\n \"def copy(self):\\r\\n return copy.copy(self)\",\n \"def copy (self):\\n return self.__class__(self.name, self[:])\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n return self.__copy__()\",\n \"def copy(self):\\n\\t\\ttemp = self.__class__()\\n\\t\\ttemp.copy_from(self)\\n\\t\\treturn temp\",\n \"def copy(self):\\n return copy(self)\",\n \"def copy(self):\\n return copy(self)\",\n \"def __copy__(self):\\n return self.copy()\",\n \"def copy(self):\\n return self.mutate().simple_copy()\",\n \"def copy(self):\\n return self.__class__(self.value, self.is_cloud)\",\n \"def copy(self):\\n return super().copy()\",\n \"def clone(self) -> Any:\\n clone = super().clone()\\n clone.clear()\\n return clone\",\n \"def copy(self):\\n return self.from_dict(self.to_dict(True))\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return deepcopy(self)\",\n \"def clone(self):\\n return self.__class__(self.name, *self)\",\n \"def clone(self):\\n from copy import deepcopy\\n return deepcopy(self)\",\n \"def _copy_(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n new = object.__new__(type(self))\\n new.approximate_online_count = self.approximate_online_count\\n new.approximate_user_count = self.approximate_user_count\\n new.description = self.description\\n new.discovery_splash_hash = self.discovery_splash_hash\\n new.discovery_splash_type = self.discovery_splash_type\\n new.emojis = self.emojis.copy()\\n features = self.features\\n if (features is not None):\\n features = (*features,)\\n new.features = features\\n new.icon_hash = self.icon_hash\\n new.icon_type = self.icon_type\\n new.id = self.id\\n new.invite_splash_hash = self.invite_splash_hash\\n new.invite_splash_type = self.invite_splash_type\\n new.stickers = self.stickers.copy()\\n new.name = self.name\\n return new\",\n \"def copy (self):\\n import copy\\n return copy.copy(self)\",\n \"def clone(self):\\n return None\",\n \"def copy(self):\\r\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n return deepcopy(self)\",\n \"def copy(self):\\n try:\\n return self.__class__(self, copy=True)\\n except TypeError:\\n new = self.__class__(copy.deepcopy(self))\\n return new\",\n \"def clone(self):\\n memo = dict()\\n c = self._clone(memo)\\n c._clone_rip(memo)\\n return c\",\n \"def duplicate(self):\\n\\t\\treturn Graph(self.vertices[:], self.edges[:])\",\n \"def copy(self):\\n copied = super().copy()\\n copied.anonymize()\\n return copied\",\n \"def __deepcopy__(self, memodict=None):\\n return self.copy()\",\n \"def duplicate(self, delayed):\\n return self.__class__(delayed)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n newVertices = [v.copy() for v in self.vertices]\\n return face(newVertices)\",\n \"def deepcopy(self):\\n return self.copy()\",\n \"def copy_with(self):\\n return self.copy()\",\n \"def copy(self):\\n return properties.copy(self)\",\n \"def copy(self):\\n import copy as pcopy\\n return pcopy.deepcopy(self)\",\n \"def copy(self):\\n copy = self.__class__()\\n copy.a = self.a\\n copy.b = self.b\\n copy.peak = self.peak\\n copy.orientation = self.orientation\\n copy.i = self.i\\n copy.coords = self.coords.copy()\\n return copy\",\n \"def clone(self):\\n return copy.deepcopy(self)\",\n \"def clone(self):\\n return type(self)(self.endpoint)\",\n \"def clone(self):\\n return type(self)(iterator=self)\",\n \"def duplicate(self):\\n return Sample(self.solutes, self.quantities, self.volume, self.endless)\",\n \"def copy(self):\\n \\n return deepcopy(self)\",\n \"def clone(self) -> Any:\\n return cp.copy(self)\",\n \"def copy(self):\\n\\t\\treturn pythoncopy.deepcopy(self)\",\n \"def copy(self):\\n from copy import deepcopy\\n return deepcopy(self)\",\n \"def copy(self):\\n return self._new_rep(self._func(self.rep))\",\n \"def profile(self) -> Profile:\\n return self._profile\",\n \"def profile(self) -> Profile:\\n return self._profile\",\n \"def copy(self):\\n new_game = Game(self.name, *self.agents, independent_update=self.independent_update, default_run_kwargs=self.default_run_kwargs, _set_defaults=False)\\n new_game.i = self.i\\n new_game.env = self.env_copy()\\n new_game.env[\\\"game\\\"] = new_game\\n return new_game\",\n \"def clone(self):\\n return shallow_clone(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65038234","0.6457992","0.6320341","0.6320341","0.6286995","0.62803096","0.6278374","0.62589824","0.6257265","0.62398297","0.62353045","0.6209959","0.6207495","0.61983556","0.61954135","0.61781174","0.61590487","0.6154005","0.6154005","0.6154005","0.61380666","0.61380666","0.61380666","0.61380666","0.613654","0.6103244","0.6103244","0.6096617","0.6070034","0.6063737","0.60629463","0.6054787","0.6053637","0.60532707","0.60532707","0.60532707","0.60532707","0.6049554","0.60322404","0.60295635","0.6024987","0.6014413","0.60063976","0.5995692","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.59925264","0.599058","0.5981701","0.5972876","0.5967541","0.59673524","0.596351","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59603465","0.59569615","0.595383","0.595321","0.5926801","0.5925133","0.5917403","0.5913789","0.59082264","0.5907798","0.59066105","0.59021103","0.5901536","0.58992374","0.5892069","0.5890004","0.5888038","0.5888038","0.588355","0.5880833"],"string":"[\n \"0.65038234\",\n \"0.6457992\",\n \"0.6320341\",\n \"0.6320341\",\n \"0.6286995\",\n \"0.62803096\",\n \"0.6278374\",\n \"0.62589824\",\n \"0.6257265\",\n \"0.62398297\",\n \"0.62353045\",\n \"0.6209959\",\n \"0.6207495\",\n \"0.61983556\",\n \"0.61954135\",\n \"0.61781174\",\n \"0.61590487\",\n \"0.6154005\",\n \"0.6154005\",\n \"0.6154005\",\n \"0.61380666\",\n \"0.61380666\",\n \"0.61380666\",\n \"0.61380666\",\n \"0.613654\",\n \"0.6103244\",\n \"0.6103244\",\n \"0.6096617\",\n \"0.6070034\",\n \"0.6063737\",\n \"0.60629463\",\n \"0.6054787\",\n \"0.6053637\",\n \"0.60532707\",\n \"0.60532707\",\n \"0.60532707\",\n \"0.60532707\",\n \"0.6049554\",\n \"0.60322404\",\n \"0.60295635\",\n \"0.6024987\",\n \"0.6014413\",\n \"0.60063976\",\n \"0.5995692\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.59925264\",\n \"0.599058\",\n \"0.5981701\",\n \"0.5972876\",\n \"0.5967541\",\n \"0.59673524\",\n \"0.596351\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59603465\",\n \"0.59569615\",\n \"0.595383\",\n \"0.595321\",\n \"0.5926801\",\n \"0.5925133\",\n \"0.5917403\",\n \"0.5913789\",\n \"0.59082264\",\n \"0.5907798\",\n \"0.59066105\",\n \"0.59021103\",\n \"0.5901536\",\n \"0.58992374\",\n \"0.5892069\",\n \"0.5890004\",\n \"0.5888038\",\n \"0.5888038\",\n \"0.588355\",\n \"0.5880833\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81918967"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11583,"cells":{"query":{"kind":"string","value":"To save this profile intance to xml file using a XmlWriter. xwriter>should be a XmlWriter instance."},"document":{"kind":"string","value":"def save_to_xml(self, xwriter):\r\n\r\n xwriter.WriteStartElement(\"Profile\")\r\n xwriter.WriteAttributeString(\"Name\", self.Name)\r\n xwriter.WriteStartAttribute(\"Version\")\r\n xwriter.WriteValue(self.Version)\r\n xwriter.WriteEndAttribute()\r\n\r\n for var_name in self.__dict__:\r\n var_type = type(getattr(self, var_name))\r\n\r\n if var_type is str and var_name != \"Name\":\r\n self.write_string_to_xml(var_name, xwriter)\r\n\r\n elif var_type is bool:\r\n self.write_bool_to_xml(var_name, xwriter)\r\n\r\n elif var_type is dict:\r\n self.write_dict_to_xml(var_name, xwriter)\r\n\r\n elif var_type is list and var_name != \"ExcludeRules\":\r\n self.write_list_to_xml(var_name, xwriter)\r\n\r\n xwriter.WriteStartElement(\"ExcludeRules\")\r\n xwriter.WriteAttributeString(\"Operator\", self.ExcludeOperator)\r\n xwriter.WriteAttributeString(\"ExcludeMode\", self.ExcludeMode)\r\n for rule in self.ExcludeRules:\r\n if rule:\r\n rule.save_xml(xwriter)\r\n xwriter.WriteEndElement()\r\n \r\n xwriter.WriteEndElement()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\r\n if lastused:\r\n writer.WriteAttributeString(\"LastUsed\", \",\".join(lastused))\r\n for profile in profiles:\r\n profiles[profile].save_to_xml(writer)\r\n writer.WriteEndElement()\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def saveSessionToXML(self, filename):\r\n xmlStr = self.createXMLStr()\r\n \r\n #Write to the file\r\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\r\n xmlFile = open(filename, 'w')\r\n xmlFile.write(xmlStr)\r\n xmlFile.close()","def saving_file(xml):\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))","def write_xml(self, xmlfile):\n system.xml.write_file(xmlfile, self.status, 'status')","def xmlWrite(self, xmlWriter, font, value, name, attrs):\n raise NotImplementedError(self)","def saveToXml(self) -> org.jdom.Element:\n ...","def save_profile(self, dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"wb\") as f:\n pickle.dump(self, f)","def save(self, filename=None):\n f = filename if filename else self.path\n etree.register_namespace('', TEI)\n etree.register_namespace('mith', MITH)\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')","def to_xml_file(self, xml_file_path):\n s = self.to_xml()\n with open(xml_file_path, \"w+b\") as f:\n f.write(s)","def save_profile(self):\n self.save()","def xmlwrite(self, doc, filename):\n pathname = os.path.join(self.session.session_dir, filename)\n f = open(pathname, \"w\")\n doc.writexml(writer=f, indent=\"\", addindent=\" \", newl=\"\\n\", encoding=\"UTF-8\")\n f.close()","def save(self, save_path=None):\n if self._xml is None:\n raise IOError(\"There's nothing to save\")\n\n path = self._path_to_xml if save_path is None else save_path\n\n with open(path, 'w') as f:\n rough_string = Et.tostring(self._xml, 'utf-8')\n par = etree.XMLParser(remove_blank_text=True)\n elem = etree.XML(rough_string, parser=par)\n parsed = minidom.parseString(etree.tostring(elem))\n f.write(parsed.toprettyxml(indent=\" \"))","def save(self, pretty=True):\n self.endInstance()\n if pretty:\n _indent(self.root, whitespace=self._whiteSpace)\n tree = ET.ElementTree(self.root)\n tree.write(self.path, encoding=\"utf-8\", method='xml', xml_declaration=True)\n if self.logger:\n self.logger.info(\"Writing %s\", self.path)","def save_xml(self, filename):\n if \".xml\" not in filename:\n filename = filename + \".xml\"\n\n shutil.copyfile(self.env.model_file, filename)","def Save_xml(self, accounts):\n try:\n\n self.extension = \".xml\"\n\n colors.info(\"Saving as XML in {}{}\".format(self.file, self.extension))\n\n Main = ET.Element(\"SpotCheck\")\n\n SpotifyFree = ET.SubElement(Main, 'SpotifyFree')\n SpotifyPremium = ET.SubElement(Main, 'SpotifyPremium')\n PremiumFamily = ET.SubElement(Main, 'PremiumFamily')\n AdminPremiumFamily = ET.SubElement(Main, 'AdminPremiumFamily')\n BadAccounts = ET.SubElement(Main, 'BadAccounts')\n\n for account in accounts:\n if account.get(\"account_login\") == \"error\":\n temp = ET.SubElement(BadAccounts, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n else:\n if account.get(\"AccountType\") == \"Spotify Free\":\n temp = ET.SubElement(SpotifyFree, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n elif account.get(\"AccountType\") == \"Spotify Premium\":\n temp = ET.SubElement(SpotifyPremium, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n elif account.get(\"AccountType\") == \"Premium Family\":\n if account.get(\"Admin\"):\n temp = ET.SubElement(AdminPremiumFamily, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n else:\n temp = ET.SubElement(PremiumFamily, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n XML = ET.tostring(Main)\n with open(self.file + self.extension, \"w\") as output_:\n output_.write(XML)\n colors.correct(\"Done! All saved successfully\")\n except Exception as e:\n colors.error(str(e))\n _exit(1)","def saveXML(self, filename):\n root = ET.Element('root')\n pklot = ET.SubElement(root, \"ParkingLot\")\n\n lotname = ET.SubElement(pklot, \"LotName\", name=str(self.name))\n idCounter = ET.SubElement(pklot, \"NextAvailableID\", counter=str(self.spotIDCounter))\n for spot in self.parkingSpots:\n ET.SubElement(pklot, 'Spot', id=str(spot.id), location=' '.join(str(x) for x in spot.location))\n\n tree = ET.ElementTree(root)\n tree.write(filename)","def save_as(self, fname, base = None, indent = '', topns = True, namespaces = {}):\n with codecs.open(fname, \"w\", encoding=\"utf-8\") as outf:\n self.serialize_xml(outf.write, base=base, indent=indent, topns=topns, namespaces=namespaces)","def save(self, filename):\n o = open(filename, 'w')\n o.write(self.write())\n o.close()","def export_to_file(self, filename):\n if len(filename.split(\".\")) == 1:\n filename += \".xml\"\n xmlstring = self._dommodel.toprettyxml(\" \", \"\\n\")\n with open(filename, \"w\") as f:\n f.write(xmlstring)","def write_to_xml(filename, xmlelement):\n xmlpretty = prettify(xmlelement)\n with open(filename, 'w') as f:\n f.write(xmlpretty)","def saveState(self, file):\n state = self.context.getState(getPositions=True, getVelocities=True, getParameters=True, getIntegratorParameters=True)\n xml = mm.XmlSerializer.serialize(state)\n if isinstance(file, str):\n with open(file, 'w') as f:\n f.write(xml)\n else:\n file.write(xml)","def write_xosc(self, generated_xml):\n reparsed_xml = minidom.parseString(generated_xml).toprettyxml(indent=\" \")\n xosc_file = open(self._filepath, \"w\")\n xosc_file.write(reparsed_xml)\n xosc_file.close()\n\n msg = QMessageBox()\n if self._warning_message:\n msg.setIcon(QMessageBox.Warning)\n text = f\"Exported OpenSCENARIO file {self._filepath} has warnings!\\n\\n\"\n text += \"\\n\".join(self._warning_message)\n else:\n msg.setIcon(QMessageBox.Information)\n text = f\"Successfully exported OpenSCENARIO file to {self._filepath}\"\n msg.setText(text)\n msg.setWindowTitle(\"OpenSCENARIO Export\")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.exec()","def _create_xml_report(self, test, xml_obj):\n xml_report_path = os.path.join(test.work_dir,\n self.XML_REPORT_PATH)\n with open(xml_report_path, 'w') as xml_report:\n xml_report.write(etree.tostring(xml_obj, pretty_print=True))","def save(self, fname):\n pass","def write(self, file_or_filename):\n etMap = revert(self)\n xmlTree = ET.ElementTree(etMap)\n xmlTree.write(file_or_filename)","def Save_Current_Profile(self):\r\n #name = tkFileDialog.asksaveasfilename()\r\n #if( name == \"\" ):\r\n # return\r\n #self.system.Save_Current_Profile(name)\r\n self.system.Save_Current_Profile()","def saveFile(self, filename):\n ret = libxml2mod.xmlSaveFile(filename, self._o)\n return ret","def save(self, filename):\n with open(filename, \"w\") as fp:\n dump(self, fp)","def write(self):\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\"UTF-8\")\n with open(self.xml_file, 'w') as f:\n f.write(temp_string)\n # f = open(self.xml_file, \"w\")\n # f.write(temp_string)\n # f.close()","def save(self, save_dir):\n ProfileManager.save_data_to_disk(self.player_data, path.join(save_dir, self.player_name + '.yaml'))","def save(self, fp):\n fp.write(self.dump())","def save_file(self):\n if self.select_path.text() != \"\":\n filepath = self.select_path.text()\n road_network = self.map_selection.currentText()\n if self.map_selection.currentText() == \"User Defined\":\n road_network = self.map_selection_user_defined.text()\n gen_xml = GenerateXML(filepath, road_network)\n gen_xml.main()\n # remember Road Network for future\n set_metadata(road_network_filepath=road_network)\n else:\n message = \"No export path was selected\"\n iface.messageBar().pushMessage(\"Warning\", message, level=Qgis.Warning)\n QgsMessageLog.logMessage(message, level=Qgis.Warning)","def writeToTempXml(self):\n name = self.fileToProcess.name\n all_tokens = ET.Element(\"tokens\")\n for token in self.tokensTable:\n if token.getType() == KEYWORD:\n keyword = ET.SubElement(all_tokens, \"keyword\")\n keyword.text = ' '+token.getValue()+' '\n elif token.getType() == IDENTIFIER:\n identifier = ET.SubElement(all_tokens, \"identifier\")\n identifier.text = ' '+token.getValue()+' '\n elif token.getType() == SYMBOL:\n symbol = ET.SubElement(all_tokens, \"symbol\")\n symbol.text = ' '+token.getValue()+' '\n elif token.getType() == STRING_CONST:\n stringConstant = ET.SubElement(all_tokens, \"stringConstant\")\n stringConstant.text = ' '+token.getValue()+' '\n elif token.getType() == INT_CONST:\n integerConstant = ET.SubElement(all_tokens, \"integerConstant\")\n integerConstant.text = ' '+token.getValue()+' '\n tree = ET.ElementTree(all_tokens)\n tree.write(name + 'T' + '.xml')","def save(self, filename):\n pass","def save_calibration_profile(self, filename: str) -> None:\n pass","def _save_helper(self, filepath, data_dict):\n # Set Default filepath\n if filepath is None:\n filepath = \"profile-{}.pkl\".format(\n datetime.now().strftime(\"%d-%b-%Y-%H:%M:%S.%f\"))\n\n # Remove data labelers as they can't be pickled\n data_labelers = self._remove_data_labelers()\n\n # add profiler class to data_dict\n data_dict['profiler_class'] = self.__class__.__name__\n\n # Pickle and save profile to disk\n with open(filepath, \"wb\") as outfile:\n pickle.dump(data_dict, outfile)\n\n # Restore all data labelers\n self._restore_data_labelers(data_labelers)","def save(self,file):\n\n with open(file,\"w\") as f:\n f.write(self.to_string())","def save(self):\n # TODO: save the file","def save_elem_file(self, output):\n with open(output, 'wb') as fid:\n self._write_elem_header(fid)\n self._write_nodes(fid)\n self._write_elements(fid)\n self._write_neighbors(fid)","def save(self):\n self.lock.acquire()\n try:\n self.xml.set(\"name\",self.name)\n self.xml.set(\"room\",self.room)\n self.xml.set(\"type\",self.type)\n self.xml.find(\"address\").text = \":\".join([str(x) for x in self.address])\n if self.pos is not None:\n self.xml.find(\"pos\").text = \" \".join([str(x) for x in self.pos])\n self.xml.find(\"icon\").text = self.icon\n \n finally:\n self.lock.release()\n \n self.house.save_devices()","def save(self, filename: str):\n dump(self, filename)","def write(self, filename):\n \n return self.model.write(filename,xml_declaration=True, encoding='utf-8')","def save_current_to_profile(self, profile_name, prof_desc='', prof_path='',\n self_contained=False):\n # Open the already existing profile\n new_profile = profile(profile_name, workdir=os.path.dirname(prof_path))\n\n # shortcut\n w3af_plugins = self._w3af_core.plugins\n\n # Save the enabled plugins\n for plugin_type in w3af_plugins.get_plugin_types():\n enabled_plugins = []\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\n enabled_plugins.append(plugin_name)\n new_profile.set_enabled_plugins(plugin_type, enabled_plugins)\n\n # Save the plugin options\n for plugin_type in w3af_plugins.get_plugin_types():\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\n plugin_options = w3af_plugins.get_plugin_options(plugin_type,\n plugin_name)\n if plugin_options:\n new_profile.set_plugin_options(plugin_type,\n plugin_name,\n plugin_options,\n self_contained=self_contained)\n\n # Save the profile targets\n targets = cf.cf.get('targets')\n if targets:\n new_profile.set_target(' , '.join(t.url_string for t in targets))\n\n # Save the misc and http settings\n misc_settings = MiscSettings()\n new_profile.set_misc_settings(misc_settings.get_options())\n new_profile.set_http_settings(\n self._w3af_core.uri_opener.settings.get_options())\n\n # Save the profile name and description\n new_profile.set_desc(prof_desc)\n new_profile.set_name(profile_name)\n\n # Save the profile to the file\n new_profile.save(profile_name)\n\n return new_profile","def get_xml(self):\n profile = self.profile\n version = self.version\n #self.attribs['xmlns'] = \"http://www.w3.org/2000/svg\"\n self.attribs['xmlns:xlink'] = \"http://www.w3.org/1999/xlink\"\n self.attribs['xmlns:ev'] = \"http://www.w3.org/2001/xml-events\"\n\n self.attribs['baseProfile'] = profile\n self.attribs['version'] = version\n return super(Drawing, self).get_xml()","def writeProfile(fname,prof):\n t = np.linspace(0,1,prof.shape[0],endpoint=False)\n fh = open(fname,'w')\n for x in range(prof.shape[0]):\n fh.write('%.7e %.7e\\n' % (t[x],prof[x]))\n fh.close()","def save_profile(sender, instance, **kwargs):\n instance.profile.save()","def save_xml_file():\n global output_on_display, import_lst, column_names, data\n if data_base == '':\n mistake_load_table()\n else:\n column_names = data[0]\n step = len(column_names)\n\n save_name = asksaveasfilename(title=\"Select file\", filetypes=((\"XML\", \"*.xml\"), (\"all files\", \"*.*\")),\n confirmoverwrite=True, defaultextension='.xml')\n data = import_lst\n\n if len(data[0]) == step:\n pass\n else:\n data = import_lst[step::]\n\n data2 = list(map(list, zip(*data)))\n\n data3 = {key: value for key, value in zip(column_names, data2)}\n\n column = list(data3.keys())\n\n df = pd.DataFrame(data3, columns=column)\n\n data_dict = df.to_dict(orient=\"records\")\n with open('output.json', \"w+\") as f:\n json.dump(data_dict, f, indent=4)\n\n xml_data = dicttoxml(data_dict).decode()\n with open(save_name, \"w+\") as f:\n f.write(xml_data)\n\n data.clear()\n data2.clear()\n data3.clear()","def save(self, filename:str):\n dump(self, filename=filename)","def SaveWiredNetworkProfile(self, profilename):\n def write_script_ent(prof, conf, script):\n if not conf.has_option(prof, script):\n conf.set(prof, script, None)\n if profilename == \"\":\n return \"500: Bad Profile name\"\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n config.remove_section(profilename)\n config.add_section(profilename)\n for x in self.WiredNetwork:\n config.set(profilename, x, self.WiredNetwork[x])\n write_script_ent(profilename, config, \"beforescript\")\n write_script_ent(profilename, config, \"afterscript\")\n write_script_ent(profilename, config, \"disconnectscript\")\n config.write(open(self.wired_conf, \"w\"))\n return \"100: Profile Written\"","def save_profiles(self, fout, save_hybrid_meta=True):\n\n self._init_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\n self._write_h5_out(fout, save_hybrid_meta=save_hybrid_meta)","def Write_XML(gui): \n # lock buttons\n gui.action_lock('Lock', gui.save_button)\n \n # clear output\n gui.output_clear() \n \n # get the desired delay from the gui.\n delay_time = gui.get_delay()\n \n \n # get the desired ascii delay from the gui.\n ascii_time = gui.get_ascii_delay()\n \n \n # get the desired I2C address from the gui.\n addr = \"0x%X\" % gui.get_i2c_address()\n \n \n # get the list of commands from the gui\n command_list = gui.get_command_list()\n \n # wrap up the writing directives\n directives = pySCPI_config.write_directives(command_list, addr,\n delay_time, ascii_time)\n \n # create the xml file\n filename = create_XML(directives, gui)\n \n # update the filename display window to show the filename saved\n gui.update_filename(filename = filename) \n \n # unlock the buttons\n gui.action_lock('Unlock')","def _make_wlan_profile_xml(ssid, auth_method, encrypt_method, key_type = \"\", key_material = \"\", key_index = \"\", use_onex = False):\n\n # Try to generate a temporary file for storing the XML profile\n fd, path = tempfile.mkstemp(\".xml\")\n\n # Fill the content\n os.write(fd, '<?xml version=\"1.0\"?>\\n')\n os.write(fd, '<WLANProfile xmlns=\"http://www.microsoft.com/networking/WLAN/profile/v1\">\\n')\n os.write(fd, ' <name>' + ssid + '</name>\\n')\n os.write(fd, ' <SSIDConfig>\\n')\n os.write(fd, ' <SSID>\\n')\n os.write(fd, ' <name>' + ssid + '</name>\\n')\n os.write(fd, ' </SSID>\\n')\n os.write(fd, ' </SSIDConfig>\\n')\n os.write(fd, ' <connectionType>ESS</connectionType>\\n')\n os.write(fd, ' <MSM>\\n')\n os.write(fd, ' <security>\\n')\n os.write(fd, ' <authEncryption>\\n')\n os.write(fd, ' <authentication>' + auth_method + '</authentication>\\n')\n os.write(fd, ' <encryption>' + encrypt_method + '</encryption>\\n')\n if use_onex:\n os.write(fd, ' <useOneX>true</useOneX>\\n')\n else:\n os.write(fd, ' <useOneX>false</useOneX>\\n')\n os.write(fd, ' </authEncryption>\\n')\n\n if not use_onex:\n # 802.1x is not applied\n if len(key_type) > 0:\n os.write(fd, ' <sharedKey>\\n')\n os.write(fd, ' <keyType>' + key_type + '</keyType>\\n')\n os.write(fd, ' <protected>false</protected>\\n')\n os.write(fd, ' <keyMaterial>' + key_material + '</keyMaterial>\\n')\n os.write(fd, ' </sharedKey>\\n')\n\n if key_index:\n os.write(fd, ' <keyIndex>' + str(int(key_index) - 1) + '</keyIndex>\\n')\n\n\n else:\n # 802.1x is applied\n os.write(fd, ' <OneX xmlns=\"http://www.microsoft.com/networking/OneX/v1\">\\n')\n os.write(fd, ' <EAPConfig>\\n')\n os.write(fd, ' <EapHostConfig xmlns=\"http://www.microsoft.com/provisioning/EapHostConfig\" \\n')\n os.write(fd, ' xmlns:eapCommon=\"http://www.microsoft.com/provisioning/EapCommon\" \\n')\n os.write(fd, ' xmlns:baseEap=\"http://www.microsoft.com/provisioning/BaseEapMethodConfig\">\\n')\n os.write(fd, ' <EapMethod>\\n')\n os.write(fd, ' <eapCommon:Type>25</eapCommon:Type>\\n')\n os.write(fd, ' <eapCommon:AuthorId>0</eapCommon:AuthorId>\\n')\n os.write(fd, ' </EapMethod>\\n')\n os.write(fd, ' <Config xmlns:baseEap=\"http://www.microsoft.com/provisioning/BaseEapConnectionPropertiesV1\" \\n')\n os.write(fd, ' xmlns:msPeap=\"http://www.microsoft.com/provisioning/MsPeapConnectionPropertiesV1\" \\n')\n os.write(fd, ' xmlns:msChapV2=\"http://www.microsoft.com/provisioning/MsChapV2ConnectionPropertiesV1\">\\n')\n os.write(fd, ' <baseEap:Eap>\\n')\n os.write(fd, ' <baseEap:Type>25</baseEap:Type>\\n')\n os.write(fd, ' <msPeap:EapType>\\n')\n os.write(fd, ' <msPeap:FastReconnect>false</msPeap:FastReconnect>\\n')\n os.write(fd, ' <msPeap:InnerEapOptional>0</msPeap:InnerEapOptional>\\n')\n os.write(fd, ' <baseEap:Eap>\\n')\n os.write(fd, ' <baseEap:Type>26</baseEap:Type>\\n')\n os.write(fd, ' <msChapV2:EapType>\\n')\n os.write(fd, ' <msChapV2:UseWinLogonCredentials>false</msChapV2:UseWinLogonCredentials>\\n')\n os.write(fd, ' </msChapV2:EapType>\\n')\n os.write(fd, ' </baseEap:Eap>\\n')\n os.write(fd, ' <msPeap:EnableQuarantineChecks>false</msPeap:EnableQuarantineChecks>\\n')\n os.write(fd, ' <msPeap:RequireCryptoBinding>false</msPeap:RequireCryptoBinding>\\n')\n os.write(fd, ' <msPeap:PeapExtensions />\\n')\n os.write(fd, ' </msPeap:EapType>\\n')\n os.write(fd, ' </baseEap:Eap>\\n')\n os.write(fd, ' </Config>\\n')\n os.write(fd, ' </EapHostConfig>\\n')\n os.write(fd, ' </EAPConfig>\\n')\n os.write(fd, ' </OneX>\\n')\n # End of if use_onex\n\n os.write(fd, ' </security>\\n')\n os.write(fd, ' </MSM>\\n')\n os.write(fd, '</WLANProfile>')\n\n os.close(fd)\n return path","def serialize(self,writer: SerializationWriter) -> None:\n if not writer:\n raise TypeError(\"writer cannot be null.\")\n writer.write_object_value(\"attackSimulationUser\", self.attack_simulation_user)\n writer.write_int_value(\"clickCount\", self.click_count)\n writer.write_int_value(\"compromisedCount\", self.compromised_count)\n writer.write_datetime_value(\"latestSimulationDateTime\", self.latest_simulation_date_time)\n writer.write_str_value(\"@odata.type\", self.odata_type)\n writer.write_int_value(\"simulationCount\", self.simulation_count)\n writer.write_additional_data_value(self.additional_data)","def save(self, x):\n self.print_handler_f(x, x.get_full_path())","def write_output_file(self, xml_text, xml_file):\n xml_fo = open(xml_file, 'w')\n xml_fo.write(xml_text+'</xml>')\n xml_fo.close()\n return","def to_file(self, file_path, smirnoff_data):\n xml_string = self.to_string(smirnoff_data)\n with open(file_path, \"w\") as of:\n of.write(xml_string)","def save_model(self, fname, pretty=False):\n with open(fname, \"w\") as f:\n xml_str = ET.tostring(self.root, encoding=\"unicode\")\n if pretty:\n parsed_xml = xml.dom.minidom.parseString(xml_str)\n xml_str = parsed_xml.toprettyxml(newl=\"\")\n f.write(xml_str)","def recordStatGraph(g, path):\n g.write(path, xml_declaration=True, encoding='utf-8', method='xml')","def save(self, filename):\n raise NotImplementedError","def save_drill_to_goalie_profile(self):\n\n goalie_path = str(Path.home())+\"/Documents/ball_e_profiles/goalie_profiles/{goalie_name}/{goalie_name}.csv\".format(\n goalie_name=self.goalie_name)\n with open(goalie_path, 'a+', newline='') as file:\n csv_writer = csv.writer(file, delimiter=\",\")\n # Row written as \"Drill Name, MM/DD/YYYY\"\n drill_info = [\"{}\".format(self.drill_name.replace(\"_\", \" \").title()), \"{}\".format(\n datetime.datetime.today().strftime(\"%m/%d/%Y\"))]\n csv_writer.writerow(drill_info)","def apply(self):\r\n\r\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\r\n\r\n def saving_file(xml):\r\n \"\"\" Save user profile in xml format to temp_ dir.\"\"\"\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))\r\n\r\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\r\n \"/data/sampleProfile.xml\")\r\n\r\n # The below code will parse the sample xml file\r\n # and fill important details entered by the user.\r\n root_tree = parse_xml.getroot()\r\n root_tree[0].text = self.ssid.get()\r\n root_tree[1][0][0].text = self.ssid.get()\r\n root_tree[3].text = self.connection_mode.get().lower()\r\n security = root_tree[4][0]\r\n security[0][0].text = self.authentication.get()\r\n security[0][1].text = self.encryption.get()\r\n if self.authentication.get() != \"open\":\r\n etree.SubElement(security, \"sharedKey\")\r\n etree.SubElement(security[1], \"keyType\").text = \"passPhrase\"\r\n etree.SubElement(security[1], \"protected\").text = \"false\"\r\n etree.SubElement(security[1], \"keyMaterial\").text = self.password.get()\r\n\r\n # Save the xml file\r\n saving_file(root_tree)\r\n\r\n # Add profile to the system.\r\n temp_path = 'netsh wlan add profile filename=\"' + self.app_path + \"\\\\temp_\\\\\"\r\n output_ = subprocess.run(temp_path + file_name + '.xml\"', shell=True,\r\n capture_output=True, text=True)\r\n os.remove(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\")\r\n\r\n # If unable to add profile.\r\n if output_.returncode != 0:\r\n message = \"Sorry, Unable to add profile.\\n(You entered wrong details \" \\\r\n \"or else you don't have admin rights.)\"\r\n image_ = \"error\"\r\n\r\n else:\r\n message = \"Profile added successfully (Please Refresh)\"\r\n image_ = \"warning\"\r\n\r\n MessageBox(self.parent, message, image_)","def save(self, filename):\n with open(filename, 'w') as f:\n pickle.dump((self.mean, self.std_dev), f)","def save_xml(self, fileName, outputTextFileName, predictedObjects):\n if self.verbose:\n print('Saving xml file', outputTextFileName)\n # root element\n root = ET.Element(\"annotation\")\n # annotation.filename\n ET.SubElement(root, \"filename\").text = inputFileName\n # For each predicted object\n for i in range(len(predictedObjects)):\n # annotation.object\n predObject = ET.SubElement(root, \"object\")\n # annotation.object.name\n ET.SubElement(\n predObject, \"name\").text = predictedObjects[i][0]\n # annotation.object.confidence\n ET.SubElement(predObject, \"confidence\").text = str(\n predictedObjects[i][5])\n # annotation.object.bndBox\n bndBox = ET.SubElement(predObject, \"bndBox\")\n # annotation.object.bndBox.xmin\n ET.SubElement(bndBox, \"xmin\").text = str(\n predictedObjects[i][1])\n # annotation.object.bndBox.ymin\n ET.SubElement(bndBox, \"ymin\").text = str(\n predictedObjects[i][2])\n # annotation.object.bndBox.xmax\n ET.SubElement(bndBox, \"xmax\").text = str(\n predictedObjects[i][3])\n # annotation.object.bndBox.ymax\n ET.SubElement(bndBox, \"ymax\").text = str(\n predictedObjects[i][4])\n # Making the xml string\n xmlString = minidom.parseString(\n ET.tostring(root)).toprettyxml(indent=\" \")\n # Saving the xml file\n with open(outputTextFileName, 'w') as f:\n f.write(xmlString)","def xml(self):\n raise NotImplementedError('must be implemented by all subclasses')","def wlanGetProfile(self, iface, profile, saveToFile=None):\n log.debug('WlanInterface wlanGetProfile() - profile:\"%s\" saveToFile:%s' % (profile,saveToFile))\n sProfile = c_wchar_p(profile)\n sProfileXML = c_wchar_p() # create_unicode_buffer(1024)\n flags = DWORD(0)\n grantedAccess = DWORD()\n ret = WlanGetProfile( self._handle, \n byref(iface.InterfaceGuid), \n sProfile,\n None,\n byref(sProfileXML),\n byref(flags),\n byref(grantedAccess))\n if ret != ERROR_SUCCESS:\n raise WinError(ret)\n profileXML = sProfileXML.value\n if saveToFile:\n open(saveToFile,'w').write(profileXML)\n return profileXML","def save(self):\n super(YacoFile, self).save(self._filename)","def saveState(self):\n e = xml.Element(self.type)\n e.attrib['lastUpdate'] = str(clock.now())\n e.attrib['name'] = self.name\n #e.attrib['status'] = ('true' if self.status else 'false')\n return e","def write(xml, outf):\n my_dom = dom.parseString(tostring(xml))\n f = open(outf, 'w')\n PrettyPrint(my_dom, f, encoding='utf-8')\n f.close()","def save(self):\n file = open(self.path, 'w')\n self.parser.write(file)\n file.close()","def save(self, filename=\"fitter.pickle\"):\n\n with open(filename, \"wb\") as outfile:\n pickle.dump(self, outfile)","def save(self):\n with self.open(self.filename, 'wt') as fd:\n for node in self.elements:\n fd.write(node.text)","def serialize(self,writer: SerializationWriter) -> None:\n if not writer:\n raise TypeError(\"writer cannot be null.\")\n writer.write_datetime_value(\"assignedDateTime\", self.assigned_date_time)\n writer.write_str_value(\"capabilityStatus\", self.capability_status)\n writer.write_str_value(\"@odata.type\", self.odata_type)\n writer.write_str_value(\"service\", self.service)\n writer.write_uuid_value(\"servicePlanId\", self.service_plan_id)\n writer.write_additional_data_value(self.additional_data)","def saveXml(self, buf: java.lang.StringBuilder) -> None:\n ...","def saver(filename = None):\n save(self, filename)","def write(self):\r\n for prop in self.prpnames:\r\n elem = SubElement(self._root, prop)\r\n data = self.__getattribute__(prop)\r\n if self.prpnames[prop]['type'] == \"text\":\r\n elem.text = data\r\n elif self.prpnames[prop]['type'] == 'list':\r\n for x in data:\r\n SubElement(elem, 'regel').text = x\r\n elif self.prpnames[prop]['type'] == 'attr':\r\n elem.set(self.prpnames[prop]['naam'], data)\r\n tree = ElementTree(self._root)\r\n tree.write(self._fn)\r\n if not self.exists:\r\n self.exists = True","def _save(self):\n file = open(\"settings.ini\", \"w\")\n self._parser.write(file)\n file.close()","def saveSettings(self):\n self.genFiles.applyData()\n self.genGraph.applyData()","def write_xml(self, filepath=None, escapeNewline=True, indent=False):\n\n if not filepath:\n filepath = self.mFilePath\n\n if indent:\n self.indent(self.tree.getroot())\n\n output = StringIO()\n\n self.tree.write(output, encoding=\"UTF-8\")\n\n outFile = open(filepath, \"w\")\n if escapeNewline:\n # we need to make sure newline 
 is written correctly\n print >> outFile, re.sub(\"###newline_escape###\", \"
\", output.getvalue())\n else:\n print >> outFile, output.getvalue()\n\n outFile.close","def _save_settings(self):\n # data to be save :\n # -----------------\n # futurePivot node\n\n # create attributes\n self._create_data_attribute()\n\n # connect futurePivot node\n pm.connectAttr(\n \"%s%s\" % (self._futurePivot.name(), \".message\"),\n self._object.attr(\"pivotData.futurePivot\"),\n f=True,\n )","def wlanSetProfile(self, iface, profileXML, overwrite=True):\n log.debug('WlanInterface wlanSetProfile()')\n flags = DWORD(0)\n sProfileXML = c_wchar_p(profileXML)\n dwReasonCode = DWORD()\n bOverwrite = BOOL(overwrite)\n ret = WlanSetProfile( self._handle, \n byref(iface.InterfaceGuid), \n flags,\n sProfileXML,\n None,\n bOverwrite,\n None,\n byref(dwReasonCode))\n log.debug('wlanSetProfile() reasonCode:%s' % getWlanReasonCodeString( dwReasonCode ))\n if ret != ERROR_SUCCESS:\n raise WinError(ret)","def save(self):\n\t\tPath(self.PATH).mkdir(parents=True,exist_ok=True)\n\n\t\twith open(self.account_file, \"wb\") as file:\n\t\t\tpickle.dump(self, file)","def save(cls):\n playerdata = getAttributes(cls)\n Data.object_dump(playerdata, \"savedata.dat\")\n del playerdata","def save(self, export_path: str):","def serialize(self,writer: SerializationWriter) -> None:\n if not writer:\n raise TypeError(\"writer cannot be null.\")\n super().serialize(writer)\n writer.write_enum_value(\"detectionStatus\", self.detection_status)\n writer.write_object_value(\"fileDetails\", self.file_details)\n writer.write_str_value(\"mdeDeviceId\", self.mde_device_id)","def save_file(self):\n # paginate over deputies and senators getting their fields\n fieldnames = set([])\n congressmen = self.deputies + self.senators\n for data in congressmen:\n fieldnames = fieldnames.union(data.dump().keys())\n\n\n with open(IDENTITY_FILE_UPDATED, 'a') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=list(fieldnames), delimiter=';')\n writer.writeheader()\n\n for data in congressmen:\n writer.writerow(data.dump())","def save_xunit(self,filename):\n f = open(filename,'w')\n f.write('<?xml version=\"1.0\" encoding=\"UTF-8\"?>')\n f.write('<testsuite name=\"fbtest\" tests=\"%i\" errors=\"%i\" failures=\"%i\" untested=\"%i\" skip=\"%i\">' %\n (len(self.results),self.get_error_count(),self.get_fail_count(),\n self.get_untested_count(),self.get_skipped_count()))\n for result in self.values():\n if result.outcome == Result.PASS:\n f.write('<testcase classname=\"Test\" name=\"%s\" time=\"%.3f\" />' % (\n result.id,result.get_elapsed()))\n else:\n f.write('<testcase classname=\"Test\" name=\"%s\" time=\"%.3f\">' % (\n result.id,result.get_elapsed()))\n if result.outcome == Result.ERROR:\n if result.has_key(Result.EXCEPTION):\n e = result[Result.EXCEPTION]\n exc = e[:e.find(':')]\n msg = e[e.find(':')+2:]\n exc = exc[exc.find(\"'\")+1:exc.rfind(\"'\")]\n msg = msg.lstrip()\n f.write('<error type=%s message=%s>' % (self._quoteattr(exc),\n self._quoteattr(msg)))\n f.write('</error>')\n else:\n msg = result.get_cause()\n f.write('<error type=\"error\" message=%s>' % (self._quoteattr(msg)))\n f.write('</error>')\n elif result.outcome == Result.FAIL:\n for key in ['ISQL_stripped_diff','Python_stripped_diff',\n 'ISQL_stderr_stripped_diff',\n 'Python_stderr_stripped_diff']:\n if result.has_key(key):\n cdata = as_utf8(result[key])\n f.write('<failure type=\"fail\" message=%s>' % self._quoteattr(result.get_cause()))\n f.write('<![CDATA[%s]]>' % escape_cdata(cdata))\n f.write('</failure>')\n elif result.outcome == Result.UNTESTED:\n f.write('<failure type=\"untested\" message=%s>' % self._quoteattr(result.get_cause()))\n f.write('</failure>')\n elif result.outcome == Result.SKIPPED:\n f.write('<failure type=\"skipped\" message=%s>' % self._quoteattr(result.get_cause()))\n f.write('</failure>')\n f.write('</testcase>')\n f.write('</testsuite>')\n f.close()","def save(self):\n\n self.image.save(\"./output/\" + self.name + \" pg\" + str(self._page) + \".png\")","def SaveWirelessNetworkProfile(self, id):\n def write_script_ent(prof, conf, script):\n if not conf.has_option(prof, script):\n conf.set(prof, script, None)\n config = ConfigParser.ConfigParser()\n config.read(self.wireless_conf)\n cur_network = self.LastScan[id]\n bssid_key = cur_network[\"bssid\"]\n essid_key = \"essid:\" + cur_network[\"essid\"]\n if config.has_section(bssid_key):\n config.remove_section(bssid_key)\n config.add_section(bssid_key)\n if cur_network[\"use_settings_globally\"]:\n if config.has_section(essid_key):\n config.remove_section(essid_key)\n config.add_section(essid_key)\n for x in cur_network:\n config.set(bssid_key, x, cur_network[x])\n if cur_network[\"use_settings_globally\"]:\n config.set(essid_key, x, cur_network[x])\n write_script_ent(bssid_key, config, \"beforescript\")\n write_script_ent(bssid_key, config, \"afterscript\")\n write_script_ent(bssid_key, config, \"disconnect\")\n if cur_network[\"use_settings_globally\"]:\n write_script_ent(essid_key, config, \"beforescript\")\n write_script_ent(essid_key, config, \"afterscript\")\n write_script_ent(essid_key, config, \"disconnect\")\n config.write(open(self.wireless_conf, \"w\"))","def write_evt(evt, ev_id):\n print(\"Writing xml file\")\n print(\"../../refined_events/{:}.xml\".format(ev_id))\n evt.write(\"../../refined_events/{:}.xml\".format(ev_id), format=\"QUAKEML\")","def serialize(self,writer: SerializationWriter) -> None:\n if not writer:\n raise TypeError(\"writer cannot be null.\")\n writer.write_bool_value(\"allowedToCreateApps\", self.allowed_to_create_apps)\n writer.write_bool_value(\"allowedToCreateSecurityGroups\", self.allowed_to_create_security_groups)\n writer.write_bool_value(\"allowedToCreateTenants\", self.allowed_to_create_tenants)\n writer.write_bool_value(\"allowedToReadBitlockerKeysForOwnedDevice\", self.allowed_to_read_bitlocker_keys_for_owned_device)\n writer.write_bool_value(\"allowedToReadOtherUsers\", self.allowed_to_read_other_users)\n writer.write_str_value(\"@odata.type\", self.odata_type)\n writer.write_collection_of_primitive_values(\"permissionGrantPoliciesAssigned\", self.permission_grant_policies_assigned)\n writer.write_additional_data_value(self.additional_data)","def save(self,outPath=None):\n if (not self.canSave): raise StateError(_(\"Insufficient data to write file.\"))\n FileRep.save(self,outPath)","def save(self,outPath=None):\n if (not self.canSave): raise StateError(_(\"Insufficient data to write file.\"))\n FileRep.save(self,outPath)","def save(self, filepath=None):\n raise NotImplementedError()","def exportXml ( w, xml ):\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n rawText = xml\n pattern = re.compile (r'[^\\S ]+')\n text = re.sub ( pattern, \"\", rawText )\n reparsed = MD.parseString ( text )\n w.write ( reparsed.toprettyxml ( indent = \"\\t\", encoding = \"UTF-8\" ) )","def save(self, filename):\n \n raise NotImplementedError(\"not implemented!\")","def save(self,filename):\n f = open(filename, 'wb')\n pickle.dump(self,f)\n f.close()","def writexml(file):\n OUTFILE=open(file,\"w\")\n doc = xml.dom.minidom.Document()\n\n # Create the <dec_reg_list> base element\n decl_reg_list = doc.createElement(\"decl_reg_list\")\n doc.appendChild(decl_reg_list)\n\n regname_old=\"\"\n rows.pop(0)\n for row in rows:\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\n if regname != regname_old:\n # Create the register element\n register = doc.createElement(\"register\")\n register.setAttribute(\"name\", regname)\n register.setAttribute(\"offset\", offset)\n if default != \"\" : register.setAttribute(\"default\", default)\n register.setAttribute(\"type\", regtype)\n if expose_reg == \"1\": register.setAttribute(\"usr\", expose_reg)\n if depth != \"\": register.setAttribute(\"size\", depth)\n if incsz != \"\": register.setAttribute(\"incsz\", incsz)\n text = doc.createTextNode(regdesc)\n register.appendChild(text)\n decl_reg_list.appendChild(register)\n \n # Create the field element\n if bitname != \"\":\n field = doc.createElement(\"field\")\n field.setAttribute(\"name\", bitname)\n if loc !=\"\": field.setAttribute(\"loc\", addcolon(loc))\n if bittype != \"\": field.setAttribute(\"type\", bittype)\n if bitdesc != \"\":\n text = doc.createTextNode(bitdesc)\n field.appendChild(text)\n register.appendChild(field)\n regname_old = regname\n\n\n # Print our newly created XML\n #print doc.toprettyxml(indent=\" \")\n #OUTFILE.write(doc.saveXML(decl_reg_list))\n OUTFILE.write(doc.toprettyxml(indent=\" \"))\n OUTFILE.close()","def save(self):\r\n with open(self.filename, 'wb') as configfile:\r\n self.write(configfile)","def saveProfile(self, request):\n return self._doProfile(request)"],"string":"[\n \"def save_profile(file_path, profile):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n profile.save_to_xml(writer)\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def save_profiles(file_path, profiles, lastused=\\\"\\\"):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n writer.WriteStartElement(\\\"Profiles\\\")\\r\\n if lastused:\\r\\n writer.WriteAttributeString(\\\"LastUsed\\\", \\\",\\\".join(lastused))\\r\\n for profile in profiles:\\r\\n profiles[profile].save_to_xml(writer)\\r\\n writer.WriteEndElement()\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def saveSessionToXML(self, filename):\\r\\n xmlStr = self.createXMLStr()\\r\\n \\r\\n #Write to the file\\r\\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\\r\\n xmlFile = open(filename, 'w')\\r\\n xmlFile.write(xmlStr)\\r\\n xmlFile.close()\",\n \"def saving_file(xml):\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def write_xml(self, xmlfile):\\n system.xml.write_file(xmlfile, self.status, 'status')\",\n \"def xmlWrite(self, xmlWriter, font, value, name, attrs):\\n raise NotImplementedError(self)\",\n \"def saveToXml(self) -> org.jdom.Element:\\n ...\",\n \"def save_profile(self, dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"wb\\\") as f:\\n pickle.dump(self, f)\",\n \"def save(self, filename=None):\\n f = filename if filename else self.path\\n etree.register_namespace('', TEI)\\n etree.register_namespace('mith', MITH)\\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')\",\n \"def to_xml_file(self, xml_file_path):\\n s = self.to_xml()\\n with open(xml_file_path, \\\"w+b\\\") as f:\\n f.write(s)\",\n \"def save_profile(self):\\n self.save()\",\n \"def xmlwrite(self, doc, filename):\\n pathname = os.path.join(self.session.session_dir, filename)\\n f = open(pathname, \\\"w\\\")\\n doc.writexml(writer=f, indent=\\\"\\\", addindent=\\\" \\\", newl=\\\"\\\\n\\\", encoding=\\\"UTF-8\\\")\\n f.close()\",\n \"def save(self, save_path=None):\\n if self._xml is None:\\n raise IOError(\\\"There's nothing to save\\\")\\n\\n path = self._path_to_xml if save_path is None else save_path\\n\\n with open(path, 'w') as f:\\n rough_string = Et.tostring(self._xml, 'utf-8')\\n par = etree.XMLParser(remove_blank_text=True)\\n elem = etree.XML(rough_string, parser=par)\\n parsed = minidom.parseString(etree.tostring(elem))\\n f.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def save(self, pretty=True):\\n self.endInstance()\\n if pretty:\\n _indent(self.root, whitespace=self._whiteSpace)\\n tree = ET.ElementTree(self.root)\\n tree.write(self.path, encoding=\\\"utf-8\\\", method='xml', xml_declaration=True)\\n if self.logger:\\n self.logger.info(\\\"Writing %s\\\", self.path)\",\n \"def save_xml(self, filename):\\n if \\\".xml\\\" not in filename:\\n filename = filename + \\\".xml\\\"\\n\\n shutil.copyfile(self.env.model_file, filename)\",\n \"def Save_xml(self, accounts):\\n try:\\n\\n self.extension = \\\".xml\\\"\\n\\n colors.info(\\\"Saving as XML in {}{}\\\".format(self.file, self.extension))\\n\\n Main = ET.Element(\\\"SpotCheck\\\")\\n\\n SpotifyFree = ET.SubElement(Main, 'SpotifyFree')\\n SpotifyPremium = ET.SubElement(Main, 'SpotifyPremium')\\n PremiumFamily = ET.SubElement(Main, 'PremiumFamily')\\n AdminPremiumFamily = ET.SubElement(Main, 'AdminPremiumFamily')\\n BadAccounts = ET.SubElement(Main, 'BadAccounts')\\n\\n for account in accounts:\\n if account.get(\\\"account_login\\\") == \\\"error\\\":\\n temp = ET.SubElement(BadAccounts, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n else:\\n if account.get(\\\"AccountType\\\") == \\\"Spotify Free\\\":\\n temp = ET.SubElement(SpotifyFree, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n elif account.get(\\\"AccountType\\\") == \\\"Spotify Premium\\\":\\n temp = ET.SubElement(SpotifyPremium, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n elif account.get(\\\"AccountType\\\") == \\\"Premium Family\\\":\\n if account.get(\\\"Admin\\\"):\\n temp = ET.SubElement(AdminPremiumFamily, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n else:\\n temp = ET.SubElement(PremiumFamily, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n XML = ET.tostring(Main)\\n with open(self.file + self.extension, \\\"w\\\") as output_:\\n output_.write(XML)\\n colors.correct(\\\"Done! All saved successfully\\\")\\n except Exception as e:\\n colors.error(str(e))\\n _exit(1)\",\n \"def saveXML(self, filename):\\n root = ET.Element('root')\\n pklot = ET.SubElement(root, \\\"ParkingLot\\\")\\n\\n lotname = ET.SubElement(pklot, \\\"LotName\\\", name=str(self.name))\\n idCounter = ET.SubElement(pklot, \\\"NextAvailableID\\\", counter=str(self.spotIDCounter))\\n for spot in self.parkingSpots:\\n ET.SubElement(pklot, 'Spot', id=str(spot.id), location=' '.join(str(x) for x in spot.location))\\n\\n tree = ET.ElementTree(root)\\n tree.write(filename)\",\n \"def save_as(self, fname, base = None, indent = '', topns = True, namespaces = {}):\\n with codecs.open(fname, \\\"w\\\", encoding=\\\"utf-8\\\") as outf:\\n self.serialize_xml(outf.write, base=base, indent=indent, topns=topns, namespaces=namespaces)\",\n \"def save(self, filename):\\n o = open(filename, 'w')\\n o.write(self.write())\\n o.close()\",\n \"def export_to_file(self, filename):\\n if len(filename.split(\\\".\\\")) == 1:\\n filename += \\\".xml\\\"\\n xmlstring = self._dommodel.toprettyxml(\\\" \\\", \\\"\\\\n\\\")\\n with open(filename, \\\"w\\\") as f:\\n f.write(xmlstring)\",\n \"def write_to_xml(filename, xmlelement):\\n xmlpretty = prettify(xmlelement)\\n with open(filename, 'w') as f:\\n f.write(xmlpretty)\",\n \"def saveState(self, file):\\n state = self.context.getState(getPositions=True, getVelocities=True, getParameters=True, getIntegratorParameters=True)\\n xml = mm.XmlSerializer.serialize(state)\\n if isinstance(file, str):\\n with open(file, 'w') as f:\\n f.write(xml)\\n else:\\n file.write(xml)\",\n \"def write_xosc(self, generated_xml):\\n reparsed_xml = minidom.parseString(generated_xml).toprettyxml(indent=\\\" \\\")\\n xosc_file = open(self._filepath, \\\"w\\\")\\n xosc_file.write(reparsed_xml)\\n xosc_file.close()\\n\\n msg = QMessageBox()\\n if self._warning_message:\\n msg.setIcon(QMessageBox.Warning)\\n text = f\\\"Exported OpenSCENARIO file {self._filepath} has warnings!\\\\n\\\\n\\\"\\n text += \\\"\\\\n\\\".join(self._warning_message)\\n else:\\n msg.setIcon(QMessageBox.Information)\\n text = f\\\"Successfully exported OpenSCENARIO file to {self._filepath}\\\"\\n msg.setText(text)\\n msg.setWindowTitle(\\\"OpenSCENARIO Export\\\")\\n msg.setStandardButtons(QMessageBox.Ok)\\n msg.exec()\",\n \"def _create_xml_report(self, test, xml_obj):\\n xml_report_path = os.path.join(test.work_dir,\\n self.XML_REPORT_PATH)\\n with open(xml_report_path, 'w') as xml_report:\\n xml_report.write(etree.tostring(xml_obj, pretty_print=True))\",\n \"def save(self, fname):\\n pass\",\n \"def write(self, file_or_filename):\\n etMap = revert(self)\\n xmlTree = ET.ElementTree(etMap)\\n xmlTree.write(file_or_filename)\",\n \"def Save_Current_Profile(self):\\r\\n #name = tkFileDialog.asksaveasfilename()\\r\\n #if( name == \\\"\\\" ):\\r\\n # return\\r\\n #self.system.Save_Current_Profile(name)\\r\\n self.system.Save_Current_Profile()\",\n \"def saveFile(self, filename):\\n ret = libxml2mod.xmlSaveFile(filename, self._o)\\n return ret\",\n \"def save(self, filename):\\n with open(filename, \\\"w\\\") as fp:\\n dump(self, fp)\",\n \"def write(self):\\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\\\"UTF-8\\\")\\n with open(self.xml_file, 'w') as f:\\n f.write(temp_string)\\n # f = open(self.xml_file, \\\"w\\\")\\n # f.write(temp_string)\\n # f.close()\",\n \"def save(self, save_dir):\\n ProfileManager.save_data_to_disk(self.player_data, path.join(save_dir, self.player_name + '.yaml'))\",\n \"def save(self, fp):\\n fp.write(self.dump())\",\n \"def save_file(self):\\n if self.select_path.text() != \\\"\\\":\\n filepath = self.select_path.text()\\n road_network = self.map_selection.currentText()\\n if self.map_selection.currentText() == \\\"User Defined\\\":\\n road_network = self.map_selection_user_defined.text()\\n gen_xml = GenerateXML(filepath, road_network)\\n gen_xml.main()\\n # remember Road Network for future\\n set_metadata(road_network_filepath=road_network)\\n else:\\n message = \\\"No export path was selected\\\"\\n iface.messageBar().pushMessage(\\\"Warning\\\", message, level=Qgis.Warning)\\n QgsMessageLog.logMessage(message, level=Qgis.Warning)\",\n \"def writeToTempXml(self):\\n name = self.fileToProcess.name\\n all_tokens = ET.Element(\\\"tokens\\\")\\n for token in self.tokensTable:\\n if token.getType() == KEYWORD:\\n keyword = ET.SubElement(all_tokens, \\\"keyword\\\")\\n keyword.text = ' '+token.getValue()+' '\\n elif token.getType() == IDENTIFIER:\\n identifier = ET.SubElement(all_tokens, \\\"identifier\\\")\\n identifier.text = ' '+token.getValue()+' '\\n elif token.getType() == SYMBOL:\\n symbol = ET.SubElement(all_tokens, \\\"symbol\\\")\\n symbol.text = ' '+token.getValue()+' '\\n elif token.getType() == STRING_CONST:\\n stringConstant = ET.SubElement(all_tokens, \\\"stringConstant\\\")\\n stringConstant.text = ' '+token.getValue()+' '\\n elif token.getType() == INT_CONST:\\n integerConstant = ET.SubElement(all_tokens, \\\"integerConstant\\\")\\n integerConstant.text = ' '+token.getValue()+' '\\n tree = ET.ElementTree(all_tokens)\\n tree.write(name + 'T' + '.xml')\",\n \"def save(self, filename):\\n pass\",\n \"def save_calibration_profile(self, filename: str) -> None:\\n pass\",\n \"def _save_helper(self, filepath, data_dict):\\n # Set Default filepath\\n if filepath is None:\\n filepath = \\\"profile-{}.pkl\\\".format(\\n datetime.now().strftime(\\\"%d-%b-%Y-%H:%M:%S.%f\\\"))\\n\\n # Remove data labelers as they can't be pickled\\n data_labelers = self._remove_data_labelers()\\n\\n # add profiler class to data_dict\\n data_dict['profiler_class'] = self.__class__.__name__\\n\\n # Pickle and save profile to disk\\n with open(filepath, \\\"wb\\\") as outfile:\\n pickle.dump(data_dict, outfile)\\n\\n # Restore all data labelers\\n self._restore_data_labelers(data_labelers)\",\n \"def save(self,file):\\n\\n with open(file,\\\"w\\\") as f:\\n f.write(self.to_string())\",\n \"def save(self):\\n # TODO: save the file\",\n \"def save_elem_file(self, output):\\n with open(output, 'wb') as fid:\\n self._write_elem_header(fid)\\n self._write_nodes(fid)\\n self._write_elements(fid)\\n self._write_neighbors(fid)\",\n \"def save(self):\\n self.lock.acquire()\\n try:\\n self.xml.set(\\\"name\\\",self.name)\\n self.xml.set(\\\"room\\\",self.room)\\n self.xml.set(\\\"type\\\",self.type)\\n self.xml.find(\\\"address\\\").text = \\\":\\\".join([str(x) for x in self.address])\\n if self.pos is not None:\\n self.xml.find(\\\"pos\\\").text = \\\" \\\".join([str(x) for x in self.pos])\\n self.xml.find(\\\"icon\\\").text = self.icon\\n \\n finally:\\n self.lock.release()\\n \\n self.house.save_devices()\",\n \"def save(self, filename: str):\\n dump(self, filename)\",\n \"def write(self, filename):\\n \\n return self.model.write(filename,xml_declaration=True, encoding='utf-8')\",\n \"def save_current_to_profile(self, profile_name, prof_desc='', prof_path='',\\n self_contained=False):\\n # Open the already existing profile\\n new_profile = profile(profile_name, workdir=os.path.dirname(prof_path))\\n\\n # shortcut\\n w3af_plugins = self._w3af_core.plugins\\n\\n # Save the enabled plugins\\n for plugin_type in w3af_plugins.get_plugin_types():\\n enabled_plugins = []\\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\\n enabled_plugins.append(plugin_name)\\n new_profile.set_enabled_plugins(plugin_type, enabled_plugins)\\n\\n # Save the plugin options\\n for plugin_type in w3af_plugins.get_plugin_types():\\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\\n plugin_options = w3af_plugins.get_plugin_options(plugin_type,\\n plugin_name)\\n if plugin_options:\\n new_profile.set_plugin_options(plugin_type,\\n plugin_name,\\n plugin_options,\\n self_contained=self_contained)\\n\\n # Save the profile targets\\n targets = cf.cf.get('targets')\\n if targets:\\n new_profile.set_target(' , '.join(t.url_string for t in targets))\\n\\n # Save the misc and http settings\\n misc_settings = MiscSettings()\\n new_profile.set_misc_settings(misc_settings.get_options())\\n new_profile.set_http_settings(\\n self._w3af_core.uri_opener.settings.get_options())\\n\\n # Save the profile name and description\\n new_profile.set_desc(prof_desc)\\n new_profile.set_name(profile_name)\\n\\n # Save the profile to the file\\n new_profile.save(profile_name)\\n\\n return new_profile\",\n \"def get_xml(self):\\n profile = self.profile\\n version = self.version\\n #self.attribs['xmlns'] = \\\"http://www.w3.org/2000/svg\\\"\\n self.attribs['xmlns:xlink'] = \\\"http://www.w3.org/1999/xlink\\\"\\n self.attribs['xmlns:ev'] = \\\"http://www.w3.org/2001/xml-events\\\"\\n\\n self.attribs['baseProfile'] = profile\\n self.attribs['version'] = version\\n return super(Drawing, self).get_xml()\",\n \"def writeProfile(fname,prof):\\n t = np.linspace(0,1,prof.shape[0],endpoint=False)\\n fh = open(fname,'w')\\n for x in range(prof.shape[0]):\\n fh.write('%.7e %.7e\\\\n' % (t[x],prof[x]))\\n fh.close()\",\n \"def save_profile(sender, instance, **kwargs):\\n instance.profile.save()\",\n \"def save_xml_file():\\n global output_on_display, import_lst, column_names, data\\n if data_base == '':\\n mistake_load_table()\\n else:\\n column_names = data[0]\\n step = len(column_names)\\n\\n save_name = asksaveasfilename(title=\\\"Select file\\\", filetypes=((\\\"XML\\\", \\\"*.xml\\\"), (\\\"all files\\\", \\\"*.*\\\")),\\n confirmoverwrite=True, defaultextension='.xml')\\n data = import_lst\\n\\n if len(data[0]) == step:\\n pass\\n else:\\n data = import_lst[step::]\\n\\n data2 = list(map(list, zip(*data)))\\n\\n data3 = {key: value for key, value in zip(column_names, data2)}\\n\\n column = list(data3.keys())\\n\\n df = pd.DataFrame(data3, columns=column)\\n\\n data_dict = df.to_dict(orient=\\\"records\\\")\\n with open('output.json', \\\"w+\\\") as f:\\n json.dump(data_dict, f, indent=4)\\n\\n xml_data = dicttoxml(data_dict).decode()\\n with open(save_name, \\\"w+\\\") as f:\\n f.write(xml_data)\\n\\n data.clear()\\n data2.clear()\\n data3.clear()\",\n \"def save(self, filename:str):\\n dump(self, filename=filename)\",\n \"def SaveWiredNetworkProfile(self, profilename):\\n def write_script_ent(prof, conf, script):\\n if not conf.has_option(prof, script):\\n conf.set(prof, script, None)\\n if profilename == \\\"\\\":\\n return \\\"500: Bad Profile name\\\"\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n config.remove_section(profilename)\\n config.add_section(profilename)\\n for x in self.WiredNetwork:\\n config.set(profilename, x, self.WiredNetwork[x])\\n write_script_ent(profilename, config, \\\"beforescript\\\")\\n write_script_ent(profilename, config, \\\"afterscript\\\")\\n write_script_ent(profilename, config, \\\"disconnectscript\\\")\\n config.write(open(self.wired_conf, \\\"w\\\"))\\n return \\\"100: Profile Written\\\"\",\n \"def save_profiles(self, fout, save_hybrid_meta=True):\\n\\n self._init_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\\n self._write_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\",\n \"def Write_XML(gui): \\n # lock buttons\\n gui.action_lock('Lock', gui.save_button)\\n \\n # clear output\\n gui.output_clear() \\n \\n # get the desired delay from the gui.\\n delay_time = gui.get_delay()\\n \\n \\n # get the desired ascii delay from the gui.\\n ascii_time = gui.get_ascii_delay()\\n \\n \\n # get the desired I2C address from the gui.\\n addr = \\\"0x%X\\\" % gui.get_i2c_address()\\n \\n \\n # get the list of commands from the gui\\n command_list = gui.get_command_list()\\n \\n # wrap up the writing directives\\n directives = pySCPI_config.write_directives(command_list, addr,\\n delay_time, ascii_time)\\n \\n # create the xml file\\n filename = create_XML(directives, gui)\\n \\n # update the filename display window to show the filename saved\\n gui.update_filename(filename = filename) \\n \\n # unlock the buttons\\n gui.action_lock('Unlock')\",\n \"def _make_wlan_profile_xml(ssid, auth_method, encrypt_method, key_type = \\\"\\\", key_material = \\\"\\\", key_index = \\\"\\\", use_onex = False):\\n\\n # Try to generate a temporary file for storing the XML profile\\n fd, path = tempfile.mkstemp(\\\".xml\\\")\\n\\n # Fill the content\\n os.write(fd, '<?xml version=\\\"1.0\\\"?>\\\\n')\\n os.write(fd, '<WLANProfile xmlns=\\\"http://www.microsoft.com/networking/WLAN/profile/v1\\\">\\\\n')\\n os.write(fd, ' <name>' + ssid + '</name>\\\\n')\\n os.write(fd, ' <SSIDConfig>\\\\n')\\n os.write(fd, ' <SSID>\\\\n')\\n os.write(fd, ' <name>' + ssid + '</name>\\\\n')\\n os.write(fd, ' </SSID>\\\\n')\\n os.write(fd, ' </SSIDConfig>\\\\n')\\n os.write(fd, ' <connectionType>ESS</connectionType>\\\\n')\\n os.write(fd, ' <MSM>\\\\n')\\n os.write(fd, ' <security>\\\\n')\\n os.write(fd, ' <authEncryption>\\\\n')\\n os.write(fd, ' <authentication>' + auth_method + '</authentication>\\\\n')\\n os.write(fd, ' <encryption>' + encrypt_method + '</encryption>\\\\n')\\n if use_onex:\\n os.write(fd, ' <useOneX>true</useOneX>\\\\n')\\n else:\\n os.write(fd, ' <useOneX>false</useOneX>\\\\n')\\n os.write(fd, ' </authEncryption>\\\\n')\\n\\n if not use_onex:\\n # 802.1x is not applied\\n if len(key_type) > 0:\\n os.write(fd, ' <sharedKey>\\\\n')\\n os.write(fd, ' <keyType>' + key_type + '</keyType>\\\\n')\\n os.write(fd, ' <protected>false</protected>\\\\n')\\n os.write(fd, ' <keyMaterial>' + key_material + '</keyMaterial>\\\\n')\\n os.write(fd, ' </sharedKey>\\\\n')\\n\\n if key_index:\\n os.write(fd, ' <keyIndex>' + str(int(key_index) - 1) + '</keyIndex>\\\\n')\\n\\n\\n else:\\n # 802.1x is applied\\n os.write(fd, ' <OneX xmlns=\\\"http://www.microsoft.com/networking/OneX/v1\\\">\\\\n')\\n os.write(fd, ' <EAPConfig>\\\\n')\\n os.write(fd, ' <EapHostConfig xmlns=\\\"http://www.microsoft.com/provisioning/EapHostConfig\\\" \\\\n')\\n os.write(fd, ' xmlns:eapCommon=\\\"http://www.microsoft.com/provisioning/EapCommon\\\" \\\\n')\\n os.write(fd, ' xmlns:baseEap=\\\"http://www.microsoft.com/provisioning/BaseEapMethodConfig\\\">\\\\n')\\n os.write(fd, ' <EapMethod>\\\\n')\\n os.write(fd, ' <eapCommon:Type>25</eapCommon:Type>\\\\n')\\n os.write(fd, ' <eapCommon:AuthorId>0</eapCommon:AuthorId>\\\\n')\\n os.write(fd, ' </EapMethod>\\\\n')\\n os.write(fd, ' <Config xmlns:baseEap=\\\"http://www.microsoft.com/provisioning/BaseEapConnectionPropertiesV1\\\" \\\\n')\\n os.write(fd, ' xmlns:msPeap=\\\"http://www.microsoft.com/provisioning/MsPeapConnectionPropertiesV1\\\" \\\\n')\\n os.write(fd, ' xmlns:msChapV2=\\\"http://www.microsoft.com/provisioning/MsChapV2ConnectionPropertiesV1\\\">\\\\n')\\n os.write(fd, ' <baseEap:Eap>\\\\n')\\n os.write(fd, ' <baseEap:Type>25</baseEap:Type>\\\\n')\\n os.write(fd, ' <msPeap:EapType>\\\\n')\\n os.write(fd, ' <msPeap:FastReconnect>false</msPeap:FastReconnect>\\\\n')\\n os.write(fd, ' <msPeap:InnerEapOptional>0</msPeap:InnerEapOptional>\\\\n')\\n os.write(fd, ' <baseEap:Eap>\\\\n')\\n os.write(fd, ' <baseEap:Type>26</baseEap:Type>\\\\n')\\n os.write(fd, ' <msChapV2:EapType>\\\\n')\\n os.write(fd, ' <msChapV2:UseWinLogonCredentials>false</msChapV2:UseWinLogonCredentials>\\\\n')\\n os.write(fd, ' </msChapV2:EapType>\\\\n')\\n os.write(fd, ' </baseEap:Eap>\\\\n')\\n os.write(fd, ' <msPeap:EnableQuarantineChecks>false</msPeap:EnableQuarantineChecks>\\\\n')\\n os.write(fd, ' <msPeap:RequireCryptoBinding>false</msPeap:RequireCryptoBinding>\\\\n')\\n os.write(fd, ' <msPeap:PeapExtensions />\\\\n')\\n os.write(fd, ' </msPeap:EapType>\\\\n')\\n os.write(fd, ' </baseEap:Eap>\\\\n')\\n os.write(fd, ' </Config>\\\\n')\\n os.write(fd, ' </EapHostConfig>\\\\n')\\n os.write(fd, ' </EAPConfig>\\\\n')\\n os.write(fd, ' </OneX>\\\\n')\\n # End of if use_onex\\n\\n os.write(fd, ' </security>\\\\n')\\n os.write(fd, ' </MSM>\\\\n')\\n os.write(fd, '</WLANProfile>')\\n\\n os.close(fd)\\n return path\",\n \"def serialize(self,writer: SerializationWriter) -> None:\\n if not writer:\\n raise TypeError(\\\"writer cannot be null.\\\")\\n writer.write_object_value(\\\"attackSimulationUser\\\", self.attack_simulation_user)\\n writer.write_int_value(\\\"clickCount\\\", self.click_count)\\n writer.write_int_value(\\\"compromisedCount\\\", self.compromised_count)\\n writer.write_datetime_value(\\\"latestSimulationDateTime\\\", self.latest_simulation_date_time)\\n writer.write_str_value(\\\"@odata.type\\\", self.odata_type)\\n writer.write_int_value(\\\"simulationCount\\\", self.simulation_count)\\n writer.write_additional_data_value(self.additional_data)\",\n \"def save(self, x):\\n self.print_handler_f(x, x.get_full_path())\",\n \"def write_output_file(self, xml_text, xml_file):\\n xml_fo = open(xml_file, 'w')\\n xml_fo.write(xml_text+'</xml>')\\n xml_fo.close()\\n return\",\n \"def to_file(self, file_path, smirnoff_data):\\n xml_string = self.to_string(smirnoff_data)\\n with open(file_path, \\\"w\\\") as of:\\n of.write(xml_string)\",\n \"def save_model(self, fname, pretty=False):\\n with open(fname, \\\"w\\\") as f:\\n xml_str = ET.tostring(self.root, encoding=\\\"unicode\\\")\\n if pretty:\\n parsed_xml = xml.dom.minidom.parseString(xml_str)\\n xml_str = parsed_xml.toprettyxml(newl=\\\"\\\")\\n f.write(xml_str)\",\n \"def recordStatGraph(g, path):\\n g.write(path, xml_declaration=True, encoding='utf-8', method='xml')\",\n \"def save(self, filename):\\n raise NotImplementedError\",\n \"def save_drill_to_goalie_profile(self):\\n\\n goalie_path = str(Path.home())+\\\"/Documents/ball_e_profiles/goalie_profiles/{goalie_name}/{goalie_name}.csv\\\".format(\\n goalie_name=self.goalie_name)\\n with open(goalie_path, 'a+', newline='') as file:\\n csv_writer = csv.writer(file, delimiter=\\\",\\\")\\n # Row written as \\\"Drill Name, MM/DD/YYYY\\\"\\n drill_info = [\\\"{}\\\".format(self.drill_name.replace(\\\"_\\\", \\\" \\\").title()), \\\"{}\\\".format(\\n datetime.datetime.today().strftime(\\\"%m/%d/%Y\\\"))]\\n csv_writer.writerow(drill_info)\",\n \"def apply(self):\\r\\n\\r\\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\\r\\n\\r\\n def saving_file(xml):\\r\\n \\\"\\\"\\\" Save user profile in xml format to temp_ dir.\\\"\\\"\\\"\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\\r\\n\\r\\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\\r\\n \\\"/data/sampleProfile.xml\\\")\\r\\n\\r\\n # The below code will parse the sample xml file\\r\\n # and fill important details entered by the user.\\r\\n root_tree = parse_xml.getroot()\\r\\n root_tree[0].text = self.ssid.get()\\r\\n root_tree[1][0][0].text = self.ssid.get()\\r\\n root_tree[3].text = self.connection_mode.get().lower()\\r\\n security = root_tree[4][0]\\r\\n security[0][0].text = self.authentication.get()\\r\\n security[0][1].text = self.encryption.get()\\r\\n if self.authentication.get() != \\\"open\\\":\\r\\n etree.SubElement(security, \\\"sharedKey\\\")\\r\\n etree.SubElement(security[1], \\\"keyType\\\").text = \\\"passPhrase\\\"\\r\\n etree.SubElement(security[1], \\\"protected\\\").text = \\\"false\\\"\\r\\n etree.SubElement(security[1], \\\"keyMaterial\\\").text = self.password.get()\\r\\n\\r\\n # Save the xml file\\r\\n saving_file(root_tree)\\r\\n\\r\\n # Add profile to the system.\\r\\n temp_path = 'netsh wlan add profile filename=\\\"' + self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\"\\r\\n output_ = subprocess.run(temp_path + file_name + '.xml\\\"', shell=True,\\r\\n capture_output=True, text=True)\\r\\n os.remove(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\")\\r\\n\\r\\n # If unable to add profile.\\r\\n if output_.returncode != 0:\\r\\n message = \\\"Sorry, Unable to add profile.\\\\n(You entered wrong details \\\" \\\\\\r\\n \\\"or else you don't have admin rights.)\\\"\\r\\n image_ = \\\"error\\\"\\r\\n\\r\\n else:\\r\\n message = \\\"Profile added successfully (Please Refresh)\\\"\\r\\n image_ = \\\"warning\\\"\\r\\n\\r\\n MessageBox(self.parent, message, image_)\",\n \"def save(self, filename):\\n with open(filename, 'w') as f:\\n pickle.dump((self.mean, self.std_dev), f)\",\n \"def save_xml(self, fileName, outputTextFileName, predictedObjects):\\n if self.verbose:\\n print('Saving xml file', outputTextFileName)\\n # root element\\n root = ET.Element(\\\"annotation\\\")\\n # annotation.filename\\n ET.SubElement(root, \\\"filename\\\").text = inputFileName\\n # For each predicted object\\n for i in range(len(predictedObjects)):\\n # annotation.object\\n predObject = ET.SubElement(root, \\\"object\\\")\\n # annotation.object.name\\n ET.SubElement(\\n predObject, \\\"name\\\").text = predictedObjects[i][0]\\n # annotation.object.confidence\\n ET.SubElement(predObject, \\\"confidence\\\").text = str(\\n predictedObjects[i][5])\\n # annotation.object.bndBox\\n bndBox = ET.SubElement(predObject, \\\"bndBox\\\")\\n # annotation.object.bndBox.xmin\\n ET.SubElement(bndBox, \\\"xmin\\\").text = str(\\n predictedObjects[i][1])\\n # annotation.object.bndBox.ymin\\n ET.SubElement(bndBox, \\\"ymin\\\").text = str(\\n predictedObjects[i][2])\\n # annotation.object.bndBox.xmax\\n ET.SubElement(bndBox, \\\"xmax\\\").text = str(\\n predictedObjects[i][3])\\n # annotation.object.bndBox.ymax\\n ET.SubElement(bndBox, \\\"ymax\\\").text = str(\\n predictedObjects[i][4])\\n # Making the xml string\\n xmlString = minidom.parseString(\\n ET.tostring(root)).toprettyxml(indent=\\\" \\\")\\n # Saving the xml file\\n with open(outputTextFileName, 'w') as f:\\n f.write(xmlString)\",\n \"def xml(self):\\n raise NotImplementedError('must be implemented by all subclasses')\",\n \"def wlanGetProfile(self, iface, profile, saveToFile=None):\\n log.debug('WlanInterface wlanGetProfile() - profile:\\\"%s\\\" saveToFile:%s' % (profile,saveToFile))\\n sProfile = c_wchar_p(profile)\\n sProfileXML = c_wchar_p() # create_unicode_buffer(1024)\\n flags = DWORD(0)\\n grantedAccess = DWORD()\\n ret = WlanGetProfile( self._handle, \\n byref(iface.InterfaceGuid), \\n sProfile,\\n None,\\n byref(sProfileXML),\\n byref(flags),\\n byref(grantedAccess))\\n if ret != ERROR_SUCCESS:\\n raise WinError(ret)\\n profileXML = sProfileXML.value\\n if saveToFile:\\n open(saveToFile,'w').write(profileXML)\\n return profileXML\",\n \"def save(self):\\n super(YacoFile, self).save(self._filename)\",\n \"def saveState(self):\\n e = xml.Element(self.type)\\n e.attrib['lastUpdate'] = str(clock.now())\\n e.attrib['name'] = self.name\\n #e.attrib['status'] = ('true' if self.status else 'false')\\n return e\",\n \"def write(xml, outf):\\n my_dom = dom.parseString(tostring(xml))\\n f = open(outf, 'w')\\n PrettyPrint(my_dom, f, encoding='utf-8')\\n f.close()\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.parser.write(file)\\n file.close()\",\n \"def save(self, filename=\\\"fitter.pickle\\\"):\\n\\n with open(filename, \\\"wb\\\") as outfile:\\n pickle.dump(self, outfile)\",\n \"def save(self):\\n with self.open(self.filename, 'wt') as fd:\\n for node in self.elements:\\n fd.write(node.text)\",\n \"def serialize(self,writer: SerializationWriter) -> None:\\n if not writer:\\n raise TypeError(\\\"writer cannot be null.\\\")\\n writer.write_datetime_value(\\\"assignedDateTime\\\", self.assigned_date_time)\\n writer.write_str_value(\\\"capabilityStatus\\\", self.capability_status)\\n writer.write_str_value(\\\"@odata.type\\\", self.odata_type)\\n writer.write_str_value(\\\"service\\\", self.service)\\n writer.write_uuid_value(\\\"servicePlanId\\\", self.service_plan_id)\\n writer.write_additional_data_value(self.additional_data)\",\n \"def saveXml(self, buf: java.lang.StringBuilder) -> None:\\n ...\",\n \"def saver(filename = None):\\n save(self, filename)\",\n \"def write(self):\\r\\n for prop in self.prpnames:\\r\\n elem = SubElement(self._root, prop)\\r\\n data = self.__getattribute__(prop)\\r\\n if self.prpnames[prop]['type'] == \\\"text\\\":\\r\\n elem.text = data\\r\\n elif self.prpnames[prop]['type'] == 'list':\\r\\n for x in data:\\r\\n SubElement(elem, 'regel').text = x\\r\\n elif self.prpnames[prop]['type'] == 'attr':\\r\\n elem.set(self.prpnames[prop]['naam'], data)\\r\\n tree = ElementTree(self._root)\\r\\n tree.write(self._fn)\\r\\n if not self.exists:\\r\\n self.exists = True\",\n \"def _save(self):\\n file = open(\\\"settings.ini\\\", \\\"w\\\")\\n self._parser.write(file)\\n file.close()\",\n \"def saveSettings(self):\\n self.genFiles.applyData()\\n self.genGraph.applyData()\",\n \"def write_xml(self, filepath=None, escapeNewline=True, indent=False):\\n\\n if not filepath:\\n filepath = self.mFilePath\\n\\n if indent:\\n self.indent(self.tree.getroot())\\n\\n output = StringIO()\\n\\n self.tree.write(output, encoding=\\\"UTF-8\\\")\\n\\n outFile = open(filepath, \\\"w\\\")\\n if escapeNewline:\\n # we need to make sure newline 
 is written correctly\\n print >> outFile, re.sub(\\\"###newline_escape###\\\", \\\"
\\\", output.getvalue())\\n else:\\n print >> outFile, output.getvalue()\\n\\n outFile.close\",\n \"def _save_settings(self):\\n # data to be save :\\n # -----------------\\n # futurePivot node\\n\\n # create attributes\\n self._create_data_attribute()\\n\\n # connect futurePivot node\\n pm.connectAttr(\\n \\\"%s%s\\\" % (self._futurePivot.name(), \\\".message\\\"),\\n self._object.attr(\\\"pivotData.futurePivot\\\"),\\n f=True,\\n )\",\n \"def wlanSetProfile(self, iface, profileXML, overwrite=True):\\n log.debug('WlanInterface wlanSetProfile()')\\n flags = DWORD(0)\\n sProfileXML = c_wchar_p(profileXML)\\n dwReasonCode = DWORD()\\n bOverwrite = BOOL(overwrite)\\n ret = WlanSetProfile( self._handle, \\n byref(iface.InterfaceGuid), \\n flags,\\n sProfileXML,\\n None,\\n bOverwrite,\\n None,\\n byref(dwReasonCode))\\n log.debug('wlanSetProfile() reasonCode:%s' % getWlanReasonCodeString( dwReasonCode ))\\n if ret != ERROR_SUCCESS:\\n raise WinError(ret)\",\n \"def save(self):\\n\\t\\tPath(self.PATH).mkdir(parents=True,exist_ok=True)\\n\\n\\t\\twith open(self.account_file, \\\"wb\\\") as file:\\n\\t\\t\\tpickle.dump(self, file)\",\n \"def save(cls):\\n playerdata = getAttributes(cls)\\n Data.object_dump(playerdata, \\\"savedata.dat\\\")\\n del playerdata\",\n \"def save(self, export_path: str):\",\n \"def serialize(self,writer: SerializationWriter) -> None:\\n if not writer:\\n raise TypeError(\\\"writer cannot be null.\\\")\\n super().serialize(writer)\\n writer.write_enum_value(\\\"detectionStatus\\\", self.detection_status)\\n writer.write_object_value(\\\"fileDetails\\\", self.file_details)\\n writer.write_str_value(\\\"mdeDeviceId\\\", self.mde_device_id)\",\n \"def save_file(self):\\n # paginate over deputies and senators getting their fields\\n fieldnames = set([])\\n congressmen = self.deputies + self.senators\\n for data in congressmen:\\n fieldnames = fieldnames.union(data.dump().keys())\\n\\n\\n with open(IDENTITY_FILE_UPDATED, 'a') as csvfile:\\n writer = csv.DictWriter(csvfile, fieldnames=list(fieldnames), delimiter=';')\\n writer.writeheader()\\n\\n for data in congressmen:\\n writer.writerow(data.dump())\",\n \"def save_xunit(self,filename):\\n f = open(filename,'w')\\n f.write('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>')\\n f.write('<testsuite name=\\\"fbtest\\\" tests=\\\"%i\\\" errors=\\\"%i\\\" failures=\\\"%i\\\" untested=\\\"%i\\\" skip=\\\"%i\\\">' %\\n (len(self.results),self.get_error_count(),self.get_fail_count(),\\n self.get_untested_count(),self.get_skipped_count()))\\n for result in self.values():\\n if result.outcome == Result.PASS:\\n f.write('<testcase classname=\\\"Test\\\" name=\\\"%s\\\" time=\\\"%.3f\\\" />' % (\\n result.id,result.get_elapsed()))\\n else:\\n f.write('<testcase classname=\\\"Test\\\" name=\\\"%s\\\" time=\\\"%.3f\\\">' % (\\n result.id,result.get_elapsed()))\\n if result.outcome == Result.ERROR:\\n if result.has_key(Result.EXCEPTION):\\n e = result[Result.EXCEPTION]\\n exc = e[:e.find(':')]\\n msg = e[e.find(':')+2:]\\n exc = exc[exc.find(\\\"'\\\")+1:exc.rfind(\\\"'\\\")]\\n msg = msg.lstrip()\\n f.write('<error type=%s message=%s>' % (self._quoteattr(exc),\\n self._quoteattr(msg)))\\n f.write('</error>')\\n else:\\n msg = result.get_cause()\\n f.write('<error type=\\\"error\\\" message=%s>' % (self._quoteattr(msg)))\\n f.write('</error>')\\n elif result.outcome == Result.FAIL:\\n for key in ['ISQL_stripped_diff','Python_stripped_diff',\\n 'ISQL_stderr_stripped_diff',\\n 'Python_stderr_stripped_diff']:\\n if result.has_key(key):\\n cdata = as_utf8(result[key])\\n f.write('<failure type=\\\"fail\\\" message=%s>' % self._quoteattr(result.get_cause()))\\n f.write('<![CDATA[%s]]>' % escape_cdata(cdata))\\n f.write('</failure>')\\n elif result.outcome == Result.UNTESTED:\\n f.write('<failure type=\\\"untested\\\" message=%s>' % self._quoteattr(result.get_cause()))\\n f.write('</failure>')\\n elif result.outcome == Result.SKIPPED:\\n f.write('<failure type=\\\"skipped\\\" message=%s>' % self._quoteattr(result.get_cause()))\\n f.write('</failure>')\\n f.write('</testcase>')\\n f.write('</testsuite>')\\n f.close()\",\n \"def save(self):\\n\\n self.image.save(\\\"./output/\\\" + self.name + \\\" pg\\\" + str(self._page) + \\\".png\\\")\",\n \"def SaveWirelessNetworkProfile(self, id):\\n def write_script_ent(prof, conf, script):\\n if not conf.has_option(prof, script):\\n conf.set(prof, script, None)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wireless_conf)\\n cur_network = self.LastScan[id]\\n bssid_key = cur_network[\\\"bssid\\\"]\\n essid_key = \\\"essid:\\\" + cur_network[\\\"essid\\\"]\\n if config.has_section(bssid_key):\\n config.remove_section(bssid_key)\\n config.add_section(bssid_key)\\n if cur_network[\\\"use_settings_globally\\\"]:\\n if config.has_section(essid_key):\\n config.remove_section(essid_key)\\n config.add_section(essid_key)\\n for x in cur_network:\\n config.set(bssid_key, x, cur_network[x])\\n if cur_network[\\\"use_settings_globally\\\"]:\\n config.set(essid_key, x, cur_network[x])\\n write_script_ent(bssid_key, config, \\\"beforescript\\\")\\n write_script_ent(bssid_key, config, \\\"afterscript\\\")\\n write_script_ent(bssid_key, config, \\\"disconnect\\\")\\n if cur_network[\\\"use_settings_globally\\\"]:\\n write_script_ent(essid_key, config, \\\"beforescript\\\")\\n write_script_ent(essid_key, config, \\\"afterscript\\\")\\n write_script_ent(essid_key, config, \\\"disconnect\\\")\\n config.write(open(self.wireless_conf, \\\"w\\\"))\",\n \"def write_evt(evt, ev_id):\\n print(\\\"Writing xml file\\\")\\n print(\\\"../../refined_events/{:}.xml\\\".format(ev_id))\\n evt.write(\\\"../../refined_events/{:}.xml\\\".format(ev_id), format=\\\"QUAKEML\\\")\",\n \"def serialize(self,writer: SerializationWriter) -> None:\\n if not writer:\\n raise TypeError(\\\"writer cannot be null.\\\")\\n writer.write_bool_value(\\\"allowedToCreateApps\\\", self.allowed_to_create_apps)\\n writer.write_bool_value(\\\"allowedToCreateSecurityGroups\\\", self.allowed_to_create_security_groups)\\n writer.write_bool_value(\\\"allowedToCreateTenants\\\", self.allowed_to_create_tenants)\\n writer.write_bool_value(\\\"allowedToReadBitlockerKeysForOwnedDevice\\\", self.allowed_to_read_bitlocker_keys_for_owned_device)\\n writer.write_bool_value(\\\"allowedToReadOtherUsers\\\", self.allowed_to_read_other_users)\\n writer.write_str_value(\\\"@odata.type\\\", self.odata_type)\\n writer.write_collection_of_primitive_values(\\\"permissionGrantPoliciesAssigned\\\", self.permission_grant_policies_assigned)\\n writer.write_additional_data_value(self.additional_data)\",\n \"def save(self,outPath=None):\\n if (not self.canSave): raise StateError(_(\\\"Insufficient data to write file.\\\"))\\n FileRep.save(self,outPath)\",\n \"def save(self,outPath=None):\\n if (not self.canSave): raise StateError(_(\\\"Insufficient data to write file.\\\"))\\n FileRep.save(self,outPath)\",\n \"def save(self, filepath=None):\\n raise NotImplementedError()\",\n \"def exportXml ( w, xml ):\\n assert str ( type ( xml ) ) == \\\"<type 'str'>\\\"\\n rawText = xml\\n pattern = re.compile (r'[^\\\\S ]+')\\n text = re.sub ( pattern, \\\"\\\", rawText )\\n reparsed = MD.parseString ( text )\\n w.write ( reparsed.toprettyxml ( indent = \\\"\\\\t\\\", encoding = \\\"UTF-8\\\" ) )\",\n \"def save(self, filename):\\n \\n raise NotImplementedError(\\\"not implemented!\\\")\",\n \"def save(self,filename):\\n f = open(filename, 'wb')\\n pickle.dump(self,f)\\n f.close()\",\n \"def writexml(file):\\n OUTFILE=open(file,\\\"w\\\")\\n doc = xml.dom.minidom.Document()\\n\\n # Create the <dec_reg_list> base element\\n decl_reg_list = doc.createElement(\\\"decl_reg_list\\\")\\n doc.appendChild(decl_reg_list)\\n\\n regname_old=\\\"\\\"\\n rows.pop(0)\\n for row in rows:\\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\\n if regname != regname_old:\\n # Create the register element\\n register = doc.createElement(\\\"register\\\")\\n register.setAttribute(\\\"name\\\", regname)\\n register.setAttribute(\\\"offset\\\", offset)\\n if default != \\\"\\\" : register.setAttribute(\\\"default\\\", default)\\n register.setAttribute(\\\"type\\\", regtype)\\n if expose_reg == \\\"1\\\": register.setAttribute(\\\"usr\\\", expose_reg)\\n if depth != \\\"\\\": register.setAttribute(\\\"size\\\", depth)\\n if incsz != \\\"\\\": register.setAttribute(\\\"incsz\\\", incsz)\\n text = doc.createTextNode(regdesc)\\n register.appendChild(text)\\n decl_reg_list.appendChild(register)\\n \\n # Create the field element\\n if bitname != \\\"\\\":\\n field = doc.createElement(\\\"field\\\")\\n field.setAttribute(\\\"name\\\", bitname)\\n if loc !=\\\"\\\": field.setAttribute(\\\"loc\\\", addcolon(loc))\\n if bittype != \\\"\\\": field.setAttribute(\\\"type\\\", bittype)\\n if bitdesc != \\\"\\\":\\n text = doc.createTextNode(bitdesc)\\n field.appendChild(text)\\n register.appendChild(field)\\n regname_old = regname\\n\\n\\n # Print our newly created XML\\n #print doc.toprettyxml(indent=\\\" \\\")\\n #OUTFILE.write(doc.saveXML(decl_reg_list))\\n OUTFILE.write(doc.toprettyxml(indent=\\\" \\\"))\\n OUTFILE.close()\",\n \"def save(self):\\r\\n with open(self.filename, 'wb') as configfile:\\r\\n self.write(configfile)\",\n \"def saveProfile(self, request):\\n return self._doProfile(request)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75539035","0.66137195","0.6487311","0.64123094","0.61329263","0.6101124","0.60908806","0.60279024","0.60243684","0.59840643","0.59765226","0.58507067","0.585065","0.58390087","0.58205575","0.58169997","0.5809403","0.58001035","0.56534475","0.5646809","0.5583415","0.55652213","0.5553969","0.5545845","0.5525345","0.5517458","0.5493895","0.54818946","0.548075","0.5452474","0.5450045","0.5449476","0.5423658","0.54224","0.54107565","0.5403317","0.5395726","0.53924805","0.53832054","0.53628075","0.53616446","0.53472173","0.5346129","0.5340433","0.5333289","0.5318538","0.53180265","0.53065765","0.530597","0.52990043","0.5298349","0.52971345","0.5295205","0.5278749","0.5270834","0.52654773","0.5262398","0.5250549","0.5248382","0.5242905","0.5242619","0.52397984","0.52320665","0.5230037","0.52284026","0.5225841","0.5215084","0.5210755","0.51870567","0.51862025","0.5179022","0.5167569","0.5163325","0.5144911","0.5142675","0.51424426","0.5140266","0.5139195","0.5137535","0.513338","0.51294893","0.51250726","0.51211363","0.51180035","0.51087695","0.5097878","0.5092669","0.5089772","0.5085121","0.508053","0.50756234","0.5070991","0.5070991","0.5069065","0.5066955","0.5066272","0.506526","0.506431","0.5061706","0.505862"],"string":"[\n \"0.75539035\",\n \"0.66137195\",\n \"0.6487311\",\n \"0.64123094\",\n \"0.61329263\",\n \"0.6101124\",\n \"0.60908806\",\n \"0.60279024\",\n \"0.60243684\",\n \"0.59840643\",\n \"0.59765226\",\n \"0.58507067\",\n \"0.585065\",\n \"0.58390087\",\n \"0.58205575\",\n \"0.58169997\",\n \"0.5809403\",\n \"0.58001035\",\n \"0.56534475\",\n \"0.5646809\",\n \"0.5583415\",\n \"0.55652213\",\n \"0.5553969\",\n \"0.5545845\",\n \"0.5525345\",\n \"0.5517458\",\n \"0.5493895\",\n \"0.54818946\",\n \"0.548075\",\n \"0.5452474\",\n \"0.5450045\",\n \"0.5449476\",\n \"0.5423658\",\n \"0.54224\",\n \"0.54107565\",\n \"0.5403317\",\n \"0.5395726\",\n \"0.53924805\",\n \"0.53832054\",\n \"0.53628075\",\n \"0.53616446\",\n \"0.53472173\",\n \"0.5346129\",\n \"0.5340433\",\n \"0.5333289\",\n \"0.5318538\",\n \"0.53180265\",\n \"0.53065765\",\n \"0.530597\",\n \"0.52990043\",\n \"0.5298349\",\n \"0.52971345\",\n \"0.5295205\",\n \"0.5278749\",\n \"0.5270834\",\n \"0.52654773\",\n \"0.5262398\",\n \"0.5250549\",\n \"0.5248382\",\n \"0.5242905\",\n \"0.5242619\",\n \"0.52397984\",\n \"0.52320665\",\n \"0.5230037\",\n \"0.52284026\",\n \"0.5225841\",\n \"0.5215084\",\n \"0.5210755\",\n \"0.51870567\",\n \"0.51862025\",\n \"0.5179022\",\n \"0.5167569\",\n \"0.5163325\",\n \"0.5144911\",\n \"0.5142675\",\n \"0.51424426\",\n \"0.5140266\",\n \"0.5139195\",\n \"0.5137535\",\n \"0.513338\",\n \"0.51294893\",\n \"0.51250726\",\n \"0.51211363\",\n \"0.51180035\",\n \"0.51087695\",\n \"0.5097878\",\n \"0.5092669\",\n \"0.5089772\",\n \"0.5085121\",\n \"0.508053\",\n \"0.50756234\",\n \"0.5070991\",\n \"0.5070991\",\n \"0.5069065\",\n \"0.5066955\",\n \"0.5066272\",\n \"0.506526\",\n \"0.506431\",\n \"0.5061706\",\n \"0.505862\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7909768"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11584,"cells":{"query":{"kind":"string","value":"Writes a dictionary to an xml file in the form of etc. attribute_name>The name of the dictonary attribute to write. xmlwriter>The xml writer to write with. write_empty>A bool of whether to write empty values to the xml file. Default is don't write them."},"document":{"kind":"string","value":"def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n if attribute_name in (\"IllegalCharacters\", \"Months\"):\r\n write_empty = True\r\n dictionary = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for key in dictionary:\r\n if dictionary[key] or write_empty:\r\n xmlwriter.WriteStartElement(\"Item\")\r\n xmlwriter.WriteStartAttribute(\"Name\")\r\n xmlwriter.WriteValue(key)\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteStartAttribute(\"Value\")\r\n xmlwriter.WriteValue(dictionary[key])\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteEndElement()\r\n xmlwriter.WriteEndElement()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def writeDictToXMLFile(outfile, target, dict):\n targetStr = \"\\t\\t<Target>%s</Target>\\n\" % (escape(target),)\n for key in dict.keys():\n outfile.write('\\t<AVU>\\n')\n outfile.write(targetStr)\n outfile.write(\"\\t\\t<Attribute>%s</Attribute>\\n\" % (escape(key),) )\n outfile.write(\"\\t\\t<Value>%s</Value>\\n\" % (escape(dict[key]),) )\n outfile.write('\\t\\t<Unit />\\n')\n outfile.write('\\t</AVU>\\n')","def write_to_xml(dictData, metadata, xmlfile):\n\tfout = codecs.open(xmlfile, 'w', 'utf-8')\n\tfout.write('<?xml version = \"1.0\" encoding = \"UTF-8\" standalone = \"no\" ?>\\n')\n\tfout.write('<?xml-stylesheet type=\"text/xsl\" href=\"maketable.xsl\"?>\\n')\n\tfout.write('<root>\\n')\n\tfout.write('<meta>\\n')\n\tfor key, value in metadata.items():\n\t\tfout.write('<' + key + '>' + value + '</' + key + '>\\n')\n\tfout.write('</meta>\\n')\n\tfout.write('<content>\\n')\n\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\n\t\txmlline = ''\n\t\txmlline += '<word><headword>' + hw + '</headword><meanings>'\n\t\tfor meaning in meanings:\n\t\t\txmlline += '<m>' + meaning + '</m>'\n\t\txmlline += '</meanings>'\n\t\txmlline += '<verse>'\n\t\tlines = verse.split('<BR>')\n\t\tfor line in lines:\n\t\t\txmlline += '<line>' + line + '</line>'\n\t\txmlline += '</verse>'\n\t\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\n\t\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\n\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\tfout.write(xmlline + '\\n')\n\t\txmlline = ''\n\tfout.write('</content>\\n</root>')\n\tfout.close()\n\n\t# Give some summary to the user\n\tprint('XML file generated. Success!')\n\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))","def write_dictionary(args, dictio):\n if not args.dictfile.endswith(\".file\"):\n args.dictfile += \".file\"\n with open(args.dictfile, \"wb\") as f:\n dump(dictio, f, protocol=HIGHEST_PROTOCOL)","def write(self, file_or_filename):\n etMap = revert(self)\n xmlTree = ET.ElementTree(etMap)\n xmlTree.write(file_or_filename)","def file_write(filename, dic):\n d = dic \n f = open(filename, 'w') \n f.write(str(d))\n f.close()","def write_dict(outputfilename, dictionary):\r\n # May want to modify this code to pickle the key and value and alter the read dictionary to do the same.\r\n try:\r\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\r\n for key, value in dictionary.items():\r\n outfile.write('%s,%s\\n' % (key, value))\r\n except:\r\n input(\"File still open! Please close and press enter to continue\")\r\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\r\n for key, value in dictionary.items():\r\n outfile.write('%s,%s\\n' % (key, value))","def write_dictionary(dictionary, path):\n with open(path, 'w+') as file:\n file.truncate() # Erase contents of config file\n for key in dictionary:\n file.write(f'{key}={dictionary[key]}\\n')","def SaveDictFile(file,dict_):\n with open(file,'w') as f:\n for id in dict_:\n f.write(dict_[id] + \"=\" + str(id)+\"\\n\")","def _write_dict_to_mdin(self, f, dictionary):\n\n for key, val in dictionary.items():\n if val is not None:\n f.write(\" {:15s} {:s},\\n\".format(key+\" =\", str(val)))\n f.write(\" /\\n\")","def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\r\n string = getattr(self, attribute_name)\r\n if string or write_empty:\r\n xmlwriter.WriteElementString(attribute_name, string)","def GenerateXML(dictionary, fileName=\"labelling.xml\") : \n root = gfg.Element(\"annotation\") \n #the big section is called Annotation\n for key in dictionary:\n #for every polygon list in inside object witho subelement name and attributes and the type \"polygon\"\n objectElement = gfg.Element(\"object\") \n root.append(objectElement) \n subElement1 = gfg.SubElement(objectElement, \"name:\".strip(\":\"))\n subElement1.text = str(dictionary[key][\"name\"])\n subElement2 = gfg.SubElement(objectElement, \"attributes\".strip(\":\"))\n subElement2.text = str(dictionary[key][\"attributes\"])\n subElement3 = gfg.SubElement(objectElement, \"polygon\")\n \n for i in range(0, len(dictionary[key])-2):\n #for every vertex of the polygon list it's rounded x, y on xml\n SubInsidePolygon = gfg.SubElement(subElement3, \"pt\")\n sub_x = gfg.SubElement(SubInsidePolygon, \"x\")\n sub_y = gfg.SubElement(SubInsidePolygon, \"y\")\n sub_x.text = str(int(round(dictionary[key][\"x_y_\" + str(i)][0])))\n sub_y.text = str(int(round(dictionary[key][\"x_y_\" + str(i)][1])))\n tree = gfg.ElementTree(root) \n #create the xml tree\n with open (fileName, \"wb\") as files : \n tree.write(files) \n #if xml does not exist create one otherwise rewrite to it","def _write_event_xml(infodict, event_dir):\n event_info = infodict['input']['event_information']\n\n otime = datetime.strptime(event_info['origin_time'], '%Y-%m-%dT%H:%M:%SZ')\n eqdict = {'id': event_info['event_id'],\n 'lat': '%.4f' % event_info['latitude'],\n 'lon': '%.4f' % event_info['longitude'],\n 'depth': '%.1f' % event_info['depth'],\n 'mag': '%.1f' % event_info['magnitude'],\n 'year': '%i' % otime.year,\n 'month': '%i' % otime.month,\n 'day': '%i' % otime.day,\n 'hour': '%i' % otime.hour,\n 'minute': '%i' % otime.minute,\n 'second': '%i' % otime.second,\n 'timezone': 'GMT',\n 'locstring': event_info['event_description']}\n eq = Element('earthquake', eqdict)\n xmlfile = os.path.join(event_dir, 'event.xml')\n tree = ElementTree(eq)\n tree.write(xmlfile)","def meta2xml(meta, filename):\n\n # this is stupid, just use dict2xml\n xml = dict2xml(meta)\n with open(filename, 'w+') as output:\n output.write(xml)","def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n attribute_list = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for item in attribute_list:\r\n if item or write_empty:\r\n xmlwriter.WriteElementString(\"Item\", item)\r\n xmlwriter.WriteEndElement()","def write_config_file(input_dict, file_name, entry_char='>',\n attribution_char='=', usekeys=None):\n fp = open(file_name, \"w\")\n fp.write(write_config_string(input_dict, entry_char, attribution_char,\n usekeys))\n fp.close()","def xml_obj(dict):\n string = \"\"\n for key in dict.keys():\n string += ' <{}>{}</{}>\\n'.format(key, dict[key], key)\n return string","def write_completed_dictionary_to_file(the_dict):\n\ttry:\n\t\toutputLocation = open('usable_dictionary.json','w')\n\t\toutputString = str(the_dict)\n\t\toutputLocation.write(outputString)\n\t\toutputLocation.close()\n\texcept IOError:\n\t\tprint (\"could not open file\")","def ConvertToXML (given_dict) :\r\n stream_thing = cStringIO.StringIO()\r\n WriteToXMLStream(given_dict, stream_thing, 'top')\r\n return stream_thing.getvalue()","def _output_dict(self):\n lang = self.ddnGuiLanguage.get()\n\n fileout = os.path.normpath('{}/{}-{}.xml'.\\\n format(self.MapCreator, self.Source, self.ddnCurProject.get()))\n linesout = ['<?xml version=\"1.0\" encoding=\"UTF-8\"?>', \\\n '<DictionarySet xmlns:mc=\"urn:fmosoft-map-creator\" xmlns=\"urn:fmosoft-map-creator\" Version=\"1\">', \\\n ' <Dictionary SourceLanguage=\"{}\" SourceLanguageIsPredefined=\"true\" TargetLanguage=\"{}\" TargetLanguageIsPredefined=\"false\">'.\\\n format(self.Source, self.ddnCurProject.get()), \\\n ]\n for child in self.tree.get_children('approved'):\n vv = self.tree.item(child)['values']\n linesout.append(' <Translation Source=\"{}\" Target=\"{}\"/>'.format(vv[0], vv[1]))\n linesout.append(' </Dictionary>')\n linesout.append('</DictionarySet>')\n linesout.append('')\n\n if os.path.exists(fileout):\n os.remove(fileout)\n\n if fileout:\n output = codecs.open(fileout, mode='w', encoding='utf-8')\n output.write('\\n'.join(linesout))\n output.close()\n pass","def write_bool_to_xml(self, attribute_name, xmlwriter):\r\n xmlwriter.WriteStartElement(attribute_name)\r\n xmlwriter.WriteValue(getattr(self, attribute_name))\r\n xmlwriter.WriteEndElement()","def write_dictionary():\n for dictionary in dictionaries:\n for values in dictionary.values():\n with open(sys.argv[1] + \"-1\", \"ab\") as dest_file:\n dest_file.write(values)","def save_to_file():\n dict_from_file.update(temp_dict)\n plik=open('data.txt', 'w')\n for key in dict_from_file.keys():\n plik.write(key)\n plik.write(\" \")\n plik.write(str(dict_from_file[key][0]))\n plik.write(' ')\n plik.write(dict_from_file[key][1].replace(' ','_'))\n plik.write(' ')\n plik.write(str(dict_from_file[key][2]))\n plik.write('\\n')","def print_xml_config(config_dictionary,**kwargs):\n \n #Check if we have passed a filename\n #If not, pass a default filename\n if 'config_file' in kwargs:\n config_file = kwargs['config_file']\n else:\n config_file = 'ebtel_config.xml'\n \n #Open the file\n f = open(config_file,'w')\n \n #Print necessary header info\n f.write('<?xml version=\"1.0\" ?>\\n')\n f.write('<input>\\n')\n\n #Loop through dictionary and print to xml file\n for key in config_dictionary:\n #Print tab delimiter, brackets and keyword\n f.write('\\t<')\n f.write(key)\n f.write('>')\n #Check if entry is a list\n #If so print it as a list\n if isinstance(config_dictionary[key],list) or type(config_dictionary[key]).__name__ == 'ndarray':\n #Make temporary list\n temp = config_dictionary[key]\n #Skip to new line\n f.write('\\n')\n #Begin loop over list\n for i in range(len(config_dictionary[key])):\n f.write('\\t\\t<')\n f.write(key+str(i))\n f.write('>')\n f.write(str(temp[i]))\n f.write('</')\n f.write(key+str(i))\n f.write('>\\n')\n #Print additional tab to preserve alignment\n f.write('\\t')\n else:\n #Print value\n f.write(str(config_dictionary[key]))\n #Close the brackets and print newline\n f.write('</')\n f.write(key)\n f.write('>\\n')\n \n #Close the main node of the file\n f.write('</input>')\n \n #Close the file\n f.close()","def xmlwrite(self, doc, filename):\n pathname = os.path.join(self.session.session_dir, filename)\n f = open(pathname, \"w\")\n doc.writexml(writer=f, indent=\"\", addindent=\" \", newl=\"\\n\", encoding=\"UTF-8\")\n f.close()","def save_to_xml(self, xwriter):\r\n\r\n xwriter.WriteStartElement(\"Profile\")\r\n xwriter.WriteAttributeString(\"Name\", self.Name)\r\n xwriter.WriteStartAttribute(\"Version\")\r\n xwriter.WriteValue(self.Version)\r\n xwriter.WriteEndAttribute()\r\n\r\n for var_name in self.__dict__:\r\n var_type = type(getattr(self, var_name))\r\n\r\n if var_type is str and var_name != \"Name\":\r\n self.write_string_to_xml(var_name, xwriter)\r\n\r\n elif var_type is bool:\r\n self.write_bool_to_xml(var_name, xwriter)\r\n\r\n elif var_type is dict:\r\n self.write_dict_to_xml(var_name, xwriter)\r\n\r\n elif var_type is list and var_name != \"ExcludeRules\":\r\n self.write_list_to_xml(var_name, xwriter)\r\n\r\n xwriter.WriteStartElement(\"ExcludeRules\")\r\n xwriter.WriteAttributeString(\"Operator\", self.ExcludeOperator)\r\n xwriter.WriteAttributeString(\"ExcludeMode\", self.ExcludeMode)\r\n for rule in self.ExcludeRules:\r\n if rule:\r\n rule.save_xml(xwriter)\r\n xwriter.WriteEndElement()\r\n \r\n xwriter.WriteEndElement()","def WriteFile(self, filename) :\n\n # open file for writing:\n f = open(filename, 'w')\n\n ## loop over key/value pairs:\n #for k,v in self.iteritems():\n # # add line; at least the specified number of characters \n # # is used for the key:\n # f.write( '%-20s:%s\\n' % (k,v) )\n ##endfor\n\n # write processed input:\n f.writelines(self.outfile)\n \n # close file:\n f.close()","def GdictWrite(filename=\"turtle_docstringdict\"):\n docsdict = {}\n\n for methodname in gScreenFunc:\n key = \"_Screen.\"+methodname\n docsdict[key] = eval(key).__doc__\n for methodname in gMoveFunc:\n key = \"Myturtle.\"+methodname\n docsdict[key] = eval(key).__doc__\n\n with open(\"%s.py\" % filename,\"w\") as f:\n keys = sorted(x for x in docsdict\n if x.split('.')[1] not in _alias_list)\n f.write('docsdict = {\\n\\n')\n for key in keys[:-1]:\n f.write('%s :\\n' % repr(key))\n f.write(' \"\"\"%s\\n\"\"\",\\n\\n' % docsdict[key])\n key = keys[-1]\n f.write('%s :\\n' % repr(key))\n f.write(' \"\"\"%s\\n\"\"\"\\n\\n' % docsdict[key])\n f.write(\"}\\n\")\n f.close()","def write(self, fp, **kwds):\n json.dump(self._dict, fp)","def xmlWrite(self, xmlWriter, font, value, name, attrs):\n raise NotImplementedError(self)","def save(self):\n if not os.path.exists(self.dictionary_save_path) and self.dictionary_save_path != \"\":\n os.makedirs(self.dictionary_save_path)\n with open(self.dictionary_save_path + \"dictionary\", 'w') as f:\n f.write(\"{}\\n\".format(len(self.dictionary)))\n\n for word in self.dictionary:\n f.write(\"{}\\t{}\\n\".format(self.dictionary[word]['id'], word))","def save_dictionary(myDict, filepath):\r\n # context manager write binary\r\n with open(filepath, 'wb') as file:\r\n # pickle save\r\n pickle.dump(myDict, file)","def write_output(word_dict):\n # create an empty output.txt file\n output = open('output.txt', 'w')\n\n for i in words_dict: \n output.write(i + \" : \" + str(words_dict[i]) + \"\\n\")","def save_dict_to_file(dictionary: dict, dst_path: str) -> None:\n with io.open(file=dst_path, mode=\"w\", encoding=\"utf-8\") as dst:\n for k, v in dictionary.items():\n dst.write(f\"{k} {v}\\n\")\n dst.close()","def dump_json_to_file(dictionary, file_name):\n if file_name is not None:\n IOUtils.mkdirf(file_name)\n with open(file_name, 'w') as file_obj:\n json.dump(dictionary, file_obj, indent=4)","def _write_packet_dict(ctx, package_dict):\n p4gf_util.write_dict_to_file(package_dict, _packet_filename(ctx.config.repo_name))","def _save_dict(self, dict_name=None):\n if dict_name is None:\n if not os.path.exists(self.config.data.dict_dir):\n os.makedirs(self.config.data.dict_dir)\n for name in self.dict_names:\n self._save_dict(name)\n else:\n dict_idx = self.dict_names.index(dict_name)\n dict_file = open(self.dict_files[dict_idx], \"w\")\n id_to_vocab_dict_map = self.id_to_vocab_dict_list[dict_idx]\n index = 0\n for vocab, count in self.count_list[dict_idx]:\n id_to_vocab_dict_map[index] = vocab\n index += 1\n dict_file.write(\"%s\\t%d\\n\" % (vocab, count))\n dict_file.close()","def _write_dict(pdf, input_dict, indent=0, border=BORDER):\n for key, value in input_dict.items():\n pdf.set_font(\"arial\", \"B\", 14)\n pdf.cell(75, 5, key, border, 1, \"L\")\n pdf.cell(indent, 0)\n pdf.set_font(\"arial\", \"\", 12)\n pdf.multi_cell(150, 5, value, border, \"L\")\n pdf.ln(h=5)","def XMLWriter(\n fd,\n encoding=\"utf-8\",\n pretty=True,\n compactempty=True,\n indentation=_DEFAULT_INDENTATION\n):\n return _document(fd, encoding, pretty, compactempty, indentation)","def serialize_dictionary(self, dic_dir: str ,terms_id: str, id_terms: str, xml_file_name = 'test'):\n if not os.path.exists(dic_dir):\n os.mkdir(dic_dir)\n with open(os.path.join(dic_dir,xml_file_name+'_'+terms_id), 'wb') as f:\n pickle.dump(self.term2id_dict, f)\n with open(os.path.join(dic_dir,xml_file_name+'_'+id_terms), 'wb') as f:\n pickle.dump(self.id2term_dict, f)","def write_to_xml(filename, xmlelement):\n xmlpretty = prettify(xmlelement)\n with open(filename, 'w') as f:\n f.write(xmlpretty)","def dict_print(self, output_file = \"dict.csv\"):\t\n\n\t\twith codecs.open(output_file,\"w\",encoding='utf-8') as f:\n\t\t\tfor (v,k) in self.token_key.items(): f.write(\"%s,%d\\n\" % (v,k))","def save_plots(plot_dict, filename) :\n outfile = ROOT.TFile(filename, \"RECREATE\")\n\n for key in sorted(plot_dict) :\n if type( plot_dict[key] ) is types.DictType :\n directory = outfile.mkdir( key )\n directory.cd()\n save_plot( plot_dict[key], directory )\n outfile.cd()\n elif plot_dict[key] is not None :\n plot_dict[key].Write()\n \n outfile.Close()","def write(self, filename, mode=\"w\"):\n d = self._result_dict\n val = yaml.safe_dump(d, default_flow_style=False)\n\n with open(str(filename), mode) as outfile:\n outfile.write(val)","def write_dict_txtfile(input_file_name, data_dict):\n \n output_file = open(input_file_name, 'w')\n output_file.write('Human Metabolome database')\n output_file.write('\\n\\n')\n\n for keys, values in data_dict.items():\n output_file.write(str(keys)+', '+str(values)+'\\n')","def save(self, filename=None):\n f = filename if filename else self.path\n etree.register_namespace('', TEI)\n etree.register_namespace('mith', MITH)\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')","def json_write(path, dictionary):\n json_output = json.dumps(dictionary, indent=4)\n with open(path, \"w\", encoding=\"utf-8\") as f:\n f.write(json_output)","def write(filename, rcdict) :\n\n # open file for writing:\n f = open(filename, 'w')\n\n # loop over key/value pairs:\n for k, v in rcdict.items():\n # add line; at least the specified number of characters \n # is used for the key:\n f.write('%-20s:%s\\n' % (k, v))\n #endfor\n\n # close file:\n f.close()","def save(self, filename=None):\n\n if filename is None:\n filename = self.__filename\n\n file = open(filename, 'w')\n for key in self:\n if key[:2] == '__':\n continue\n file.write(key)\n file.write(' = ')\n if key in self.__orig:\n value = self.__orig[key]\n else:\n value = self[key]\n file.write(str(value))\n\n file.close()","def write(self, filename: str):\n obj = self.to_dict(self)\n config.write(obj, filename)","def write_pecfile_dict(output_file, record_dict):\n for key in sorted(record_dict.keys()):\n output_file.write('%s\\n' % str(record_dict[key]))\n return output_file","def save_plot(plot_dict, outfile) :\n for key in sorted(plot_dict) :\n if type( plot_dict[key] ) is types.DictType :\n directory = outfile.mkdir( key )\n directory.cd()\n save_plot( plot_dict[key], directory )\n outfile.cd()\n else :\n plot_dict[key].Write()","def dump(mapping, fp):\n fp.write(dumps(mapping))","def write_taxonomic_key(outfile: TextIO, do_print: bool, taxkey: TMB_TaxKeyGen.KeyText,\n location: Optional[TMB_Classes.LocationClass]) -> None:\n if do_print:\n start_page_division(outfile, \"base_page\")\n else:\n if location is None:\n common_header_part1(outfile, \"Fiddler Crab Taxonomic Key\", indexpath=\"../../\")\n else:\n common_header_part1(outfile, location.trimmed_name + \": Taxonomic Key\", indexpath=\"../../\")\n outfile.writelines(taxkey.header)\n common_header_part2(outfile, indexpath=\"../../\")\n\n if location is None:\n outfile.write(\" <header id=\\\"all_taxkey.html\\\">\\n\")\n outfile.write(\" <h1 class=\\\"nobookmark\\\">Taxonomic Key to All Fiddler Crabs</h1>\\n\")\n else:\n outfile.write(\" <header id=\\\"\" + place_to_filename(location.name) + \"_taxkey.html\\\">\\n\")\n outfile.write(\" <h1 class=\\\"nobookmark\\\">\" + location.trimmed_name + \": Taxonomic Key</h1>\\n\")\n if not do_print:\n outfile.write(\" <nav>\\n\")\n outfile.write(\" <ul>\\n\")\n if location is not None:\n outfile.write(\" <li>\" + create_location_link(location, location.trimmed_name, do_print, path=\"../\",\n inc_icon=True) + \"</li>\\n\")\n outfile.write(\" <li><a href=\\\"../index.html\\\">\" + fetch_fa_glyph(\"index\") +\n \"Location Index</a></li>\\n\")\n outfile.write(\" <li><a href=\\\"index.html\\\">\" + fetch_fa_glyph(\"tax key\") +\n \"Taxonomic Key Guide</a></li>\\n\")\n outfile.write(\" </ul>\\n\")\n outfile.write(\" </nav>\\n\")\n outfile.write(\" </header>\\n\")\n outfile.write(\" <p> </p>\\n\")\n\n for line in taxkey.body:\n outfile.write(replace_species_in_string(line, True, do_print, \"../../\"))\n\n if do_print:\n end_page_division(outfile)\n else:\n common_html_footer(outfile)","def dict2file(dict, filename, foldername):\n if foldername:\n if not os.path.exists(\"../Created_QD/\" + foldername):\n os.makedirs(\"../Created_QD/\" + foldername)\n file = open(\"../Created_QD/\" + foldername + \"/\" + filename + \".xyz\", \"w\")\n else:\n file = open(\"../Created_QD/\" + filename + \".xyz\", \"w\")\n file.write(\" \\n\\n\")\n for atom, values in dict.items():\n file.write(values['element'] + \"\\t\" + str(values['coor'][0]) + \"\\t\\t\" +\n str(values['coor'][1]) + \"\\t\\t\" + str(values['coor'][2]) + \"\\n\")\n file.seek(0)\n file.write(str(len(dict)))\n file.close()\n print(\"\\nQuantum Dot created :)\")","def write(self):\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\"UTF-8\")\n with open(self.xml_file, 'w') as f:\n f.write(temp_string)\n # f = open(self.xml_file, \"w\")\n # f.write(temp_string)\n # f.close()","def write_json(dictionary, filename):\r\n with open(filename, 'w') as data_file:\r\n json.dump(dictionary, data_file, indent=4, sort_keys=True)\r\n print('--> Wrote ' + os.path.basename(filename))","def serialize_mapping_to_file(\n file_path, alignment, onto_one=None, onto_two=None, extension=None\n):\n with open(file_path, \"w\", encoding=\"utf-8\") as out_file:\n out_file.write(__get_xml_intro(onto_one, onto_two, extension))\n for correspondence in alignment:\n out_file.write(__get_mapping_string(*correspondence))\n out_file.write(__get_xml_outro())","def convert_dict_to_xml(data_dict):\n from dicttoxml import dicttoxml\n\n return dicttoxml(data_dict, attr_type=False)","def dump_dict(dct, outpath='./dict.txt'):\n with open( Path(outpath), 'w' ) as file:\n for k in sorted(dct.keys()):\n file.write('{}: {}\\n'.format(k, dct[k]))","def store(my_dict, filename):\n my_json = json.dumps(my_dict)\n f = open(filename,\"w\")\n f.write(my_json)\n f.close()","def testFlatDict(self):\n ddict = {\n \"group/group/dataset\": 10,\n \"group/group/dataset@attr\": 11,\n \"group/group@attr\": 12,\n }\n with h5py.File(self.h5_fname, \"w\") as h5file:\n dictdump.dicttonx(ddict, h5file)\n self.assertEqual(h5file[\"group/group/dataset\"][()], 10)\n self.assertEqual(h5file[\"group/group/dataset\"].attrs['attr'], 11)\n self.assertEqual(h5file[\"group/group\"].attrs['attr'], 12)","def save_meta_file(gen_dict, f_name):\r\n logger = custom_logger.CustomLogger(run_id+':'+file_id)\r\n filename = run_id+'_'+ f_name +'.meta'\r\n f = open(os.path.join(unique_op_dir, filename),'a')\r\n print('Output stored in %s'%(str(os.path.join(unique_op_dir, filename))))\r\n logger.info('Output stored in %s'%(str(os.path.join(unique_op_dir, filename))))\r\n for key, val in gen_dict.items():\r\n line = str(key)+\" : \"+str(val)+\"\\n\"\r\n f.write(line)","def mapSave(regions,filename):\n root = ET.Element('map')\n for name,table in regions.items():\n node = ET.SubElement(root,'region')\n node.set('name',name)\n if table.has_key('value'): node.set('value',str(table['value']))\n if table.has_key('occupants'): node.set('occupants',str(table['occupants']))\n node.set('owner',str(table['owner']))\n for neighbor in table['neighbors']:\n subnode = ET.SubElement(node,'neighbor')\n subnode.set('name',neighbor)\n tree = ET.ElementTree(root)\n tree.write(filename,pretty_print=True)\n return tree","def dict_to_dom(root_node, xml_dict):\n\n if '_content' in list(xml_dict.keys()):\n\n root_node.appendChild(\n root_node.ownerDocument.createTextNode(\n convert_to_str(xml_dict['_content'])\n )\n )\n\n for key, value in xml_dict.items():\n\n if key == '_content':\n continue\n\n if type(value) == dict:\n\n # Root node\n\n tmp_node = root_node.ownerDocument.createElement(key)\n\n dict_to_dom(tmp_node, value)\n\n root_node.appendChild(tmp_node)\n\n elif type(value) == list:\n\n for multinode in value:\n\n tmp_node = root_node.ownerDocument.createElement(key)\n\n dict_to_dom(tmp_node, multinode)\n\n root_node.appendChild(tmp_node)\n\n else:\n\n # Attributes\n\n root_node.setAttribute(\n key,\n convert_to_str(value)\n )","def write_dict_as_wiki(self, aDict, name, directory):\n with codecs.open(os.path.join(directory, name), 'w', 'utf-8') as wikipage:\n wikipage.write(\"{|\\nItem | Count | Tag | Categories\")\n items = aDict.items()\n items.sort(key=lambda x: x[1], reverse=True)\n for item in items:\n values = (self.alignment_config_items['alignment_template'],\n {'item': item[0], 'count': item[1],\n 'value': \"\", 'categories': \"\"})\n table_line = '\\n' + textlib.glue_template_and_params(values)\n wikipage.write(unicode(table_line))\n wikipage.write(\"\\n|}\")","def write(name, keyword, domain, citation, author, description, species, version, contact, license, values,\n functions, output, value_prefix):\n write_namespace(\n name, keyword, domain, author, citation, values,\n namespace_description=description,\n namespace_species=species,\n namespace_version=version,\n author_contact=contact,\n author_copyright=license,\n functions=functions,\n file=output,\n value_prefix=value_prefix\n )","def saveSessionToXML(self, filename):\r\n xmlStr = self.createXMLStr()\r\n \r\n #Write to the file\r\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\r\n xmlFile = open(filename, 'w')\r\n xmlFile.write(xmlStr)\r\n xmlFile.close()","def save(self) -> None:\n with open(dict_path, 'w', encoding='utf-8') as dictionary_file:\n json.dump(self.data, dictionary_file, indent=2, separators=(',', ':'), ensure_ascii=False)","def test_dict_to_xml(self):\n xmlns = {\n '_': utils.NETCONF_NAMESPACE,\n 'nm': 's'\n }\n\n xml_node = utils.generate_xml_node(\n self.SIMPLE_DICT,\n xmlns,\n 'rpc'\n )\n\n xml_node_string = etree.tostring(\n xml_node, pretty_print=False\n )\n\n self.assertEqual(\n json.dumps(xmltodict.parse(xml_node_string.decode('utf-8')),\n indent=4, sort_keys=True),\n json.dumps(xmltodict.parse(\n \"\"\"<rpc xmlns:nm=\"s\" xmlns=\"urn:ietf:params:xml:ns:netc\"\"\" +\n \"\"\"onf:base:1.0\"><a xmlns:ns0=\"urn:ietf:params:xml:ns:n\"\"\" +\n \"\"\"etconf:base:1.0\"><b ns0:m=\"g\">b</b><c nm:nm=\"update\">\"\"\" +\n \"\"\"<f>1</f><e>str</e><d>1</d><d>2</d><d>3</d></c></a>\"\"\" +\n \"\"\"</rpc>\"\"\"), indent=4, sort_keys=True)\n )","def serialize_dictionaries(self,\n output_file,\n restrict_to = None):\n io.write_symbol_table_dict(output_file, self._symtab, restrict_to)","def write_csv(dictionary):\n\n with open(constant.METADATA_FILE, 'a', newline='') as file:\n writer = csv.DictWriter(file, fieldnames=dictionary.keys())\n try:\n writer.writerow(dictionary)\n except Exception:\n writeable = {k: str(v).encode(\"utf-8\") for k, v in dictionary.items()}\n writer.writerow(writeable)","def write(self, file=sys.stdout):\n d = self.to_dict()\n if d:\n yaml.dump([d], file, default_flow_style=False)","def dump_dict(dct, outpath='./dict.txt'):\n with open(Path(outpath), 'w') as file:\n for k in sorted(dct.keys()):\n file.write('{}: {}\\n'.format(k, dct[k]))","def save_data(self, **kwargs):\n\n file = kwargs['file']\n with open(file, 'w') as data_file:\n for item in self._items.values():\n data_string = '{'\n item_dict = item.to_dict()\n\n for key, value in item_dict.items():\n data_string += f'\"{key}\":\"{value}\",'\n\n data_string = data_string[:-1]\n data_string += '}\\n'\n data_file.write(data_string)","def writeFile(self, filename):\n s = ET.tostring(self._root)\n\n #Remove all formatting\n s = s.replace('\\n','')\n s = s.replace('\\t','')\n s = s.replace('\\r','')\n\n f = open(filename, 'w')\n f.write(minidom.parseString(s).toprettyxml())\n f.close()","def XMLWrite(one, two, three, four, five, six, seven, eight):\n filePath = \"/mnt/RAM/kanban.xml\"\n xmlFile = open(filePath, 'w')\n\n xmlFile.write('<kanbanShelf>\\n')\n xmlFile.write(' <one>%s</one>\\n' % one)\n xmlFile.write(' <two>%s</two>\\n' % two)\n xmlFile.write(' <three>%s</three>\\n' % three)\n xmlFile.write(' <four>%s</four>\\n' % four)\n xmlFile.write(' <five>%s</five>\\n' % five)\n xmlFile.write(' <six>%s</six>\\n' % six)\n xmlFile.write(' <seven>%s</seven>\\n' % seven)\n xmlFile.write(' <eight>%s</eight>\\n' % eight)\n xmlFile.write('</kanbanShelf>')","def write_kdv_dictionary(data, file_name, get_is_active=True, extension='.txt'):\n\n file_name += extension\n\n with open(file_name, 'w', encoding='utf-8') as my_file:\n my_file.write('{0}\\n'.format( data[0].file_output(get_header=True)) )\n\n for key, value in data.items():\n if get_is_active is True:\n if value.IsActive is True:\n my_file.write('{0}\\n'.format(value.file_output()))\n else:\n my_file.write('{0}\\n'.format(value.file_output()))","def print_xml(tree, file):\n tree.write(file, encoding=\"utf-8\", xml_declaration=True)","def write(self):\r\n for prop in self.prpnames:\r\n elem = SubElement(self._root, prop)\r\n data = self.__getattribute__(prop)\r\n if self.prpnames[prop]['type'] == \"text\":\r\n elem.text = data\r\n elif self.prpnames[prop]['type'] == 'list':\r\n for x in data:\r\n SubElement(elem, 'regel').text = x\r\n elif self.prpnames[prop]['type'] == 'attr':\r\n elem.set(self.prpnames[prop]['naam'], data)\r\n tree = ElementTree(self._root)\r\n tree.write(self._fn)\r\n if not self.exists:\r\n self.exists = True","def write(name, keyword, domain, citation, author, description, species, version, contact, license, values, output):\n write_namespace(\n name, keyword, domain, author, citation, values,\n namespace_description=description,\n namespace_species=species,\n namespace_version=version,\n author_contact=contact,\n author_copyright=license,\n file=output,\n )","def testAttributes(self):\n ddict = {\n \"group\": {\"datatset\": \"hmmm\", (\"\", \"group_attr\"): 10},\n \"dataset\": \"aaaaaaaaaaaaaaa\",\n (\"\", \"root_attr\"): 11,\n (\"dataset\", \"dataset_attr\"): 12,\n (\"group\", \"group_attr2\"): 13,\n }\n with h5py.File(self.h5_fname, \"w\") as h5file:\n dictdump.dicttoh5(ddict, h5file)\n self.assertEqual(h5file[\"group\"].attrs['group_attr'], 10)\n self.assertEqual(h5file.attrs['root_attr'], 11)\n self.assertEqual(h5file[\"dataset\"].attrs['dataset_attr'], 12)\n self.assertEqual(h5file[\"group\"].attrs['group_attr2'], 13)","def print_dict(init_dict, file_name=\"test\"):\n ordered_dict = collections.OrderedDict()\n order = [\"SIMULATION\", \"PARAMS\", \"DIST\"]\n for key_ in order:\n ordered_dict[key_] = init_dict[key_]\n\n with open(\"{}.boupy.yml\".format(file_name), \"w\") as outfile:\n yaml.dump(ordered_dict, outfile, explicit_start=True, indent=4)","def getXML(self):\n\n def _getElementForMappingEntry(entry, mappingStyle):\n xmlDocTmp = Document()\n element = xmlDocTmp.createElement(mappingStyle)\n for k, v in viewitems(entry):\n # ignore empty, None or compiled regexp items into output\n if not v or (k == \"path-match-expr\"):\n continue\n element.setAttribute(k, str(v))\n return element\n\n xmlDoc = Document()\n root = xmlDoc.createElement(\"storage-mapping\") # root element name\n for mappingStyle, mappings in viewitems(self):\n for mapping in mappings:\n mapElem = _getElementForMappingEntry(mapping, mappingStyle)\n root.appendChild(mapElem)\n return root.toprettyxml()","def write_xml(self, xmlfile):\n system.xml.write_file(xmlfile, self.status, 'status')","def write_json_file(data, filename, dictionary=False):\n try:\n if dictionary:\n with open(filename, \"wb\") as outfile:\n json.dump(data, outfile)\n else:\n with open(filename, \"w\") as outfile:\n json.dump(data, outfile, indent=2)\n return True\n except OSError:\n return False","def _encode_dictionary(data, name=\"Second\", sub=False):\n\n if sub:\n root = ET.Element(\"Field\", {\"Name\": f'{name}', \"Type\": \"elsystem.collections.dictionary\"})\n else: \n root = ET.Element(\"elsystem.collections.dictionary\")\n\n items = ET.SubElement(root, 'Field', {'Name': 'Items', 'Type': 'elsystem.collections.vector'})\n\n index = 0\n\n for key, val in data.items():\n\n pair = ET.SubElement(items, 'Field', {'Name': f'E{index}', 'Type': 'elsystem.collections.pair'})\n \n if type(val) == dict:\n ET.SubElement(pair, 'Field', {'Name': 'First', 'Value': _encode_value(key)}) \n sub_dict = _encode_dictionary(data=val, name=\"Second\", sub=True)\n pair.append(sub_dict)\n elif type(val) == list:\n ET.SubElement(pair, 'Field', {'Name': 'First', 'Value': _encode_value(key)}) \n sub_vec = _encode_list(data=val, name=F'E{index}', sub=True)\n pair.append(sub_vec)\n else:\n ET.SubElement(pair, 'Field', {'Name': 'First', 'Value': _encode_value(key)}) \n ET.SubElement(pair, 'Field', {'Name': 'Second', 'Value': _encode_value(val)}) \n\n index += 1\n\n ET.SubElement(items, 'Field', {'Name': 'count', 'Value': _encode_value(index)})\n\n if sub:\n return root \n else:\n return ET.tostring(root)","def testAttributes(self):\n ddict = {\n \"group\": {\"dataset\": 100, \"@group_attr1\": 10},\n \"dataset\": 200,\n \"@root_attr\": 11,\n \"dataset@dataset_attr\": \"12\",\n \"group@group_attr2\": 13,\n }\n with h5py.File(self.h5_fname, \"w\") as h5file:\n dictdump.dicttonx(ddict, h5file)\n self.assertEqual(h5file[\"group\"].attrs['group_attr1'], 10)\n self.assertEqual(h5file.attrs['root_attr'], 11)\n self.assertEqual(h5file[\"dataset\"].attrs['dataset_attr'], \"12\")\n self.assertEqual(h5file[\"group\"].attrs['group_attr2'], 13)","def savemat(self, file_name, mdict=None, appendmat=True, **kwargs):\n # Set mdict default value to empty dictionary\n if mdict is None:\n mdict = {}\n\n # Merge mdict with attributes dictionary, giving mdict the upper-hand\n # in case of inconsistency\n dsavemat = {**vars(self), **mdict}\n\n # Save the merged dictionary to a .mat file\n scipy.io.savemat(file_name, dsavemat, appendmat, **kwargs)","def _write_json(fname, dictionary, overwrite=False, verbose=False):\n if op.exists(fname) and not overwrite:\n raise FileExistsError(f'\"{fname}\" already exists. '\n 'Please set overwrite to True.')\n\n json_output = json.dumps(dictionary, indent=4)\n with open(fname, 'w') as fid:\n fid.write(json_output)\n fid.write('\\n')\n\n if verbose is True:\n print(os.linesep + f\"Writing '{fname}'...\" + os.linesep)\n print(json_output)","def writexml(file):\n OUTFILE=open(file,\"w\")\n doc = xml.dom.minidom.Document()\n\n # Create the <dec_reg_list> base element\n decl_reg_list = doc.createElement(\"decl_reg_list\")\n doc.appendChild(decl_reg_list)\n\n regname_old=\"\"\n rows.pop(0)\n for row in rows:\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\n if regname != regname_old:\n # Create the register element\n register = doc.createElement(\"register\")\n register.setAttribute(\"name\", regname)\n register.setAttribute(\"offset\", offset)\n if default != \"\" : register.setAttribute(\"default\", default)\n register.setAttribute(\"type\", regtype)\n if expose_reg == \"1\": register.setAttribute(\"usr\", expose_reg)\n if depth != \"\": register.setAttribute(\"size\", depth)\n if incsz != \"\": register.setAttribute(\"incsz\", incsz)\n text = doc.createTextNode(regdesc)\n register.appendChild(text)\n decl_reg_list.appendChild(register)\n \n # Create the field element\n if bitname != \"\":\n field = doc.createElement(\"field\")\n field.setAttribute(\"name\", bitname)\n if loc !=\"\": field.setAttribute(\"loc\", addcolon(loc))\n if bittype != \"\": field.setAttribute(\"type\", bittype)\n if bitdesc != \"\":\n text = doc.createTextNode(bitdesc)\n field.appendChild(text)\n register.appendChild(field)\n regname_old = regname\n\n\n # Print our newly created XML\n #print doc.toprettyxml(indent=\" \")\n #OUTFILE.write(doc.saveXML(decl_reg_list))\n OUTFILE.write(doc.toprettyxml(indent=\" \"))\n OUTFILE.close()","def write_dump_file(dir_name, file_name, ext, datas):\n f_name = build_file_path(dir_name, file_name, ext)\n with open(f_name, \"w\") as fd_out:\n fd_out.write(\"Dump file for %s\" % file_name)\n for name, data in datas.items():\n fd_out.write(\"\\n\\n%s\\n%s\\n\\n\" % (\"-\" *80, name))\n if isinstance(data, dict):\n for key, value in data.items():\n if isinstance(value, OrderedDict):\n value = dict(value)\n fd_out.write('\"%s\": %s\\n' % (key, pprint.pformat(value, indent=4)))\n else:\n fd_out.write(pprint.pformat(data, indent=4))","def saving_file(xml):\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))","def write(self, key, value):\n if( self.storage is None ): \n self.storage = {};\n if( value is None ):\n self.storage[ key ] = None;\n del self.storage[ key ];\n else:\n self.storage[ key ] = value;","def save_settings(dic):\n json.dump(dic, open(\"resources/files/settings.txt\", \"w\"))\n\n # LEGACY\n # with open(\"resources/files/settings.txt\", \"w\", newline=\"\\n\") as w:\n # for sett, val in dic.items():\n # w.write(sett + '\\\\' + val + '\\n')","def writeXmlForDET(filename,trigger,listDict,stopInfo):\n # trigger == 'variable trigger'\n # Variables == 'variables changed in the branch control logic block'\n # associated_pb = 'CDF' in case multibranch needs to be performed\n # stopInfo {'end_time': end simulation time (already stopped), 'end_ts': end time step}\n root=ET.Element('Branch_info')\n root.set(\"end_time\",str(stopInfo['end_time']))\n if \"end_ts\" in stopInfo.keys():\n root.set(\"end_ts\",str(stopInfo['end_ts']))\n triggerNode=ET.SubElement(root,\"Distribution_trigger\")\n triggerNode.set(\"name\",trigger)\n for varInfo in listDict:\n var=ET.SubElement(triggerNode,'Variable')\n var.text=varInfo['name']\n var.set('type',varInfo['type'])\n var.set('old_value',str(varInfo['old_value']))\n var.set('actual_value',str(varInfo['new_value']))\n if 'associated_pb' in varInfo.keys():\n var.set('probability',str(varInfo['associated_pb']))\n with open(filename,'w') as fileObject:\n fileObject.write(minidom.parseString(ET.tostring(root, 'utf-8')).toprettyxml(indent=\"\\t\"))","def __write_mapping(encrypted_metadata: dict) -> None:\n with open(\".mapping\", \"w\") as file:\n json.dump(encrypted_metadata, file, indent=2)","def write_map_to_file(dir, version, role, map_id, d):\n if not os.path.exists(dir):\n os.makedirs(dir)\n path = build_output_file_path(dir, version, role, map_id)\n with open(path, \"w\") as f:\n json.dump(d, f, sort_keys=True, indent=4)\n f.close()","def dump_dict(dumpfilename, file_names, test_files, offsets_dict):\n if _dump_dict_on:\n file(_dumpify(umpfilename + '.txt'), 'wt').write(_od_offsets_string(file_names, offsets_dict))\n file(o_dumpify('.strings.txt'), 'wt').write(_od_substrings_string(offsets_dict))\n matrix = _od_offsets_matrix(file_names, offsets_dict, test_files)\n file(_dumpify(dumpfilename + '.csv'), 'wt').write(matrix)","def extend_attribute_dictionary(attributedict, ns, name, value):\n\n key = ns, name\n if value is None:\n if key in attributedict:\n del attributedict[key]\n else:\n if ns == 'html' and key in attributedict:\n if name == 'class':\n # CSS classes are appended by space-separated list\n value = attributedict[key] + ' ' + value\n elif name == 'style':\n # CSS styles are appended by semicolon-separated rules list\n value = attributedict[key] + '; ' + value\n elif name in _html_attribute_boolflags:\n # All attributes must have a value. According to XHTML those\n # traditionally used as flags should have their value set to\n # the same as the attribute name.\n value = name\n attributedict[key] = value","def writexml(self, writer, indent=\"\", add_indent=\"\", new_line=\"\"):\n writer.write(indent + \"<\" + self.tagName)\n attrs = self._get_attributes()\n\n for a_name in attrs.keys():\n writer.write(\" %s=\\\"\" % a_name)\n self.write_data(writer, attrs[a_name].value)\n writer.write(\"\\\"\")\n if self.childNodes:\n writer.write(\">\")\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\n self.childNodes[0].writexml(writer, '', '', '')\n else:\n writer.write(new_line)\n for node in self.childNodes:\n node.writexml(writer, indent + add_indent, add_indent, new_line)\n writer.write(indent)\n writer.write(\"</%s>%s\" % (self.tagName, new_line))\n else:\n writer.write(\"/>%s\" % new_line)"],"string":"[\n \"def writeDictToXMLFile(outfile, target, dict):\\n targetStr = \\\"\\\\t\\\\t<Target>%s</Target>\\\\n\\\" % (escape(target),)\\n for key in dict.keys():\\n outfile.write('\\\\t<AVU>\\\\n')\\n outfile.write(targetStr)\\n outfile.write(\\\"\\\\t\\\\t<Attribute>%s</Attribute>\\\\n\\\" % (escape(key),) )\\n outfile.write(\\\"\\\\t\\\\t<Value>%s</Value>\\\\n\\\" % (escape(dict[key]),) )\\n outfile.write('\\\\t\\\\t<Unit />\\\\n')\\n outfile.write('\\\\t</AVU>\\\\n')\",\n \"def write_to_xml(dictData, metadata, xmlfile):\\n\\tfout = codecs.open(xmlfile, 'w', 'utf-8')\\n\\tfout.write('<?xml version = \\\"1.0\\\" encoding = \\\"UTF-8\\\" standalone = \\\"no\\\" ?>\\\\n')\\n\\tfout.write('<?xml-stylesheet type=\\\"text/xsl\\\" href=\\\"maketable.xsl\\\"?>\\\\n')\\n\\tfout.write('<root>\\\\n')\\n\\tfout.write('<meta>\\\\n')\\n\\tfor key, value in metadata.items():\\n\\t\\tfout.write('<' + key + '>' + value + '</' + key + '>\\\\n')\\n\\tfout.write('</meta>\\\\n')\\n\\tfout.write('<content>\\\\n')\\n\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\\n\\t\\txmlline = ''\\n\\t\\txmlline += '<word><headword>' + hw + '</headword><meanings>'\\n\\t\\tfor meaning in meanings:\\n\\t\\t\\txmlline += '<m>' + meaning + '</m>'\\n\\t\\txmlline += '</meanings>'\\n\\t\\txmlline += '<verse>'\\n\\t\\tlines = verse.split('<BR>')\\n\\t\\tfor line in lines:\\n\\t\\t\\txmlline += '<line>' + line + '</line>'\\n\\t\\txmlline += '</verse>'\\n\\t\\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\\n\\t\\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\\n\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\tfout.write(xmlline + '\\\\n')\\n\\t\\txmlline = ''\\n\\tfout.write('</content>\\\\n</root>')\\n\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('XML file generated. Success!')\\n\\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))\",\n \"def write_dictionary(args, dictio):\\n if not args.dictfile.endswith(\\\".file\\\"):\\n args.dictfile += \\\".file\\\"\\n with open(args.dictfile, \\\"wb\\\") as f:\\n dump(dictio, f, protocol=HIGHEST_PROTOCOL)\",\n \"def write(self, file_or_filename):\\n etMap = revert(self)\\n xmlTree = ET.ElementTree(etMap)\\n xmlTree.write(file_or_filename)\",\n \"def file_write(filename, dic):\\n d = dic \\n f = open(filename, 'w') \\n f.write(str(d))\\n f.close()\",\n \"def write_dict(outputfilename, dictionary):\\r\\n # May want to modify this code to pickle the key and value and alter the read dictionary to do the same.\\r\\n try:\\r\\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\\r\\n for key, value in dictionary.items():\\r\\n outfile.write('%s,%s\\\\n' % (key, value))\\r\\n except:\\r\\n input(\\\"File still open! Please close and press enter to continue\\\")\\r\\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\\r\\n for key, value in dictionary.items():\\r\\n outfile.write('%s,%s\\\\n' % (key, value))\",\n \"def write_dictionary(dictionary, path):\\n with open(path, 'w+') as file:\\n file.truncate() # Erase contents of config file\\n for key in dictionary:\\n file.write(f'{key}={dictionary[key]}\\\\n')\",\n \"def SaveDictFile(file,dict_):\\n with open(file,'w') as f:\\n for id in dict_:\\n f.write(dict_[id] + \\\"=\\\" + str(id)+\\\"\\\\n\\\")\",\n \"def _write_dict_to_mdin(self, f, dictionary):\\n\\n for key, val in dictionary.items():\\n if val is not None:\\n f.write(\\\" {:15s} {:s},\\\\n\\\".format(key+\\\" =\\\", str(val)))\\n f.write(\\\" /\\\\n\\\")\",\n \"def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\\r\\n string = getattr(self, attribute_name)\\r\\n if string or write_empty:\\r\\n xmlwriter.WriteElementString(attribute_name, string)\",\n \"def GenerateXML(dictionary, fileName=\\\"labelling.xml\\\") : \\n root = gfg.Element(\\\"annotation\\\") \\n #the big section is called Annotation\\n for key in dictionary:\\n #for every polygon list in inside object witho subelement name and attributes and the type \\\"polygon\\\"\\n objectElement = gfg.Element(\\\"object\\\") \\n root.append(objectElement) \\n subElement1 = gfg.SubElement(objectElement, \\\"name:\\\".strip(\\\":\\\"))\\n subElement1.text = str(dictionary[key][\\\"name\\\"])\\n subElement2 = gfg.SubElement(objectElement, \\\"attributes\\\".strip(\\\":\\\"))\\n subElement2.text = str(dictionary[key][\\\"attributes\\\"])\\n subElement3 = gfg.SubElement(objectElement, \\\"polygon\\\")\\n \\n for i in range(0, len(dictionary[key])-2):\\n #for every vertex of the polygon list it's rounded x, y on xml\\n SubInsidePolygon = gfg.SubElement(subElement3, \\\"pt\\\")\\n sub_x = gfg.SubElement(SubInsidePolygon, \\\"x\\\")\\n sub_y = gfg.SubElement(SubInsidePolygon, \\\"y\\\")\\n sub_x.text = str(int(round(dictionary[key][\\\"x_y_\\\" + str(i)][0])))\\n sub_y.text = str(int(round(dictionary[key][\\\"x_y_\\\" + str(i)][1])))\\n tree = gfg.ElementTree(root) \\n #create the xml tree\\n with open (fileName, \\\"wb\\\") as files : \\n tree.write(files) \\n #if xml does not exist create one otherwise rewrite to it\",\n \"def _write_event_xml(infodict, event_dir):\\n event_info = infodict['input']['event_information']\\n\\n otime = datetime.strptime(event_info['origin_time'], '%Y-%m-%dT%H:%M:%SZ')\\n eqdict = {'id': event_info['event_id'],\\n 'lat': '%.4f' % event_info['latitude'],\\n 'lon': '%.4f' % event_info['longitude'],\\n 'depth': '%.1f' % event_info['depth'],\\n 'mag': '%.1f' % event_info['magnitude'],\\n 'year': '%i' % otime.year,\\n 'month': '%i' % otime.month,\\n 'day': '%i' % otime.day,\\n 'hour': '%i' % otime.hour,\\n 'minute': '%i' % otime.minute,\\n 'second': '%i' % otime.second,\\n 'timezone': 'GMT',\\n 'locstring': event_info['event_description']}\\n eq = Element('earthquake', eqdict)\\n xmlfile = os.path.join(event_dir, 'event.xml')\\n tree = ElementTree(eq)\\n tree.write(xmlfile)\",\n \"def meta2xml(meta, filename):\\n\\n # this is stupid, just use dict2xml\\n xml = dict2xml(meta)\\n with open(filename, 'w+') as output:\\n output.write(xml)\",\n \"def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\\r\\n attribute_list = getattr(self, attribute_name)\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n for item in attribute_list:\\r\\n if item or write_empty:\\r\\n xmlwriter.WriteElementString(\\\"Item\\\", item)\\r\\n xmlwriter.WriteEndElement()\",\n \"def write_config_file(input_dict, file_name, entry_char='>',\\n attribution_char='=', usekeys=None):\\n fp = open(file_name, \\\"w\\\")\\n fp.write(write_config_string(input_dict, entry_char, attribution_char,\\n usekeys))\\n fp.close()\",\n \"def xml_obj(dict):\\n string = \\\"\\\"\\n for key in dict.keys():\\n string += ' <{}>{}</{}>\\\\n'.format(key, dict[key], key)\\n return string\",\n \"def write_completed_dictionary_to_file(the_dict):\\n\\ttry:\\n\\t\\toutputLocation = open('usable_dictionary.json','w')\\n\\t\\toutputString = str(the_dict)\\n\\t\\toutputLocation.write(outputString)\\n\\t\\toutputLocation.close()\\n\\texcept IOError:\\n\\t\\tprint (\\\"could not open file\\\")\",\n \"def ConvertToXML (given_dict) :\\r\\n stream_thing = cStringIO.StringIO()\\r\\n WriteToXMLStream(given_dict, stream_thing, 'top')\\r\\n return stream_thing.getvalue()\",\n \"def _output_dict(self):\\n lang = self.ddnGuiLanguage.get()\\n\\n fileout = os.path.normpath('{}/{}-{}.xml'.\\\\\\n format(self.MapCreator, self.Source, self.ddnCurProject.get()))\\n linesout = ['<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>', \\\\\\n '<DictionarySet xmlns:mc=\\\"urn:fmosoft-map-creator\\\" xmlns=\\\"urn:fmosoft-map-creator\\\" Version=\\\"1\\\">', \\\\\\n ' <Dictionary SourceLanguage=\\\"{}\\\" SourceLanguageIsPredefined=\\\"true\\\" TargetLanguage=\\\"{}\\\" TargetLanguageIsPredefined=\\\"false\\\">'.\\\\\\n format(self.Source, self.ddnCurProject.get()), \\\\\\n ]\\n for child in self.tree.get_children('approved'):\\n vv = self.tree.item(child)['values']\\n linesout.append(' <Translation Source=\\\"{}\\\" Target=\\\"{}\\\"/>'.format(vv[0], vv[1]))\\n linesout.append(' </Dictionary>')\\n linesout.append('</DictionarySet>')\\n linesout.append('')\\n\\n if os.path.exists(fileout):\\n os.remove(fileout)\\n\\n if fileout:\\n output = codecs.open(fileout, mode='w', encoding='utf-8')\\n output.write('\\\\n'.join(linesout))\\n output.close()\\n pass\",\n \"def write_bool_to_xml(self, attribute_name, xmlwriter):\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n xmlwriter.WriteValue(getattr(self, attribute_name))\\r\\n xmlwriter.WriteEndElement()\",\n \"def write_dictionary():\\n for dictionary in dictionaries:\\n for values in dictionary.values():\\n with open(sys.argv[1] + \\\"-1\\\", \\\"ab\\\") as dest_file:\\n dest_file.write(values)\",\n \"def save_to_file():\\n dict_from_file.update(temp_dict)\\n plik=open('data.txt', 'w')\\n for key in dict_from_file.keys():\\n plik.write(key)\\n plik.write(\\\" \\\")\\n plik.write(str(dict_from_file[key][0]))\\n plik.write(' ')\\n plik.write(dict_from_file[key][1].replace(' ','_'))\\n plik.write(' ')\\n plik.write(str(dict_from_file[key][2]))\\n plik.write('\\\\n')\",\n \"def print_xml_config(config_dictionary,**kwargs):\\n \\n #Check if we have passed a filename\\n #If not, pass a default filename\\n if 'config_file' in kwargs:\\n config_file = kwargs['config_file']\\n else:\\n config_file = 'ebtel_config.xml'\\n \\n #Open the file\\n f = open(config_file,'w')\\n \\n #Print necessary header info\\n f.write('<?xml version=\\\"1.0\\\" ?>\\\\n')\\n f.write('<input>\\\\n')\\n\\n #Loop through dictionary and print to xml file\\n for key in config_dictionary:\\n #Print tab delimiter, brackets and keyword\\n f.write('\\\\t<')\\n f.write(key)\\n f.write('>')\\n #Check if entry is a list\\n #If so print it as a list\\n if isinstance(config_dictionary[key],list) or type(config_dictionary[key]).__name__ == 'ndarray':\\n #Make temporary list\\n temp = config_dictionary[key]\\n #Skip to new line\\n f.write('\\\\n')\\n #Begin loop over list\\n for i in range(len(config_dictionary[key])):\\n f.write('\\\\t\\\\t<')\\n f.write(key+str(i))\\n f.write('>')\\n f.write(str(temp[i]))\\n f.write('</')\\n f.write(key+str(i))\\n f.write('>\\\\n')\\n #Print additional tab to preserve alignment\\n f.write('\\\\t')\\n else:\\n #Print value\\n f.write(str(config_dictionary[key]))\\n #Close the brackets and print newline\\n f.write('</')\\n f.write(key)\\n f.write('>\\\\n')\\n \\n #Close the main node of the file\\n f.write('</input>')\\n \\n #Close the file\\n f.close()\",\n \"def xmlwrite(self, doc, filename):\\n pathname = os.path.join(self.session.session_dir, filename)\\n f = open(pathname, \\\"w\\\")\\n doc.writexml(writer=f, indent=\\\"\\\", addindent=\\\" \\\", newl=\\\"\\\\n\\\", encoding=\\\"UTF-8\\\")\\n f.close()\",\n \"def save_to_xml(self, xwriter):\\r\\n\\r\\n xwriter.WriteStartElement(\\\"Profile\\\")\\r\\n xwriter.WriteAttributeString(\\\"Name\\\", self.Name)\\r\\n xwriter.WriteStartAttribute(\\\"Version\\\")\\r\\n xwriter.WriteValue(self.Version)\\r\\n xwriter.WriteEndAttribute()\\r\\n\\r\\n for var_name in self.__dict__:\\r\\n var_type = type(getattr(self, var_name))\\r\\n\\r\\n if var_type is str and var_name != \\\"Name\\\":\\r\\n self.write_string_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is bool:\\r\\n self.write_bool_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is dict:\\r\\n self.write_dict_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is list and var_name != \\\"ExcludeRules\\\":\\r\\n self.write_list_to_xml(var_name, xwriter)\\r\\n\\r\\n xwriter.WriteStartElement(\\\"ExcludeRules\\\")\\r\\n xwriter.WriteAttributeString(\\\"Operator\\\", self.ExcludeOperator)\\r\\n xwriter.WriteAttributeString(\\\"ExcludeMode\\\", self.ExcludeMode)\\r\\n for rule in self.ExcludeRules:\\r\\n if rule:\\r\\n rule.save_xml(xwriter)\\r\\n xwriter.WriteEndElement()\\r\\n \\r\\n xwriter.WriteEndElement()\",\n \"def WriteFile(self, filename) :\\n\\n # open file for writing:\\n f = open(filename, 'w')\\n\\n ## loop over key/value pairs:\\n #for k,v in self.iteritems():\\n # # add line; at least the specified number of characters \\n # # is used for the key:\\n # f.write( '%-20s:%s\\\\n' % (k,v) )\\n ##endfor\\n\\n # write processed input:\\n f.writelines(self.outfile)\\n \\n # close file:\\n f.close()\",\n \"def GdictWrite(filename=\\\"turtle_docstringdict\\\"):\\n docsdict = {}\\n\\n for methodname in gScreenFunc:\\n key = \\\"_Screen.\\\"+methodname\\n docsdict[key] = eval(key).__doc__\\n for methodname in gMoveFunc:\\n key = \\\"Myturtle.\\\"+methodname\\n docsdict[key] = eval(key).__doc__\\n\\n with open(\\\"%s.py\\\" % filename,\\\"w\\\") as f:\\n keys = sorted(x for x in docsdict\\n if x.split('.')[1] not in _alias_list)\\n f.write('docsdict = {\\\\n\\\\n')\\n for key in keys[:-1]:\\n f.write('%s :\\\\n' % repr(key))\\n f.write(' \\\"\\\"\\\"%s\\\\n\\\"\\\"\\\",\\\\n\\\\n' % docsdict[key])\\n key = keys[-1]\\n f.write('%s :\\\\n' % repr(key))\\n f.write(' \\\"\\\"\\\"%s\\\\n\\\"\\\"\\\"\\\\n\\\\n' % docsdict[key])\\n f.write(\\\"}\\\\n\\\")\\n f.close()\",\n \"def write(self, fp, **kwds):\\n json.dump(self._dict, fp)\",\n \"def xmlWrite(self, xmlWriter, font, value, name, attrs):\\n raise NotImplementedError(self)\",\n \"def save(self):\\n if not os.path.exists(self.dictionary_save_path) and self.dictionary_save_path != \\\"\\\":\\n os.makedirs(self.dictionary_save_path)\\n with open(self.dictionary_save_path + \\\"dictionary\\\", 'w') as f:\\n f.write(\\\"{}\\\\n\\\".format(len(self.dictionary)))\\n\\n for word in self.dictionary:\\n f.write(\\\"{}\\\\t{}\\\\n\\\".format(self.dictionary[word]['id'], word))\",\n \"def save_dictionary(myDict, filepath):\\r\\n # context manager write binary\\r\\n with open(filepath, 'wb') as file:\\r\\n # pickle save\\r\\n pickle.dump(myDict, file)\",\n \"def write_output(word_dict):\\n # create an empty output.txt file\\n output = open('output.txt', 'w')\\n\\n for i in words_dict: \\n output.write(i + \\\" : \\\" + str(words_dict[i]) + \\\"\\\\n\\\")\",\n \"def save_dict_to_file(dictionary: dict, dst_path: str) -> None:\\n with io.open(file=dst_path, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as dst:\\n for k, v in dictionary.items():\\n dst.write(f\\\"{k} {v}\\\\n\\\")\\n dst.close()\",\n \"def dump_json_to_file(dictionary, file_name):\\n if file_name is not None:\\n IOUtils.mkdirf(file_name)\\n with open(file_name, 'w') as file_obj:\\n json.dump(dictionary, file_obj, indent=4)\",\n \"def _write_packet_dict(ctx, package_dict):\\n p4gf_util.write_dict_to_file(package_dict, _packet_filename(ctx.config.repo_name))\",\n \"def _save_dict(self, dict_name=None):\\n if dict_name is None:\\n if not os.path.exists(self.config.data.dict_dir):\\n os.makedirs(self.config.data.dict_dir)\\n for name in self.dict_names:\\n self._save_dict(name)\\n else:\\n dict_idx = self.dict_names.index(dict_name)\\n dict_file = open(self.dict_files[dict_idx], \\\"w\\\")\\n id_to_vocab_dict_map = self.id_to_vocab_dict_list[dict_idx]\\n index = 0\\n for vocab, count in self.count_list[dict_idx]:\\n id_to_vocab_dict_map[index] = vocab\\n index += 1\\n dict_file.write(\\\"%s\\\\t%d\\\\n\\\" % (vocab, count))\\n dict_file.close()\",\n \"def _write_dict(pdf, input_dict, indent=0, border=BORDER):\\n for key, value in input_dict.items():\\n pdf.set_font(\\\"arial\\\", \\\"B\\\", 14)\\n pdf.cell(75, 5, key, border, 1, \\\"L\\\")\\n pdf.cell(indent, 0)\\n pdf.set_font(\\\"arial\\\", \\\"\\\", 12)\\n pdf.multi_cell(150, 5, value, border, \\\"L\\\")\\n pdf.ln(h=5)\",\n \"def XMLWriter(\\n fd,\\n encoding=\\\"utf-8\\\",\\n pretty=True,\\n compactempty=True,\\n indentation=_DEFAULT_INDENTATION\\n):\\n return _document(fd, encoding, pretty, compactempty, indentation)\",\n \"def serialize_dictionary(self, dic_dir: str ,terms_id: str, id_terms: str, xml_file_name = 'test'):\\n if not os.path.exists(dic_dir):\\n os.mkdir(dic_dir)\\n with open(os.path.join(dic_dir,xml_file_name+'_'+terms_id), 'wb') as f:\\n pickle.dump(self.term2id_dict, f)\\n with open(os.path.join(dic_dir,xml_file_name+'_'+id_terms), 'wb') as f:\\n pickle.dump(self.id2term_dict, f)\",\n \"def write_to_xml(filename, xmlelement):\\n xmlpretty = prettify(xmlelement)\\n with open(filename, 'w') as f:\\n f.write(xmlpretty)\",\n \"def dict_print(self, output_file = \\\"dict.csv\\\"):\\t\\n\\n\\t\\twith codecs.open(output_file,\\\"w\\\",encoding='utf-8') as f:\\n\\t\\t\\tfor (v,k) in self.token_key.items(): f.write(\\\"%s,%d\\\\n\\\" % (v,k))\",\n \"def save_plots(plot_dict, filename) :\\n outfile = ROOT.TFile(filename, \\\"RECREATE\\\")\\n\\n for key in sorted(plot_dict) :\\n if type( plot_dict[key] ) is types.DictType :\\n directory = outfile.mkdir( key )\\n directory.cd()\\n save_plot( plot_dict[key], directory )\\n outfile.cd()\\n elif plot_dict[key] is not None :\\n plot_dict[key].Write()\\n \\n outfile.Close()\",\n \"def write(self, filename, mode=\\\"w\\\"):\\n d = self._result_dict\\n val = yaml.safe_dump(d, default_flow_style=False)\\n\\n with open(str(filename), mode) as outfile:\\n outfile.write(val)\",\n \"def write_dict_txtfile(input_file_name, data_dict):\\n \\n output_file = open(input_file_name, 'w')\\n output_file.write('Human Metabolome database')\\n output_file.write('\\\\n\\\\n')\\n\\n for keys, values in data_dict.items():\\n output_file.write(str(keys)+', '+str(values)+'\\\\n')\",\n \"def save(self, filename=None):\\n f = filename if filename else self.path\\n etree.register_namespace('', TEI)\\n etree.register_namespace('mith', MITH)\\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')\",\n \"def json_write(path, dictionary):\\n json_output = json.dumps(dictionary, indent=4)\\n with open(path, \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\n f.write(json_output)\",\n \"def write(filename, rcdict) :\\n\\n # open file for writing:\\n f = open(filename, 'w')\\n\\n # loop over key/value pairs:\\n for k, v in rcdict.items():\\n # add line; at least the specified number of characters \\n # is used for the key:\\n f.write('%-20s:%s\\\\n' % (k, v))\\n #endfor\\n\\n # close file:\\n f.close()\",\n \"def save(self, filename=None):\\n\\n if filename is None:\\n filename = self.__filename\\n\\n file = open(filename, 'w')\\n for key in self:\\n if key[:2] == '__':\\n continue\\n file.write(key)\\n file.write(' = ')\\n if key in self.__orig:\\n value = self.__orig[key]\\n else:\\n value = self[key]\\n file.write(str(value))\\n\\n file.close()\",\n \"def write(self, filename: str):\\n obj = self.to_dict(self)\\n config.write(obj, filename)\",\n \"def write_pecfile_dict(output_file, record_dict):\\n for key in sorted(record_dict.keys()):\\n output_file.write('%s\\\\n' % str(record_dict[key]))\\n return output_file\",\n \"def save_plot(plot_dict, outfile) :\\n for key in sorted(plot_dict) :\\n if type( plot_dict[key] ) is types.DictType :\\n directory = outfile.mkdir( key )\\n directory.cd()\\n save_plot( plot_dict[key], directory )\\n outfile.cd()\\n else :\\n plot_dict[key].Write()\",\n \"def dump(mapping, fp):\\n fp.write(dumps(mapping))\",\n \"def write_taxonomic_key(outfile: TextIO, do_print: bool, taxkey: TMB_TaxKeyGen.KeyText,\\n location: Optional[TMB_Classes.LocationClass]) -> None:\\n if do_print:\\n start_page_division(outfile, \\\"base_page\\\")\\n else:\\n if location is None:\\n common_header_part1(outfile, \\\"Fiddler Crab Taxonomic Key\\\", indexpath=\\\"../../\\\")\\n else:\\n common_header_part1(outfile, location.trimmed_name + \\\": Taxonomic Key\\\", indexpath=\\\"../../\\\")\\n outfile.writelines(taxkey.header)\\n common_header_part2(outfile, indexpath=\\\"../../\\\")\\n\\n if location is None:\\n outfile.write(\\\" <header id=\\\\\\\"all_taxkey.html\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <h1 class=\\\\\\\"nobookmark\\\\\\\">Taxonomic Key to All Fiddler Crabs</h1>\\\\n\\\")\\n else:\\n outfile.write(\\\" <header id=\\\\\\\"\\\" + place_to_filename(location.name) + \\\"_taxkey.html\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <h1 class=\\\\\\\"nobookmark\\\\\\\">\\\" + location.trimmed_name + \\\": Taxonomic Key</h1>\\\\n\\\")\\n if not do_print:\\n outfile.write(\\\" <nav>\\\\n\\\")\\n outfile.write(\\\" <ul>\\\\n\\\")\\n if location is not None:\\n outfile.write(\\\" <li>\\\" + create_location_link(location, location.trimmed_name, do_print, path=\\\"../\\\",\\n inc_icon=True) + \\\"</li>\\\\n\\\")\\n outfile.write(\\\" <li><a href=\\\\\\\"../index.html\\\\\\\">\\\" + fetch_fa_glyph(\\\"index\\\") +\\n \\\"Location Index</a></li>\\\\n\\\")\\n outfile.write(\\\" <li><a href=\\\\\\\"index.html\\\\\\\">\\\" + fetch_fa_glyph(\\\"tax key\\\") +\\n \\\"Taxonomic Key Guide</a></li>\\\\n\\\")\\n outfile.write(\\\" </ul>\\\\n\\\")\\n outfile.write(\\\" </nav>\\\\n\\\")\\n outfile.write(\\\" </header>\\\\n\\\")\\n outfile.write(\\\" <p> </p>\\\\n\\\")\\n\\n for line in taxkey.body:\\n outfile.write(replace_species_in_string(line, True, do_print, \\\"../../\\\"))\\n\\n if do_print:\\n end_page_division(outfile)\\n else:\\n common_html_footer(outfile)\",\n \"def dict2file(dict, filename, foldername):\\n if foldername:\\n if not os.path.exists(\\\"../Created_QD/\\\" + foldername):\\n os.makedirs(\\\"../Created_QD/\\\" + foldername)\\n file = open(\\\"../Created_QD/\\\" + foldername + \\\"/\\\" + filename + \\\".xyz\\\", \\\"w\\\")\\n else:\\n file = open(\\\"../Created_QD/\\\" + filename + \\\".xyz\\\", \\\"w\\\")\\n file.write(\\\" \\\\n\\\\n\\\")\\n for atom, values in dict.items():\\n file.write(values['element'] + \\\"\\\\t\\\" + str(values['coor'][0]) + \\\"\\\\t\\\\t\\\" +\\n str(values['coor'][1]) + \\\"\\\\t\\\\t\\\" + str(values['coor'][2]) + \\\"\\\\n\\\")\\n file.seek(0)\\n file.write(str(len(dict)))\\n file.close()\\n print(\\\"\\\\nQuantum Dot created :)\\\")\",\n \"def write(self):\\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\\\"UTF-8\\\")\\n with open(self.xml_file, 'w') as f:\\n f.write(temp_string)\\n # f = open(self.xml_file, \\\"w\\\")\\n # f.write(temp_string)\\n # f.close()\",\n \"def write_json(dictionary, filename):\\r\\n with open(filename, 'w') as data_file:\\r\\n json.dump(dictionary, data_file, indent=4, sort_keys=True)\\r\\n print('--> Wrote ' + os.path.basename(filename))\",\n \"def serialize_mapping_to_file(\\n file_path, alignment, onto_one=None, onto_two=None, extension=None\\n):\\n with open(file_path, \\\"w\\\", encoding=\\\"utf-8\\\") as out_file:\\n out_file.write(__get_xml_intro(onto_one, onto_two, extension))\\n for correspondence in alignment:\\n out_file.write(__get_mapping_string(*correspondence))\\n out_file.write(__get_xml_outro())\",\n \"def convert_dict_to_xml(data_dict):\\n from dicttoxml import dicttoxml\\n\\n return dicttoxml(data_dict, attr_type=False)\",\n \"def dump_dict(dct, outpath='./dict.txt'):\\n with open( Path(outpath), 'w' ) as file:\\n for k in sorted(dct.keys()):\\n file.write('{}: {}\\\\n'.format(k, dct[k]))\",\n \"def store(my_dict, filename):\\n my_json = json.dumps(my_dict)\\n f = open(filename,\\\"w\\\")\\n f.write(my_json)\\n f.close()\",\n \"def testFlatDict(self):\\n ddict = {\\n \\\"group/group/dataset\\\": 10,\\n \\\"group/group/dataset@attr\\\": 11,\\n \\\"group/group@attr\\\": 12,\\n }\\n with h5py.File(self.h5_fname, \\\"w\\\") as h5file:\\n dictdump.dicttonx(ddict, h5file)\\n self.assertEqual(h5file[\\\"group/group/dataset\\\"][()], 10)\\n self.assertEqual(h5file[\\\"group/group/dataset\\\"].attrs['attr'], 11)\\n self.assertEqual(h5file[\\\"group/group\\\"].attrs['attr'], 12)\",\n \"def save_meta_file(gen_dict, f_name):\\r\\n logger = custom_logger.CustomLogger(run_id+':'+file_id)\\r\\n filename = run_id+'_'+ f_name +'.meta'\\r\\n f = open(os.path.join(unique_op_dir, filename),'a')\\r\\n print('Output stored in %s'%(str(os.path.join(unique_op_dir, filename))))\\r\\n logger.info('Output stored in %s'%(str(os.path.join(unique_op_dir, filename))))\\r\\n for key, val in gen_dict.items():\\r\\n line = str(key)+\\\" : \\\"+str(val)+\\\"\\\\n\\\"\\r\\n f.write(line)\",\n \"def mapSave(regions,filename):\\n root = ET.Element('map')\\n for name,table in regions.items():\\n node = ET.SubElement(root,'region')\\n node.set('name',name)\\n if table.has_key('value'): node.set('value',str(table['value']))\\n if table.has_key('occupants'): node.set('occupants',str(table['occupants']))\\n node.set('owner',str(table['owner']))\\n for neighbor in table['neighbors']:\\n subnode = ET.SubElement(node,'neighbor')\\n subnode.set('name',neighbor)\\n tree = ET.ElementTree(root)\\n tree.write(filename,pretty_print=True)\\n return tree\",\n \"def dict_to_dom(root_node, xml_dict):\\n\\n if '_content' in list(xml_dict.keys()):\\n\\n root_node.appendChild(\\n root_node.ownerDocument.createTextNode(\\n convert_to_str(xml_dict['_content'])\\n )\\n )\\n\\n for key, value in xml_dict.items():\\n\\n if key == '_content':\\n continue\\n\\n if type(value) == dict:\\n\\n # Root node\\n\\n tmp_node = root_node.ownerDocument.createElement(key)\\n\\n dict_to_dom(tmp_node, value)\\n\\n root_node.appendChild(tmp_node)\\n\\n elif type(value) == list:\\n\\n for multinode in value:\\n\\n tmp_node = root_node.ownerDocument.createElement(key)\\n\\n dict_to_dom(tmp_node, multinode)\\n\\n root_node.appendChild(tmp_node)\\n\\n else:\\n\\n # Attributes\\n\\n root_node.setAttribute(\\n key,\\n convert_to_str(value)\\n )\",\n \"def write_dict_as_wiki(self, aDict, name, directory):\\n with codecs.open(os.path.join(directory, name), 'w', 'utf-8') as wikipage:\\n wikipage.write(\\\"{|\\\\nItem | Count | Tag | Categories\\\")\\n items = aDict.items()\\n items.sort(key=lambda x: x[1], reverse=True)\\n for item in items:\\n values = (self.alignment_config_items['alignment_template'],\\n {'item': item[0], 'count': item[1],\\n 'value': \\\"\\\", 'categories': \\\"\\\"})\\n table_line = '\\\\n' + textlib.glue_template_and_params(values)\\n wikipage.write(unicode(table_line))\\n wikipage.write(\\\"\\\\n|}\\\")\",\n \"def write(name, keyword, domain, citation, author, description, species, version, contact, license, values,\\n functions, output, value_prefix):\\n write_namespace(\\n name, keyword, domain, author, citation, values,\\n namespace_description=description,\\n namespace_species=species,\\n namespace_version=version,\\n author_contact=contact,\\n author_copyright=license,\\n functions=functions,\\n file=output,\\n value_prefix=value_prefix\\n )\",\n \"def saveSessionToXML(self, filename):\\r\\n xmlStr = self.createXMLStr()\\r\\n \\r\\n #Write to the file\\r\\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\\r\\n xmlFile = open(filename, 'w')\\r\\n xmlFile.write(xmlStr)\\r\\n xmlFile.close()\",\n \"def save(self) -> None:\\n with open(dict_path, 'w', encoding='utf-8') as dictionary_file:\\n json.dump(self.data, dictionary_file, indent=2, separators=(',', ':'), ensure_ascii=False)\",\n \"def test_dict_to_xml(self):\\n xmlns = {\\n '_': utils.NETCONF_NAMESPACE,\\n 'nm': 's'\\n }\\n\\n xml_node = utils.generate_xml_node(\\n self.SIMPLE_DICT,\\n xmlns,\\n 'rpc'\\n )\\n\\n xml_node_string = etree.tostring(\\n xml_node, pretty_print=False\\n )\\n\\n self.assertEqual(\\n json.dumps(xmltodict.parse(xml_node_string.decode('utf-8')),\\n indent=4, sort_keys=True),\\n json.dumps(xmltodict.parse(\\n \\\"\\\"\\\"<rpc xmlns:nm=\\\"s\\\" xmlns=\\\"urn:ietf:params:xml:ns:netc\\\"\\\"\\\" +\\n \\\"\\\"\\\"onf:base:1.0\\\"><a xmlns:ns0=\\\"urn:ietf:params:xml:ns:n\\\"\\\"\\\" +\\n \\\"\\\"\\\"etconf:base:1.0\\\"><b ns0:m=\\\"g\\\">b</b><c nm:nm=\\\"update\\\">\\\"\\\"\\\" +\\n \\\"\\\"\\\"<f>1</f><e>str</e><d>1</d><d>2</d><d>3</d></c></a>\\\"\\\"\\\" +\\n \\\"\\\"\\\"</rpc>\\\"\\\"\\\"), indent=4, sort_keys=True)\\n )\",\n \"def serialize_dictionaries(self,\\n output_file,\\n restrict_to = None):\\n io.write_symbol_table_dict(output_file, self._symtab, restrict_to)\",\n \"def write_csv(dictionary):\\n\\n with open(constant.METADATA_FILE, 'a', newline='') as file:\\n writer = csv.DictWriter(file, fieldnames=dictionary.keys())\\n try:\\n writer.writerow(dictionary)\\n except Exception:\\n writeable = {k: str(v).encode(\\\"utf-8\\\") for k, v in dictionary.items()}\\n writer.writerow(writeable)\",\n \"def write(self, file=sys.stdout):\\n d = self.to_dict()\\n if d:\\n yaml.dump([d], file, default_flow_style=False)\",\n \"def dump_dict(dct, outpath='./dict.txt'):\\n with open(Path(outpath), 'w') as file:\\n for k in sorted(dct.keys()):\\n file.write('{}: {}\\\\n'.format(k, dct[k]))\",\n \"def save_data(self, **kwargs):\\n\\n file = kwargs['file']\\n with open(file, 'w') as data_file:\\n for item in self._items.values():\\n data_string = '{'\\n item_dict = item.to_dict()\\n\\n for key, value in item_dict.items():\\n data_string += f'\\\"{key}\\\":\\\"{value}\\\",'\\n\\n data_string = data_string[:-1]\\n data_string += '}\\\\n'\\n data_file.write(data_string)\",\n \"def writeFile(self, filename):\\n s = ET.tostring(self._root)\\n\\n #Remove all formatting\\n s = s.replace('\\\\n','')\\n s = s.replace('\\\\t','')\\n s = s.replace('\\\\r','')\\n\\n f = open(filename, 'w')\\n f.write(minidom.parseString(s).toprettyxml())\\n f.close()\",\n \"def XMLWrite(one, two, three, four, five, six, seven, eight):\\n filePath = \\\"/mnt/RAM/kanban.xml\\\"\\n xmlFile = open(filePath, 'w')\\n\\n xmlFile.write('<kanbanShelf>\\\\n')\\n xmlFile.write(' <one>%s</one>\\\\n' % one)\\n xmlFile.write(' <two>%s</two>\\\\n' % two)\\n xmlFile.write(' <three>%s</three>\\\\n' % three)\\n xmlFile.write(' <four>%s</four>\\\\n' % four)\\n xmlFile.write(' <five>%s</five>\\\\n' % five)\\n xmlFile.write(' <six>%s</six>\\\\n' % six)\\n xmlFile.write(' <seven>%s</seven>\\\\n' % seven)\\n xmlFile.write(' <eight>%s</eight>\\\\n' % eight)\\n xmlFile.write('</kanbanShelf>')\",\n \"def write_kdv_dictionary(data, file_name, get_is_active=True, extension='.txt'):\\n\\n file_name += extension\\n\\n with open(file_name, 'w', encoding='utf-8') as my_file:\\n my_file.write('{0}\\\\n'.format( data[0].file_output(get_header=True)) )\\n\\n for key, value in data.items():\\n if get_is_active is True:\\n if value.IsActive is True:\\n my_file.write('{0}\\\\n'.format(value.file_output()))\\n else:\\n my_file.write('{0}\\\\n'.format(value.file_output()))\",\n \"def print_xml(tree, file):\\n tree.write(file, encoding=\\\"utf-8\\\", xml_declaration=True)\",\n \"def write(self):\\r\\n for prop in self.prpnames:\\r\\n elem = SubElement(self._root, prop)\\r\\n data = self.__getattribute__(prop)\\r\\n if self.prpnames[prop]['type'] == \\\"text\\\":\\r\\n elem.text = data\\r\\n elif self.prpnames[prop]['type'] == 'list':\\r\\n for x in data:\\r\\n SubElement(elem, 'regel').text = x\\r\\n elif self.prpnames[prop]['type'] == 'attr':\\r\\n elem.set(self.prpnames[prop]['naam'], data)\\r\\n tree = ElementTree(self._root)\\r\\n tree.write(self._fn)\\r\\n if not self.exists:\\r\\n self.exists = True\",\n \"def write(name, keyword, domain, citation, author, description, species, version, contact, license, values, output):\\n write_namespace(\\n name, keyword, domain, author, citation, values,\\n namespace_description=description,\\n namespace_species=species,\\n namespace_version=version,\\n author_contact=contact,\\n author_copyright=license,\\n file=output,\\n )\",\n \"def testAttributes(self):\\n ddict = {\\n \\\"group\\\": {\\\"datatset\\\": \\\"hmmm\\\", (\\\"\\\", \\\"group_attr\\\"): 10},\\n \\\"dataset\\\": \\\"aaaaaaaaaaaaaaa\\\",\\n (\\\"\\\", \\\"root_attr\\\"): 11,\\n (\\\"dataset\\\", \\\"dataset_attr\\\"): 12,\\n (\\\"group\\\", \\\"group_attr2\\\"): 13,\\n }\\n with h5py.File(self.h5_fname, \\\"w\\\") as h5file:\\n dictdump.dicttoh5(ddict, h5file)\\n self.assertEqual(h5file[\\\"group\\\"].attrs['group_attr'], 10)\\n self.assertEqual(h5file.attrs['root_attr'], 11)\\n self.assertEqual(h5file[\\\"dataset\\\"].attrs['dataset_attr'], 12)\\n self.assertEqual(h5file[\\\"group\\\"].attrs['group_attr2'], 13)\",\n \"def print_dict(init_dict, file_name=\\\"test\\\"):\\n ordered_dict = collections.OrderedDict()\\n order = [\\\"SIMULATION\\\", \\\"PARAMS\\\", \\\"DIST\\\"]\\n for key_ in order:\\n ordered_dict[key_] = init_dict[key_]\\n\\n with open(\\\"{}.boupy.yml\\\".format(file_name), \\\"w\\\") as outfile:\\n yaml.dump(ordered_dict, outfile, explicit_start=True, indent=4)\",\n \"def getXML(self):\\n\\n def _getElementForMappingEntry(entry, mappingStyle):\\n xmlDocTmp = Document()\\n element = xmlDocTmp.createElement(mappingStyle)\\n for k, v in viewitems(entry):\\n # ignore empty, None or compiled regexp items into output\\n if not v or (k == \\\"path-match-expr\\\"):\\n continue\\n element.setAttribute(k, str(v))\\n return element\\n\\n xmlDoc = Document()\\n root = xmlDoc.createElement(\\\"storage-mapping\\\") # root element name\\n for mappingStyle, mappings in viewitems(self):\\n for mapping in mappings:\\n mapElem = _getElementForMappingEntry(mapping, mappingStyle)\\n root.appendChild(mapElem)\\n return root.toprettyxml()\",\n \"def write_xml(self, xmlfile):\\n system.xml.write_file(xmlfile, self.status, 'status')\",\n \"def write_json_file(data, filename, dictionary=False):\\n try:\\n if dictionary:\\n with open(filename, \\\"wb\\\") as outfile:\\n json.dump(data, outfile)\\n else:\\n with open(filename, \\\"w\\\") as outfile:\\n json.dump(data, outfile, indent=2)\\n return True\\n except OSError:\\n return False\",\n \"def _encode_dictionary(data, name=\\\"Second\\\", sub=False):\\n\\n if sub:\\n root = ET.Element(\\\"Field\\\", {\\\"Name\\\": f'{name}', \\\"Type\\\": \\\"elsystem.collections.dictionary\\\"})\\n else: \\n root = ET.Element(\\\"elsystem.collections.dictionary\\\")\\n\\n items = ET.SubElement(root, 'Field', {'Name': 'Items', 'Type': 'elsystem.collections.vector'})\\n\\n index = 0\\n\\n for key, val in data.items():\\n\\n pair = ET.SubElement(items, 'Field', {'Name': f'E{index}', 'Type': 'elsystem.collections.pair'})\\n \\n if type(val) == dict:\\n ET.SubElement(pair, 'Field', {'Name': 'First', 'Value': _encode_value(key)}) \\n sub_dict = _encode_dictionary(data=val, name=\\\"Second\\\", sub=True)\\n pair.append(sub_dict)\\n elif type(val) == list:\\n ET.SubElement(pair, 'Field', {'Name': 'First', 'Value': _encode_value(key)}) \\n sub_vec = _encode_list(data=val, name=F'E{index}', sub=True)\\n pair.append(sub_vec)\\n else:\\n ET.SubElement(pair, 'Field', {'Name': 'First', 'Value': _encode_value(key)}) \\n ET.SubElement(pair, 'Field', {'Name': 'Second', 'Value': _encode_value(val)}) \\n\\n index += 1\\n\\n ET.SubElement(items, 'Field', {'Name': 'count', 'Value': _encode_value(index)})\\n\\n if sub:\\n return root \\n else:\\n return ET.tostring(root)\",\n \"def testAttributes(self):\\n ddict = {\\n \\\"group\\\": {\\\"dataset\\\": 100, \\\"@group_attr1\\\": 10},\\n \\\"dataset\\\": 200,\\n \\\"@root_attr\\\": 11,\\n \\\"dataset@dataset_attr\\\": \\\"12\\\",\\n \\\"group@group_attr2\\\": 13,\\n }\\n with h5py.File(self.h5_fname, \\\"w\\\") as h5file:\\n dictdump.dicttonx(ddict, h5file)\\n self.assertEqual(h5file[\\\"group\\\"].attrs['group_attr1'], 10)\\n self.assertEqual(h5file.attrs['root_attr'], 11)\\n self.assertEqual(h5file[\\\"dataset\\\"].attrs['dataset_attr'], \\\"12\\\")\\n self.assertEqual(h5file[\\\"group\\\"].attrs['group_attr2'], 13)\",\n \"def savemat(self, file_name, mdict=None, appendmat=True, **kwargs):\\n # Set mdict default value to empty dictionary\\n if mdict is None:\\n mdict = {}\\n\\n # Merge mdict with attributes dictionary, giving mdict the upper-hand\\n # in case of inconsistency\\n dsavemat = {**vars(self), **mdict}\\n\\n # Save the merged dictionary to a .mat file\\n scipy.io.savemat(file_name, dsavemat, appendmat, **kwargs)\",\n \"def _write_json(fname, dictionary, overwrite=False, verbose=False):\\n if op.exists(fname) and not overwrite:\\n raise FileExistsError(f'\\\"{fname}\\\" already exists. '\\n 'Please set overwrite to True.')\\n\\n json_output = json.dumps(dictionary, indent=4)\\n with open(fname, 'w') as fid:\\n fid.write(json_output)\\n fid.write('\\\\n')\\n\\n if verbose is True:\\n print(os.linesep + f\\\"Writing '{fname}'...\\\" + os.linesep)\\n print(json_output)\",\n \"def writexml(file):\\n OUTFILE=open(file,\\\"w\\\")\\n doc = xml.dom.minidom.Document()\\n\\n # Create the <dec_reg_list> base element\\n decl_reg_list = doc.createElement(\\\"decl_reg_list\\\")\\n doc.appendChild(decl_reg_list)\\n\\n regname_old=\\\"\\\"\\n rows.pop(0)\\n for row in rows:\\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\\n if regname != regname_old:\\n # Create the register element\\n register = doc.createElement(\\\"register\\\")\\n register.setAttribute(\\\"name\\\", regname)\\n register.setAttribute(\\\"offset\\\", offset)\\n if default != \\\"\\\" : register.setAttribute(\\\"default\\\", default)\\n register.setAttribute(\\\"type\\\", regtype)\\n if expose_reg == \\\"1\\\": register.setAttribute(\\\"usr\\\", expose_reg)\\n if depth != \\\"\\\": register.setAttribute(\\\"size\\\", depth)\\n if incsz != \\\"\\\": register.setAttribute(\\\"incsz\\\", incsz)\\n text = doc.createTextNode(regdesc)\\n register.appendChild(text)\\n decl_reg_list.appendChild(register)\\n \\n # Create the field element\\n if bitname != \\\"\\\":\\n field = doc.createElement(\\\"field\\\")\\n field.setAttribute(\\\"name\\\", bitname)\\n if loc !=\\\"\\\": field.setAttribute(\\\"loc\\\", addcolon(loc))\\n if bittype != \\\"\\\": field.setAttribute(\\\"type\\\", bittype)\\n if bitdesc != \\\"\\\":\\n text = doc.createTextNode(bitdesc)\\n field.appendChild(text)\\n register.appendChild(field)\\n regname_old = regname\\n\\n\\n # Print our newly created XML\\n #print doc.toprettyxml(indent=\\\" \\\")\\n #OUTFILE.write(doc.saveXML(decl_reg_list))\\n OUTFILE.write(doc.toprettyxml(indent=\\\" \\\"))\\n OUTFILE.close()\",\n \"def write_dump_file(dir_name, file_name, ext, datas):\\n f_name = build_file_path(dir_name, file_name, ext)\\n with open(f_name, \\\"w\\\") as fd_out:\\n fd_out.write(\\\"Dump file for %s\\\" % file_name)\\n for name, data in datas.items():\\n fd_out.write(\\\"\\\\n\\\\n%s\\\\n%s\\\\n\\\\n\\\" % (\\\"-\\\" *80, name))\\n if isinstance(data, dict):\\n for key, value in data.items():\\n if isinstance(value, OrderedDict):\\n value = dict(value)\\n fd_out.write('\\\"%s\\\": %s\\\\n' % (key, pprint.pformat(value, indent=4)))\\n else:\\n fd_out.write(pprint.pformat(data, indent=4))\",\n \"def saving_file(xml):\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def write(self, key, value):\\n if( self.storage is None ): \\n self.storage = {};\\n if( value is None ):\\n self.storage[ key ] = None;\\n del self.storage[ key ];\\n else:\\n self.storage[ key ] = value;\",\n \"def save_settings(dic):\\n json.dump(dic, open(\\\"resources/files/settings.txt\\\", \\\"w\\\"))\\n\\n # LEGACY\\n # with open(\\\"resources/files/settings.txt\\\", \\\"w\\\", newline=\\\"\\\\n\\\") as w:\\n # for sett, val in dic.items():\\n # w.write(sett + '\\\\\\\\' + val + '\\\\n')\",\n \"def writeXmlForDET(filename,trigger,listDict,stopInfo):\\n # trigger == 'variable trigger'\\n # Variables == 'variables changed in the branch control logic block'\\n # associated_pb = 'CDF' in case multibranch needs to be performed\\n # stopInfo {'end_time': end simulation time (already stopped), 'end_ts': end time step}\\n root=ET.Element('Branch_info')\\n root.set(\\\"end_time\\\",str(stopInfo['end_time']))\\n if \\\"end_ts\\\" in stopInfo.keys():\\n root.set(\\\"end_ts\\\",str(stopInfo['end_ts']))\\n triggerNode=ET.SubElement(root,\\\"Distribution_trigger\\\")\\n triggerNode.set(\\\"name\\\",trigger)\\n for varInfo in listDict:\\n var=ET.SubElement(triggerNode,'Variable')\\n var.text=varInfo['name']\\n var.set('type',varInfo['type'])\\n var.set('old_value',str(varInfo['old_value']))\\n var.set('actual_value',str(varInfo['new_value']))\\n if 'associated_pb' in varInfo.keys():\\n var.set('probability',str(varInfo['associated_pb']))\\n with open(filename,'w') as fileObject:\\n fileObject.write(minidom.parseString(ET.tostring(root, 'utf-8')).toprettyxml(indent=\\\"\\\\t\\\"))\",\n \"def __write_mapping(encrypted_metadata: dict) -> None:\\n with open(\\\".mapping\\\", \\\"w\\\") as file:\\n json.dump(encrypted_metadata, file, indent=2)\",\n \"def write_map_to_file(dir, version, role, map_id, d):\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n path = build_output_file_path(dir, version, role, map_id)\\n with open(path, \\\"w\\\") as f:\\n json.dump(d, f, sort_keys=True, indent=4)\\n f.close()\",\n \"def dump_dict(dumpfilename, file_names, test_files, offsets_dict):\\n if _dump_dict_on:\\n file(_dumpify(umpfilename + '.txt'), 'wt').write(_od_offsets_string(file_names, offsets_dict))\\n file(o_dumpify('.strings.txt'), 'wt').write(_od_substrings_string(offsets_dict))\\n matrix = _od_offsets_matrix(file_names, offsets_dict, test_files)\\n file(_dumpify(dumpfilename + '.csv'), 'wt').write(matrix)\",\n \"def extend_attribute_dictionary(attributedict, ns, name, value):\\n\\n key = ns, name\\n if value is None:\\n if key in attributedict:\\n del attributedict[key]\\n else:\\n if ns == 'html' and key in attributedict:\\n if name == 'class':\\n # CSS classes are appended by space-separated list\\n value = attributedict[key] + ' ' + value\\n elif name == 'style':\\n # CSS styles are appended by semicolon-separated rules list\\n value = attributedict[key] + '; ' + value\\n elif name in _html_attribute_boolflags:\\n # All attributes must have a value. According to XHTML those\\n # traditionally used as flags should have their value set to\\n # the same as the attribute name.\\n value = name\\n attributedict[key] = value\",\n \"def writexml(self, writer, indent=\\\"\\\", add_indent=\\\"\\\", new_line=\\\"\\\"):\\n writer.write(indent + \\\"<\\\" + self.tagName)\\n attrs = self._get_attributes()\\n\\n for a_name in attrs.keys():\\n writer.write(\\\" %s=\\\\\\\"\\\" % a_name)\\n self.write_data(writer, attrs[a_name].value)\\n writer.write(\\\"\\\\\\\"\\\")\\n if self.childNodes:\\n writer.write(\\\">\\\")\\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\\n self.childNodes[0].writexml(writer, '', '', '')\\n else:\\n writer.write(new_line)\\n for node in self.childNodes:\\n node.writexml(writer, indent + add_indent, add_indent, new_line)\\n writer.write(indent)\\n writer.write(\\\"</%s>%s\\\" % (self.tagName, new_line))\\n else:\\n writer.write(\\\"/>%s\\\" % new_line)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6729531","0.65415585","0.6262941","0.6016264","0.5983849","0.5939113","0.58700234","0.58539236","0.5813194","0.5684836","0.56377923","0.56335074","0.5623472","0.56098765","0.55822074","0.5524061","0.5433924","0.5403624","0.5400973","0.53912425","0.5356394","0.5355461","0.53117216","0.5308995","0.5303472","0.52977353","0.5287409","0.525638","0.52556443","0.5217364","0.5191141","0.518958","0.51832473","0.5168432","0.5163782","0.51533175","0.51405287","0.5139958","0.512663","0.5097546","0.5095176","0.5083171","0.5074787","0.50734854","0.50633293","0.50527525","0.50517637","0.5039151","0.50311524","0.5029764","0.50268495","0.50233173","0.5020479","0.5018044","0.5017723","0.5011441","0.50096744","0.5003826","0.4987999","0.4977554","0.49761355","0.49752447","0.4968254","0.49614486","0.49468878","0.49390045","0.49289265","0.49220955","0.49203846","0.4918623","0.49168903","0.4912399","0.49103186","0.49079412","0.49029487","0.490111","0.4896318","0.4889191","0.48881388","0.4887237","0.48792517","0.4868855","0.48654157","0.4864868","0.48537654","0.4853054","0.48515108","0.4844764","0.48431113","0.48353437","0.48287743","0.48278123","0.48180607","0.4815106","0.48109683","0.48085722","0.48072773","0.4805849","0.4796112","0.47916442"],"string":"[\n \"0.6729531\",\n \"0.65415585\",\n \"0.6262941\",\n \"0.6016264\",\n \"0.5983849\",\n \"0.5939113\",\n \"0.58700234\",\n \"0.58539236\",\n \"0.5813194\",\n \"0.5684836\",\n \"0.56377923\",\n \"0.56335074\",\n \"0.5623472\",\n \"0.56098765\",\n \"0.55822074\",\n \"0.5524061\",\n \"0.5433924\",\n \"0.5403624\",\n \"0.5400973\",\n \"0.53912425\",\n \"0.5356394\",\n \"0.5355461\",\n \"0.53117216\",\n \"0.5308995\",\n \"0.5303472\",\n \"0.52977353\",\n \"0.5287409\",\n \"0.525638\",\n \"0.52556443\",\n \"0.5217364\",\n \"0.5191141\",\n \"0.518958\",\n \"0.51832473\",\n \"0.5168432\",\n \"0.5163782\",\n \"0.51533175\",\n \"0.51405287\",\n \"0.5139958\",\n \"0.512663\",\n \"0.5097546\",\n \"0.5095176\",\n \"0.5083171\",\n \"0.5074787\",\n \"0.50734854\",\n \"0.50633293\",\n \"0.50527525\",\n \"0.50517637\",\n \"0.5039151\",\n \"0.50311524\",\n \"0.5029764\",\n \"0.50268495\",\n \"0.50233173\",\n \"0.5020479\",\n \"0.5018044\",\n \"0.5017723\",\n \"0.5011441\",\n \"0.50096744\",\n \"0.5003826\",\n \"0.4987999\",\n \"0.4977554\",\n \"0.49761355\",\n \"0.49752447\",\n \"0.4968254\",\n \"0.49614486\",\n \"0.49468878\",\n \"0.49390045\",\n \"0.49289265\",\n \"0.49220955\",\n \"0.49203846\",\n \"0.4918623\",\n \"0.49168903\",\n \"0.4912399\",\n \"0.49103186\",\n \"0.49079412\",\n \"0.49029487\",\n \"0.490111\",\n \"0.4896318\",\n \"0.4889191\",\n \"0.48881388\",\n \"0.4887237\",\n \"0.48792517\",\n \"0.4868855\",\n \"0.48654157\",\n \"0.4864868\",\n \"0.48537654\",\n \"0.4853054\",\n \"0.48515108\",\n \"0.4844764\",\n \"0.48431113\",\n \"0.48353437\",\n \"0.48287743\",\n \"0.48278123\",\n \"0.48180607\",\n \"0.4815106\",\n \"0.48109683\",\n \"0.48085722\",\n \"0.48072773\",\n \"0.4805849\",\n \"0.4796112\",\n \"0.47916442\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80239266"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11585,"cells":{"query":{"kind":"string","value":"Writes a list to an xml file in the form of value value etc. attribute_name>The name of the list attribute to write. xmlwriter>The xml writer to write with. write_empty>A bool of whether to write empty values to the xml file. Default is don't write them."},"document":{"kind":"string","value":"def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n attribute_list = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for item in attribute_list:\r\n if item or write_empty:\r\n xmlwriter.WriteElementString(\"Item\", item)\r\n xmlwriter.WriteEndElement()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def write(lst):\n # TODO","def write(self, data, filename):\n id_ = 1\n weightlist_el = Element('weight-list')\n for dataset in data:\n weight_el = SubElement(weightlist_el, 'weight')\n id_el = SubElement(weight_el, 'id')\n id_el.text = str(id_)\n date_el = SubElement(weight_el, 'date')\n date_el.text = str(dataset.date) + 'T12:00:00'\n value_el = SubElement(weight_el, 'value')\n value_el.text = str(dataset.weight)\n comment_el = SubElement(weight_el, 'comment')\n comment_el.text = dataset.note\n id_ += 1\n st_tree = ElementTree(weightlist_el)\n st_tree.write(filename, encoding='UTF-8')","def write_list(outputfilename, list):\r\n try:\r\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\r\n itemwriter = csv.writer(outfile, delimiter=\",\")\r\n for item in list:\r\n itemwriter.writerow(item)\r\n except:\r\n input(\"File still open! Please close and press enter to continue\")\r\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\r\n itemwriter = csv.writer(outfile, delimiter=\",\")\r\n for item in list:\r\n itemwriter.writerow(item)","def write_list_to_file(myList, filename):\r\n\r\n with open(filename, \"w\") as outfile:\r\n for entries in myList:\r\n outfile.write(entries)\r\n\t\t\t# add a return after each line\r\n outfile.write(\"\\n\")","def write_list(args, file_list):\n if not args.listfile.endswith(\".txt\"):\n args.listfile += \".txt\"\n outputfile = open(args.listfile, 'w')\n for name in file_list:\n outputfile.write(name)\n outputfile.write(\"\\n\")\n outputfile.close()","def writexml(file):\n OUTFILE=open(file,\"w\")\n doc = xml.dom.minidom.Document()\n\n # Create the <dec_reg_list> base element\n decl_reg_list = doc.createElement(\"decl_reg_list\")\n doc.appendChild(decl_reg_list)\n\n regname_old=\"\"\n rows.pop(0)\n for row in rows:\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\n if regname != regname_old:\n # Create the register element\n register = doc.createElement(\"register\")\n register.setAttribute(\"name\", regname)\n register.setAttribute(\"offset\", offset)\n if default != \"\" : register.setAttribute(\"default\", default)\n register.setAttribute(\"type\", regtype)\n if expose_reg == \"1\": register.setAttribute(\"usr\", expose_reg)\n if depth != \"\": register.setAttribute(\"size\", depth)\n if incsz != \"\": register.setAttribute(\"incsz\", incsz)\n text = doc.createTextNode(regdesc)\n register.appendChild(text)\n decl_reg_list.appendChild(register)\n \n # Create the field element\n if bitname != \"\":\n field = doc.createElement(\"field\")\n field.setAttribute(\"name\", bitname)\n if loc !=\"\": field.setAttribute(\"loc\", addcolon(loc))\n if bittype != \"\": field.setAttribute(\"type\", bittype)\n if bitdesc != \"\":\n text = doc.createTextNode(bitdesc)\n field.appendChild(text)\n register.appendChild(field)\n regname_old = regname\n\n\n # Print our newly created XML\n #print doc.toprettyxml(indent=\" \")\n #OUTFILE.write(doc.saveXML(decl_reg_list))\n OUTFILE.write(doc.toprettyxml(indent=\" \"))\n OUTFILE.close()","def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n if attribute_name in (\"IllegalCharacters\", \"Months\"):\r\n write_empty = True\r\n dictionary = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for key in dictionary:\r\n if dictionary[key] or write_empty:\r\n xmlwriter.WriteStartElement(\"Item\")\r\n xmlwriter.WriteStartAttribute(\"Name\")\r\n xmlwriter.WriteValue(key)\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteStartAttribute(\"Value\")\r\n xmlwriter.WriteValue(dictionary[key])\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteEndElement()\r\n xmlwriter.WriteEndElement()","def save_list_to_file(the_list, filepath):\n with open(filepath, 'w') as file_handler:\n for item in the_list:\n file_handler.write(\"{}\\n\".format(item))","def export_list_to_xacro(list, filename):\n global robot, OUTPUT\n doc = Document()\n root = doc.createElement('robot')\n doc.appendChild(root)\n root.setAttribute(\"xmlns:xacro\", \"http://www.ros.org/wiki/xacro\")\n print ('exporting ' + os.path.basename(filename))\n for string in list:\n for link in robot.links:\n if robot.links[link].name.find(string) != -1:\n root.appendChild(robot.links[link].to_xml(doc))\n for joint in robot.joints:\n if robot.joints[joint].child == robot.links[link].name:\n root.appendChild(robot.joints[joint].to_xml(doc))\n write_comments_in_xacro(doc, filename)","def write_list_to_file(file_name: str, list_name: List[str]):\n # Write to a file, overwriting the old contents\n file = open(file_name, 'w')\n\n # Loop through the list, append a newline character to each line\n for item in list_name:\n file.writelines(item + '\\n')\n\n # Close the file\n file.close()","def write(file_path, tag_list, attr_name='kMDItemUserTags'):\n tag_data = ['<string>{}</string>'.format(tag) for tag in tag_list]\n tag_data.insert(0, ('<!DOCTYPE plist PUBLIC'\n '\"-//Apple//DTD PLIST 1.0//EN\"'\n '\"http://www.apple.com/DTDs/PropertyList-1.0.dtd\">'\n '<plist version=\"1.0\"><array>'))\n tag_data.append('</array></plist>')\n tag_text = ''.join(tag_data)\n\n xattr = \"com.apple.metadata:{}\".format(attr_name)\n # Other attributes you might want to try:\n # ['kMDItemOMUserTags', 'kOMUserTags',\n # 'kMDItemkeywords', 'kMDItemFinderComment']\n cmd = ['xattr',\n '-w',\n xattr,\n tag_text.encode(\"utf8\"),\n file_path]\n return utils.run_process(cmd)","def write_list(l, fname):\n thefile = open(fname, \"w\")\n for line in l:\n thefile.write(\"%s\\n\" % line)\n thefile.close()","def write_list(self, register, data):\n raise NotImplementedError","def write_into_file(name, liste):\n file = open(name, \"w\")\n for item in liste:\n file.write(item)\n file.write('\\n')\n file.close()","def write_list(self):\n with open(self.path, 'w') as file:\n for i in map(self.addziros, range(1, int(str(1) + self.number_length * '0') + 1)):\n file.write(i + '\\n')\n file.close()","def createXMLFile(list, stock_symbol, market):\n \n stock = ET.Element(\"stock\")\n \n stock.set(\"source\", 'yahoo finance')\n exchange = ET.SubElement(stock, \"exchange\")\n exchange.set(\"market\", market)\n \n for s in list: \n \n if s.array[0] == 'Date' or list[0].array[0] != 'Date':\n continue\n dividend_date = ET.SubElement(exchange, \"dividend_date\")\n dividend_date.set(\"date\", s.array[0])\n \n price = ET.SubElement(dividend_date, \"price\")\n price.text = s.array[1]\n \n \n indent(stock)\n tree = ET.ElementTree(stock)\n \n tree.write(\"dividend_history.xml\", xml_declaration=True, encoding='utf-8', method=\"xml\")\n print 'xml created for ' + stock_symbol","def __writeToFile(self, filePath, lst): \n \n if not self.outDir is None: \n filePath = os.path.join(self.outDir, filePath) \n \n open(filePath,'a').writelines(lst)","def write_list_to_file(input_list, output_folder, delimiter=\" \", header=None):\n with open(output_folder, 'w') as doc_out:\n if header:\n doc_out.write(delimiter.join(header) + \"\\n\")\n for element in input_list:\n doc_out.write(delimiter.join([str(i) for i in element]) + \"\\n\")","def write_to_xml(filename, xmlelement):\n xmlpretty = prettify(xmlelement)\n with open(filename, 'w') as f:\n f.write(xmlpretty)","def write_list_to_file(program, list_to_write):\n with open(program.split('.')[0] + \".output.json\", 'a+') as output_file:\n output_file.write(json.dumps(list_to_write, indent=3, sort_keys=False))","def write(self):\r\n for prop in self.prpnames:\r\n elem = SubElement(self._root, prop)\r\n data = self.__getattribute__(prop)\r\n if self.prpnames[prop]['type'] == \"text\":\r\n elem.text = data\r\n elif self.prpnames[prop]['type'] == 'list':\r\n for x in data:\r\n SubElement(elem, 'regel').text = x\r\n elif self.prpnames[prop]['type'] == 'attr':\r\n elem.set(self.prpnames[prop]['naam'], data)\r\n tree = ElementTree(self._root)\r\n tree.write(self._fn)\r\n if not self.exists:\r\n self.exists = True","def toXML( self, indent = '', **kwargs ) :\n\n return( '\\n'.join( self.toXMLList( **kwargs ) ) )","def write_package_list(_, package_list, *args):\n logger.debug(\"Attempting to write package list\")\n try:\n with open(Base.get_package_list_filepath(), \"w\") as file:\n yaml.dump(package_list, file, sort_keys=True)\n except:\n logger.exception(\"Could not write package list\")\n exit(1)\n logger.debug(\"Packages written to file\")","def writexml(self, writer, indent=\"\", add_indent=\"\", new_line=\"\"):\n writer.write(indent + \"<\" + self.tagName)\n attrs = self._get_attributes()\n\n for a_name in attrs.keys():\n writer.write(\" %s=\\\"\" % a_name)\n self.write_data(writer, attrs[a_name].value)\n writer.write(\"\\\"\")\n if self.childNodes:\n writer.write(\">\")\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\n self.childNodes[0].writexml(writer, '', '', '')\n else:\n writer.write(new_line)\n for node in self.childNodes:\n node.writexml(writer, indent + add_indent, add_indent, new_line)\n writer.write(indent)\n writer.write(\"</%s>%s\" % (self.tagName, new_line))\n else:\n writer.write(\"/>%s\" % new_line)","def write_2D_list(self, list_name, statistics):\n filename = os.getcwd() + list_name + \".csv\"\n print(filename)\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n with open(filename, mode='w+', newline='',encoding='utf8') as list_file:\n list_writer = csv.writer(list_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n for item in statistics:\n list_writer.writerow(item)","def save_to_xml(self, xwriter):\r\n\r\n xwriter.WriteStartElement(\"Profile\")\r\n xwriter.WriteAttributeString(\"Name\", self.Name)\r\n xwriter.WriteStartAttribute(\"Version\")\r\n xwriter.WriteValue(self.Version)\r\n xwriter.WriteEndAttribute()\r\n\r\n for var_name in self.__dict__:\r\n var_type = type(getattr(self, var_name))\r\n\r\n if var_type is str and var_name != \"Name\":\r\n self.write_string_to_xml(var_name, xwriter)\r\n\r\n elif var_type is bool:\r\n self.write_bool_to_xml(var_name, xwriter)\r\n\r\n elif var_type is dict:\r\n self.write_dict_to_xml(var_name, xwriter)\r\n\r\n elif var_type is list and var_name != \"ExcludeRules\":\r\n self.write_list_to_xml(var_name, xwriter)\r\n\r\n xwriter.WriteStartElement(\"ExcludeRules\")\r\n xwriter.WriteAttributeString(\"Operator\", self.ExcludeOperator)\r\n xwriter.WriteAttributeString(\"ExcludeMode\", self.ExcludeMode)\r\n for rule in self.ExcludeRules:\r\n if rule:\r\n rule.save_xml(xwriter)\r\n xwriter.WriteEndElement()\r\n \r\n xwriter.WriteEndElement()","def write_list_to_file(ls, save_path):\n # Open in appendation mode given that this function may be called multiple\n # times on the same file (positive and negative sentiment are in separate\n # directories).\n out_file = open(save_path, \"w+\")\n for example in ls:\n out_file.write(example)\n out_file.write('\\n')","def writeXml(self):\n curdir = os.getcwd()\n os.chdir(self.Imagedir)\n allImageLists = [self.sciImlist, self.ctxImlist, self.wgtImlist, self.rmsImlist]\n \n for imlist in allImageLists:\n for im in imlist:\n file = xmlUtil.markupImage(im,dataset=self.obsName)\n \n # Don't write these images as output of this module, which\n # really doesn't have any.\n \n #if file not in self.outputList.keys():\n # self.outputList[file] = [im]\n \n os.chdir(curdir)\n return","def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\r\n string = getattr(self, attribute_name)\r\n if string or write_empty:\r\n xmlwriter.WriteElementString(attribute_name, string)","def write_bool_to_xml(self, attribute_name, xmlwriter):\r\n xmlwriter.WriteStartElement(attribute_name)\r\n xmlwriter.WriteValue(getattr(self, attribute_name))\r\n xmlwriter.WriteEndElement()","def SaveListFile(file,lst):\n\tlst = [str(i) +\"\\n\" for i in lst]\n\tif len(lst) == 0:\n\t\treturn\n\twith open(file,'w') as f:\n\t\tf.writelines(lst)\n\treturn lst","def write(self, *items):\n for item in items:\n if type(item) == list:\n for _item in item:\n self.write(_item)\n elif type(item) == str:\n self.include(item)\n else:\n item.write(self)","def write_lis_lis(lis_lis,filename,cols=[]):\n lis_lis = [[str(l) for l in lis]\n for lis in lis_lis] # trans every element to str\n #make all inner lists of the same length\n inner_lis_max_len = max(len(lis) for lis in lis_lis)\n lis_lis = [lis + (inner_lis_max_len - len(lis)) * [''] for lis in lis_lis]\n #make element in the same list have the same length\n aligned = []\n for lis in lis_lis:\n width = max([len(l) for l in lis])\n lis = [l + (width - len(l)) * ' ' for l in lis]\n aligned.append(lis)\n new_lis_lis = [';'.join([aligned[i][j] for i in range(len(aligned))]) for j in range(len(aligned[0]))]\n with open(filename+'.txt','w') as w_f:\n if cols:\n print >> w_f,'\\t;'.join(cols)\n for l in new_lis_lis:\n print >> w_f,l","def write_to(self, fp):\n fp.write('<')\n fp.write(self.tag)\n for k, v in self.attrs.iteritems():\n fp.write(' ')\n fp.write(k),\n fp.write('=\"')\n fp.write(xml_escape(v))\n fp.write('\"')\n if len(self.contents) == 0:\n fp.write(' />')\n else:\n fp.write('>')\n for item in self.contents:\n if isinstance(item, basestring):\n item = xml_escape(item)\n fp.write(item)\n elif isinstance(item, Markup) or isinstance(item, Element):\n item.write_to(fp)\n else:\n raise TypeError('Item %r must be either a string, '\n '``Element``, or ``Markup``' % item)\n fp.write('</')\n fp.write(self.tag)\n fp.write('>')","def persist_list_to_csv(liste, nom_fichier):\n with open(nom_fichier, 'w') as f:\n for elem in liste :\n f.write(\"{}\\n\".format(elem))","def write_list(self, data, delimiter=\"\\n\"):\n if self.check_list_exists(data):\n with opened_w_error(self.filename, self.lock, \"a\") as (f, err):\n if err:\n logging.error(\"File '%s'. Error: %s\", self.filename, err)\n else:\n f.write(delimiter.join(data))\n else:\n logging.error(\"Data isn't list or it's not contains elements\")","def writeAttributes(self, *args):\n return _libsbml.ASTBasePlugin_writeAttributes(self, *args)","def writeAttribute(self, *args):\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\n\n\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)","def dump(self, filename, plist):\r\n xml = self.tostring(plist)\r\n fd = open(filename, 'wb')\r\n try:\r\n fd.write(xml)\r\n finally:\r\n fd.close()","def save_output(output_list):\n wb = Workbook()\n ws1 = wb.active\n ws1.title = 'Sheet1'\n # Create title for columns\n columns_titles = ['Name', 'Condition description', 'birad[0]','birad[1]','birad[2]','birad[3]','birad[4]','birad[5]','birad[6]','Relevant modalities',\n 'Relevant findings', 'Unique findings','Additional info',\n 'Parameters', 50, 30, 10, 5, 1,'Pathogenomonic', 'Negative',\n 'Ignore', 'Associated conditions', 'Differential diagnosis', 'Notes']\n ws1.append(columns_titles)\n # Create list for output file\n for ol in output_list:\n for o in ol:\n cr_list = create_list(o)\n ws1.append(cr_list)\n wb.save(filename=OUTPUT_FILE)","def get_xml(self):\n xml = svgwrite.etree.etree.Element(self.elementname)\n if self.debug:\n self.validator.check_all_svg_attribute_values(self.elementname, self.attribs)\n for attribute, value in self.attribs.items():\n # filter 'None' values\n if value is not None:\n value = self.value_to_string(value)\n if value: # just add not empty attributes\n xml.set(attribute, value)\n \n for element in self.elements:\n xml.append(element)\n return xml","def write_string_list_to_file(string_list, filename):\n with open(filename, 'w') as f:\n for element in string_list:\n f.write(element+'\\n')","def write_sequence(list):\n pass","def write_graph_list(name, graph_list, data_root):\n\n data_path = os.path.join(data_root, name)\n if not os.path.exists(data_path):\n os.makedirs(data_path)\n\n # compute right number of trailing zeros for file names\n format_positions = ceil(log10(len(graph_list)))\n\n for i, g in enumerate(graph_list):\n lines = nx.generate_gml(g)\n\n # stupid networkx requires labels to be equal to node ids.\n # we need to fix this\n def sanitize_labels(x):\n def getint(v:str):\n return int(v.strip('\"'))\n if x.find('label') == -1:\n return x + '\\n'\n else:\n v = x[10:]\n label = g.node[getint(v)]['label']\n return f' label \"{label}\"\\n'\n\n fixed_lines = map(sanitize_labels, lines)\n\n f = open(os.path.join(data_path, f'{i:0{format_positions}d}.gml'), 'w')\n f.writelines(fixed_lines)\n f.close()","def write_to_text_file(directory, filename, data_structure_to_be_written_out):\n if os.path.exists(directory + filename):\n print(f\"Writing out list to {filename}....\\n\")\n with open(directory + filename, \"w\") as filehandle:\n if isinstance(data_structure_to_be_written_out, list):\n filehandle.writelines(\n f\"{item}\\n\" for item in data_structure_to_be_written_out\n )\n elif isinstance(data_structure_to_be_written_out, dict):\n filehandle.writelines(\n f\"{key} {data_structure_to_be_written_out[key]}\\n\"\n for key in data_structure_to_be_written_out.keys()\n )","def toXML(self):\n return self._xmlpre+\"\\n\".join(map(lambda f:f.toXML(),self._items))+self._xmlpost","def xmlWrite(self, xmlWriter, font, value, name, attrs):\n raise NotImplementedError(self)","def save_double_list(list1, list2, filename):\r\n the_file = open(filename, \"wb\")\r\n try:\r\n writer = csv.writer(the_file)\r\n if len(list1)!=len(list2):\r\n raise Exception(\"Saving a double list : The list have not the same length !\")\r\n for i in range(len(list1)):\r\n writer.writerow( (list1[i], list2[i]) ) \r\n finally:\r\n the_file.close()","def save_list_to_file(content: list, dst_path: str, append=False) -> None:\n with io.open(file=dst_path, mode=\"a\" if append else \"w\", encoding='utf-8') as destination_file:\n for element in content:\n destination_file.write(element + \"\\n\")","def writeXmlForDET(filename,trigger,listDict,stopInfo):\n # trigger == 'variable trigger'\n # Variables == 'variables changed in the branch control logic block'\n # associated_pb = 'CDF' in case multibranch needs to be performed\n # stopInfo {'end_time': end simulation time (already stopped), 'end_ts': end time step}\n root=ET.Element('Branch_info')\n root.set(\"end_time\",str(stopInfo['end_time']))\n if \"end_ts\" in stopInfo.keys():\n root.set(\"end_ts\",str(stopInfo['end_ts']))\n triggerNode=ET.SubElement(root,\"Distribution_trigger\")\n triggerNode.set(\"name\",trigger)\n for varInfo in listDict:\n var=ET.SubElement(triggerNode,'Variable')\n var.text=varInfo['name']\n var.set('type',varInfo['type'])\n var.set('old_value',str(varInfo['old_value']))\n var.set('actual_value',str(varInfo['new_value']))\n if 'associated_pb' in varInfo.keys():\n var.set('probability',str(varInfo['associated_pb']))\n with open(filename,'w') as fileObject:\n fileObject.write(minidom.parseString(ET.tostring(root, 'utf-8')).toprettyxml(indent=\"\\t\"))","def CSVWriter (iterable, outLoc, header=\"\", ):\n if not iterable:\n print (\"nothing to write\")\n return 0\n\n out = open(outLoc, 'w')\n\n if header:\n out.write(header+'\\n')\n\n #Only works if iterable is a nested list\n for member in iterable:\n for item in member:\n out.write(str(item)+',')\n out.write('\\n')\n\n print(\"write to \"+outLoc+\" successful.\")\n return 1","def __writeElement(fd, elm):\n\n print(\n \" <td>\",\n \" <h4>\" + elm[\"name\"] + \"</h4>\",\n \" <ul>\",\n \" <li>\" + str(elm[\"number\"]) + \"</li>\",\n \" <li>\" + elm[\"small\"] + \"</li>\",\n \" <li>\" + str(elm[\"molar\"]) + \"</li>\",\n \" </ul>\",\n \" </td>\",\n sep=\"\\n\",\n file=fd\n )","def write_listing(listing, dep, pom):\n print(f'| {dep.groupId} | {dep.artifactId} | {dep.version} ', end='', file=listing)\n print(f'| {pom.authors} ', end='', file=listing)\n print(f'| {pom.license.name} | {pom.license.url} ', end='', file=listing)\n print(f'|', file=listing)","def xmlwrite(self, doc, filename):\n pathname = os.path.join(self.session.session_dir, filename)\n f = open(pathname, \"w\")\n doc.writexml(writer=f, indent=\"\", addindent=\" \", newl=\"\\n\", encoding=\"UTF-8\")\n f.close()","def write_the_contents_to_the_same_file(self):\n if not len(self.student_list):\n print('There is no contents to write')\n return\n\n if self._filename is None:\n self._filename = self.input_filename()\n\n with open(self._filename, 'w') as OUT:\n OUT.write(self.student_list.to_csv(date_format='%Y-%m-%d',\n sep='\\t', header=False, columns=self.columns_to_save))\n print(f'Data are saved into {self._filename!r}')","def toXML_strList( self, indent = \"\", **kwargs ) :\n\n attrs = self.standardXML_attributes( )\n return [ '%s<%s path=\"%s\"%s/>' % ( indent, self.moniker, self.path, attrs ) ]","def write_nested_string_list_to_file(string_list, filename):\n with open(filename, 'w') as f:\n for i in range(0,len(string_list)):\n for element in string_list[i]:\n f.write(element+'\\t'+str(i)+'\\n')","def xml(self, indent):\n if self.__commentOut:\n prefix = \"<!--\"\n suffix = \" -->\"\n else:\n prefix = \"\"\n suffix = \"\"\n hubs = self.__stringMap.keys()\n if self.OMIT_HUB_NUMBER or len(hubs) != 1:\n nStr = \"\"\n else:\n nStr = \" hub=\\\"%d\\\"\" % hubs[0]\n return \"%s%s<domConfigList%s>%s</domConfigList>%s\" % \\\n (prefix, indent, nStr, self.__fileName, suffix)","def writeToTempXml(self):\n name = self.fileToProcess.name\n all_tokens = ET.Element(\"tokens\")\n for token in self.tokensTable:\n if token.getType() == KEYWORD:\n keyword = ET.SubElement(all_tokens, \"keyword\")\n keyword.text = ' '+token.getValue()+' '\n elif token.getType() == IDENTIFIER:\n identifier = ET.SubElement(all_tokens, \"identifier\")\n identifier.text = ' '+token.getValue()+' '\n elif token.getType() == SYMBOL:\n symbol = ET.SubElement(all_tokens, \"symbol\")\n symbol.text = ' '+token.getValue()+' '\n elif token.getType() == STRING_CONST:\n stringConstant = ET.SubElement(all_tokens, \"stringConstant\")\n stringConstant.text = ' '+token.getValue()+' '\n elif token.getType() == INT_CONST:\n integerConstant = ET.SubElement(all_tokens, \"integerConstant\")\n integerConstant.text = ' '+token.getValue()+' '\n tree = ET.ElementTree(all_tokens)\n tree.write(name + 'T' + '.xml')","def output_to_file(utilist, filepath=\"demo.csv\"):\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\n with open(filepath, \"a\") as f:\n f.write(utilist + \"\\n\")","def write(self):\n o = []\n print self.comment\n o.append('% ' + re.sub(\"\\n\", \"\\n% \", self.comment))\n o.append(\"@relation \" + self.esc(self.relation))\n for a in self.attributes:\n at = self.attribute_types[a]\n if at == 'numeric':\n o.append(\"@attribute \" + self.esc(a) + \" numeric\")\n elif at == 'string':\n o.append(\"@attribute \" + self.esc(a) + \" string\")\n elif at == 'nominal':\n o.append(\"@attribute \" + self.esc(a) +\n \" {\" + ','.join(self.attribute_data[a]) + \"}\")\n else:\n raise \"Type \" + at + \" not supported for writing!\"\n o.append(\"\\n@data\")\n for d in self.data:\n line = []\n for e, a in zip(d, self.attributes):\n at = self.attribute_types[a]\n if at == 'numeric':\n line.append(str(e))\n elif at == 'string':\n line.append(self.esc(e))\n elif at == 'nominal':\n line.append(e)\n else:\n raise \"Type \" + at + \" not supported for writing!\"\n o.append(','.join(line))\n return \"\\n\".join(o) + \"\\n\"","def compile_parameter_list(self):\n\n\t\txml = self.tokenizer.keyword() + self.tokenizer.identifier()\n\n\t\tself.outfile.write(xml)\n\n\t\tif self.tokenizer.get_token() == ',':\n\t\t\tself.outfile.write(self.tokenizer.symbol())\n\t\t\tself.compile_parameter_list()","def debugDumpAttrList(self, output, depth):\n libxml2mod.xmlDebugDumpAttrList(output, self._o, depth)","def writeAttributes(self, *args):\n return _libsbml.MultiASTPlugin_writeAttributes(self, *args)","def list_to_file(l, file_name):\r\n fw = open(file_name, 'w', encoding = 'utf-8')\r\n fw.write('\\n'.join(l))\r\n fw.close()","def write_xml(self, xmlfile):\n system.xml.write_file(xmlfile, self.status, 'status')","def writeToFile(self, outfile):\n self.finalizePlot()\n\n kk = self.mElements.keys()\n kk.sort()\n kk.reverse()\n\n ## make sure the image size is ok\n min_x, min_y, max_x, max_y = 0, 0, 0, 0\n \n for k in kk:\n for e in self.mElements[k]:\n for x in ('x', 'x2', 'x1'):\n if x in e.attributes:\n v = e.attributes[x] \n min_x = min(min_x, v )\n max_x = max(max_x, v )\n for y in ('y', 'y2', 'y1'):\n if y in e.attributes:\n v = e.attributes[y]\n min_y = min(min_y, v )\n max_y = max(max_y, v )\n\n min_x, min_y = int(math.floor(min_x)), int(math.floor(min_y))\n max_x, max_y = int(math.floor(max_x)), int(math.floor(max_y)) \n\n for k in kk:\n for e in self.mElements[k]:\n for x in ('x', 'x2', 'x1'):\n if x in e.attributes:\n e.attributes[x] -= min_x\n for x in ('y', 'y2', 'y1'):\n if y in e.attributes:\n e.attributes[y] -= min_y\n\n ## now add all the elements\n self.mRoot = SVGdraw.drawing()\n self.mDraw = SVGdraw.svg( (0, 0, self.mPageWidth - min_x, self.mPageHeight - min_y ) , \"100%\", \"100%\" )\n \n for k in kk:\n for e in self.mElements[k]:\n self.mDraw.addElement( e )\n \n self.mRoot.setSVG(self.mDraw)\n\n tfile = tempfile.mktemp()\n \n self.mRoot.toXml( tfile )\n\n lines = open(tfile,\"r\").readlines()\n \n outfile.write(string.join(lines,\"\"))\n outfile.write(\"\\n\")\n \n os.remove(tfile)","def write_config(filename, data):\n\n # Encode data\n desiredgraphs = ET.Element('desiredgraphs')\n\n for graph in data:\n curr_graph = ET.SubElement(desiredgraphs, 'graph', {key: value for key, value in graph.items() if not isinstance(value, list) and value})\n for key, lst in [(key, value) for key, value in graph.items() if isinstance(value, list) and value]:\n for item in lst:\n ET.SubElement(curr_graph, key, {key: value for key, value in item.items() if value})\n\n # Write\n with open(filename, 'w') as g:\n g.write(xml_to_string(desiredgraphs))","def saveToXml(self) -> org.jdom.Element:\n ...","def write_feed(self, elements, context, filename=None, feed_type='atom'):\n old_locale = locale.setlocale(locale.LC_ALL)\n locale.setlocale(locale.LC_ALL, 'C')\n try:\n self.site_url = context.get('SITEURL', get_relative_path(filename))\n self.feed_url= '%s/%s' % (self.site_url, filename)\n\n feed = self._create_new_feed(feed_type, context)\n\n for item in elements:\n self._add_item_to_the_feed(feed, item)\n\n if filename:\n complete_path = os.path.join(self.output_path, filename)\n try:\n os.makedirs(os.path.dirname(complete_path))\n except Exception:\n pass\n fp = open(complete_path, 'w')\n feed.write(fp, 'utf-8')\n print u' [ok] writing %s' % complete_path\n\n fp.close()\n return feed\n finally:\n locale.setlocale(locale.LC_ALL, old_locale)","def writeList2File(filename, array, overwrite=False, separator=';'):\n mode = 'a'\n if overwrite:\n mode = 'w'\n file = open(filename, mode)\n file.write(separator.join(map(str,array)) + '\\n')","def write_xml(self, filepath=None, escapeNewline=True, indent=False):\n\n if not filepath:\n filepath = self.mFilePath\n\n if indent:\n self.indent(self.tree.getroot())\n\n output = StringIO()\n\n self.tree.write(output, encoding=\"UTF-8\")\n\n outFile = open(filepath, \"w\")\n if escapeNewline:\n # we need to make sure newline 
 is written correctly\n print >> outFile, re.sub(\"###newline_escape###\", \"
\", output.getvalue())\n else:\n print >> outFile, output.getvalue()\n\n outFile.close","def test_save_list_to_file(self):\n task_list = TaskList()\n task1 = Task()\n output_file_path = self.testing_files[0]\n task1.set_name('Test Task One')\n task1.set_minutes(30)\n task1.set_notes('This is a great test task')\n task_list.add_task(task1)\n\n self.assert_equal(True, task_list.save_to_file(output_file_path))\n self.assert_equal(True, os.path.isfile(output_file_path))","def write_list_file(output_file, clip_list_arr):\n list_file = output_file+'_clip_list.txt'\n print \"list_file: \", list_file\n f = open(list_file, 'w')\n for clip in clip_list_arr:\n line = 'file '+clip\n f.write(\"%s\\n\" % line)\n # Add in a divider movie between clips? (it could go here)\n f.close()\n # print 'list_file', list_file\n # print clip_list_arr\n\n return list_file","def list_to_file(itemlist, filename):\n # Create dir if needed\n dir_path = os.path.dirname(filename)\n if not os.path.isdir(dir_path):\n os.makedirs(dir_path)\n\n # Delete existing file\n if os.path.exists(filename):\n os.remove(filename)\n\n # itemlist.sort()\n\n # Write new file\n with open(filename, 'w') as f:\n fname = os.path.basename(filename)\n if 'local' in fname:\n f.write('# Local development dependencies go here\\n-r base.txt\\n\\n')\n if 'production' in fname:\n f.write('# Pro-tip: Try not to put anything here. Avoid dependencies in production that aren\\'t in development.\\n-r base.txt\\n\\n')\n if 'test' in fname:\n f.write('# Test dependencies go here.\\n-r base.txt\\n\\n')\n if 'subdependencies' in fname:\n f.write('# Sub-dependencies (i.e. most likely dependencies of top level dependencies).\\n-r base.txt\\n\\n')\n for item in itemlist:\n f.write('%s\\n' % item)","def writeXmlOutput(self, output, prettyname):\n\n # make an alias\n w = output.write\n\n # bumf that says file was autogenerated\n self.writeHeaderOutput((\"<!--\",\"-->\"), output, prettyname)\n\n # build the first table\n w('<Chapter anchor=\"Summary.0\">\\n')\n w(' <Title>Summary</Title>\\n')\n w(' <Description anchor=\"Summary.1\">\\n')\n w(xmlTableStart(\"SymbolList\", 3, [\"Symbol\", \"Type\", \"Description\"]))\n\n for d in self.data:\n # now just print it out\n line = d.buildSimpleXmlDeclaration()\n if len(line) != 0:\n w(line+\"\\n\\n\")\n\n w(xmlTableEnd('Symbol list'))\n w(' </Description>\\n')\n w('</Chapter>\\n\\n\\n')\n\n w('<Chapter anchor=\"Baton.0\">\\n')\n w(' <Title>Baton</Title>\\n')\n w(' <Description anchor=\"Baton.1\">\\n')\n w(' </Description>\\n')\n for d in self.data:\n # now just print it out\n line = d.buildFullXmlDeclaration(1)\n if len(line) != 0:\n output.write(line+\"\\n\\n\")\n\n w('</Chapter>\\n\\n\\n')","def write_csv(fname, olist):\n ofile = open(fname, \"wb\")\n writer = csv.writer(ofile, delimiter=',', quotechar='\"',\n quoting=csv.QUOTE_ALL)\n writer.writerows(olist)","def setListDoc(self, list):\n if list is None: list__o = None\n else: list__o = list._o\n libxml2mod.xmlSetListDoc(list__o, self._o)","def writeCSV(path,aList):\n\twith open(path,'wb') as w:\n\t\ta = csv.writer(w, delimiter = ',')\n\t\ta.writerows(aList)\n\tw.close()","def toXMLList( self, indent = '', **kwargs ) :\n\n indent2 = indent + kwargs.get( 'incrementalIndent', ' ' )\n\n attributesStr = baseModule.xDataFunctional.attributesToXMLAttributeStr( self )\n if( None in self.fixedDomain( ) ) : attributesStr += ' domainMin=\"%s\" domainMax=\"%s\"' % ( self.domainMin, self.domainMax )\n if( self.lowerIndex != 0 ) : attributesStr += ' lowerIndex=\"%s\"' % self.lowerIndex\n\n # FIXME: converting self.coefficients to values for printing. Should it be stored as values in the first place?\n coefs = valuesModule.values( self.coefficients, valueType = self.valueType, sep = self.__sep )\n\n XMLList = [ '%s<%s%s>' % ( indent, self.moniker, attributesStr ) ]\n if( self.isPrimaryXData( ) and ( self.axes is not None ) ) :\n XMLList += self.axes.toXMLList( indent2, **kwargs )\n XMLList += coefs.toXMLList( indent2, **kwargs )\n XMLList[-1] += '</%s>' % self.moniker\n return XMLList\n else:\n XMLList += coefs.toXMLList( '', **kwargs )\n XMLList[-1] += '</%s>' % self.moniker\n return( [ ''.join( XMLList ) ] )","def writeXMLDecl(self):\n return _libsbml.XMLOutputStream_writeXMLDecl(self)","def to_xml(self, f=None):\r\n if not f:\r\n from tempfile import TemporaryFile\r\n f = TemporaryFile()\r\n print >> f, '<?xml version=\"1.0\" encoding=\"UTF-8\"?>'\r\n print >> f, '<Domain id=\"%s\">' % self.name\r\n for item in self:\r\n print >> f, '\\t<Item id=\"%s\">' % item.name\r\n for k in item:\r\n print >> f, '\\t\\t<attribute id=\"%s\">' % k\r\n values = item[k]\r\n if not isinstance(values, list):\r\n values = [values]\r\n for value in values:\r\n print >> f, '\\t\\t\\t<value><![CDATA[',\r\n if isinstance(value, unicode):\r\n value = value.encode('utf-8', 'replace')\r\n else:\r\n value = unicode(value, errors='replace').encode('utf-8', 'replace')\r\n f.write(value)\r\n print >> f, ']]></value>'\r\n print >> f, '\\t\\t</attribute>'\r\n print >> f, '\\t</Item>'\r\n print >> f, '</Domain>'\r\n f.flush()\r\n f.seek(0)\r\n return f","def write_to_xml(dictData, metadata, xmlfile):\n\tfout = codecs.open(xmlfile, 'w', 'utf-8')\n\tfout.write('<?xml version = \"1.0\" encoding = \"UTF-8\" standalone = \"no\" ?>\\n')\n\tfout.write('<?xml-stylesheet type=\"text/xsl\" href=\"maketable.xsl\"?>\\n')\n\tfout.write('<root>\\n')\n\tfout.write('<meta>\\n')\n\tfor key, value in metadata.items():\n\t\tfout.write('<' + key + '>' + value + '</' + key + '>\\n')\n\tfout.write('</meta>\\n')\n\tfout.write('<content>\\n')\n\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\n\t\txmlline = ''\n\t\txmlline += '<word><headword>' + hw + '</headword><meanings>'\n\t\tfor meaning in meanings:\n\t\t\txmlline += '<m>' + meaning + '</m>'\n\t\txmlline += '</meanings>'\n\t\txmlline += '<verse>'\n\t\tlines = verse.split('<BR>')\n\t\tfor line in lines:\n\t\t\txmlline += '<line>' + line + '</line>'\n\t\txmlline += '</verse>'\n\t\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\n\t\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\n\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\tfout.write(xmlline + '\\n')\n\t\txmlline = ''\n\tfout.write('</content>\\n</root>')\n\tfout.close()\n\n\t# Give some summary to the user\n\tprint('XML file generated. Success!')\n\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))","def writeExtensionElements(self, *args):\n return _libsbml.SBase_writeExtensionElements(self, *args)","def savealist(alist, filename):\n out = open(filename, \"w\")\n for i in alist:\n out.write(str(i) + \"\\n\") # if i is numeric\n out.close()","def save_lists_to_file(filename, elev_list, dist_list):\n import numpy as np\n\n np.save(file=filename,arr=np.array([elev_list, dist_list]))","def htmlList(contents, attr='', listType='ul'):\n return '<%s%s>\\n%s</%s>\\n' % (listType,sep(attr),contents,listType)","def create_xml_regression(lfiles, lsbj, foxml):\n\n impl = xml.dom.minidom.getDOMImplementation()\n doc = impl.createDocument(None, \"some_tag\", None)\n top_element = doc.documentElement\n\n e = doc.createElement('subject')\n e.setAttribute('id', 'case')\n\n for i, fn in enumerate(lfiles):\n v = doc.createElement('visit')\n v.setAttribute('id', \"subj{}\".format(i))\n\n f = doc.createElement('filename')\n f.setAttribute('object_id', \"face\")\n t = doc.createTextNode(fn)\n f.appendChild(t)\n\n a = doc.createElement('age')\n x = doc.createTextNode(str(lsbj[i][\"age\"]))\n a.appendChild(x)\n\n\n v.appendChild(f)\n v.appendChild(a)\n e.appendChild(v)\n\n top_element.appendChild(e)\n\n with open(foxml, \"w\") as fo:\n fo.write(doc.toprettyxml())","def write_csv(list_file, path):\n\n\twith open(path, 'w') as f:\n\t\twriter = csv.writer(f, delimiter=',')\n\t\tfor i in list_file:\n\t\t\twriter.writerow(i)","def writeItemsToCSV(self, fileName, itemList):\n\t\twith open(fileName, 'w') as csvFile:\n\t\t csvWriter = csv.writer(csvFile, delimiter=',')\n\t\t # Column titles\n\t\t csvWriter.writerow([\"Brandname\",\"Productname\",\"Colors\",\"Sizes\",\"Description\",\"Materials\",\"Maintenance\",\"RegularPrice\",\"CurrentPrice\"])\n\t\t for item in itemList:\n\t\t csvWriter.writerow(list(item))","def write(xml, outf):\n my_dom = dom.parseString(tostring(xml))\n f = open(outf, 'w')\n PrettyPrint(my_dom, f, encoding='utf-8')\n f.close()","def __att_to_xml(self, doc, parent, att_name, att):\r\n \r\n if att == None:\r\n e=doc.createElementNS(None, att_name)\r\n parent.appendChild(e)\r\n e.appendChild(doc.createTextNode(''))\r\n elif type(att) == str or type(att) == unicode or type(att) == int:\r\n e=doc.createElementNS(None, att_name)\r\n parent.appendChild(e)\r\n e.appendChild(doc.createTextNode('%s' % att))\r\n #e.setAttributeNS(None, \"class\", \"sample\")\r\n elif hasattr(att, 'to_xml'):\r\n # Looks like an entity object, so call it's to_xml and append it\r\n child_frag = att.to_xml(doc)\r\n parent.appendChild(child_frag)\r\n elif type(att) == list or type(att) == tuple:\r\n # Looks like an ordered collection\r\n col = doc.createElementNS(None, att_name)\r\n parent.appendChild(col)\r\n for item in att:\r\n self.__att_to_xml(doc, col, att_name, item)\r\n elif isinstance(att, dict):\r\n # Looks like a dictionary\r\n col = doc.createElementNS(None, att_name)\r\n parent.appendChild(col)\r\n att_keys = att.keys()\r\n att_keys.sort()\r\n for item in att_keys:\r\n self.__att_to_xml(doc, col, item, att[item])\r\n else:\r\n # Not sure, so treat it like a string\r\n e=doc.createElementNS(None, att_name)\r\n parent.appendChild(e)\r\n e.appendChild(doc.createTextNode('%s' % att))","def XMLWrite(one, two, three, four, five, six, seven, eight):\n filePath = \"/mnt/RAM/kanban.xml\"\n xmlFile = open(filePath, 'w')\n\n xmlFile.write('<kanbanShelf>\\n')\n xmlFile.write(' <one>%s</one>\\n' % one)\n xmlFile.write(' <two>%s</two>\\n' % two)\n xmlFile.write(' <three>%s</three>\\n' % three)\n xmlFile.write(' <four>%s</four>\\n' % four)\n xmlFile.write(' <five>%s</five>\\n' % five)\n xmlFile.write(' <six>%s</six>\\n' % six)\n xmlFile.write(' <seven>%s</seven>\\n' % seven)\n xmlFile.write(' <eight>%s</eight>\\n' % eight)\n xmlFile.write('</kanbanShelf>')","def write_manifests( file_lists, target_dir, output_dir ):\n for i, lst in enumerate( file_lists ):\n with open( os.path.join( output_dir, \"manifest-{}.txt\".format( i ) ), \"w\" ) as fout:\n for r in lst:\n fout.write( insert_rsync_marker( r, target_dir ) + \"\\n\" )","def save_list(todo_list, save_location):\r\n data_file_w = open(save_location,\r\n \"w\") # open the save file and clear the data from it\r\n data_file_w.write(\"Warning: The Todo-List Program will not be able to \"\r\n \"load this save file if it is incorrectly modified. \"\r\n \"Modify at your own risk. The structure is Entry \"\r\n \"Text, Entry Priority as a number, Entry Group as a \"\r\n \"number (Not Yet Utilized, but necessary), and Entry \"\r\n \"Visibility as a boolean, each on a separate line, a \"\r\n \"single line gap in between, and the \"\r\n \"very first line is skipped\\n\")\r\n for item in todo_list:\r\n data_file_w.write(\"{0}\\n{1}\\n{2}\\n{3}\\n\\n\".format(item.text,\r\n str(item.priority),\r\n str(item.group),\r\n str(item.visible)))\r\n data_file_w.close()\r\n return","def to_XML(self, selected=None, long_output=False):\n\n\t\twith self.lock:\n\t\t\tif selected is None:\n\t\t\t\tusers = self\n\t\t\telse:\n\t\t\t\tusers = selected\n\n\t\t\tassert ltrace(TRACE_USERS, '| to_XML(%r)' % users)\n\n\t\treturn ('<?xml version=\"1.0\" encoding=\"UTF-8\"?>\\n'\n\t\t\t\t\t'<users-list>\\n'\n\t\t\t\t\t'%s\\n'\n\t\t\t\t\t'</users-list>\\n') % '\\n'.join(\n\t\t\t\t\t\tuser.to_XML() for user in users)","def write(self):\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\"UTF-8\")\n with open(self.xml_file, 'w') as f:\n f.write(temp_string)\n # f = open(self.xml_file, \"w\")\n # f.write(temp_string)\n # f.close()","def writeStrListToFile(ldata, filePath, delem=\",\"):\n\twith open(filePath, \"w\") as fh:\n\t\tfor r in ldata:\n\t\t\tif type(r) == list:\n\t\t\t\tr = delem.join(r)\n\t\t\tfh.write(r + \"\\n\")","def save_list(lines, filename):\n data = '\\n'.join(lines)\n file = open(filename, 'w')\n file.write(data)\n file.close()","def export(tako_list, filename):\n for tak in tako_list:\n tak = tak[0]\n l1 = [tak.ident, \"a\"]\n for gen in tak.genome.weightchr_a:\n l1.append(gen.ident)\n l1.append(gen.weight)\n l1.append(gen.mut_rate)\n l1.append(gen.dom)\n f = os.path.join(\"Data\", (filename[:-4] + \" gene data.csv\"))\n with open(f, 'a', newline=\"\") as csvfile:\n writ = csv.writer(csvfile)\n writ.writerow(l1)\n if len(tak.genome.weightchr_b) != 0:\n l2 = [tak.ident, \"b\"]\n for gen in tak.genome.weightchr_b:\n l2.append(gen.ident)\n l2.append(gen.weight)\n l2.append(gen.mut_rate)\n l2.append(gen.dom) \n writ.writerow(l2)"],"string":"[\n \"def write(lst):\\n # TODO\",\n \"def write(self, data, filename):\\n id_ = 1\\n weightlist_el = Element('weight-list')\\n for dataset in data:\\n weight_el = SubElement(weightlist_el, 'weight')\\n id_el = SubElement(weight_el, 'id')\\n id_el.text = str(id_)\\n date_el = SubElement(weight_el, 'date')\\n date_el.text = str(dataset.date) + 'T12:00:00'\\n value_el = SubElement(weight_el, 'value')\\n value_el.text = str(dataset.weight)\\n comment_el = SubElement(weight_el, 'comment')\\n comment_el.text = dataset.note\\n id_ += 1\\n st_tree = ElementTree(weightlist_el)\\n st_tree.write(filename, encoding='UTF-8')\",\n \"def write_list(outputfilename, list):\\r\\n try:\\r\\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\\r\\n itemwriter = csv.writer(outfile, delimiter=\\\",\\\")\\r\\n for item in list:\\r\\n itemwriter.writerow(item)\\r\\n except:\\r\\n input(\\\"File still open! Please close and press enter to continue\\\")\\r\\n with open(outputfilename, 'w', newline='', encoding='utf-8') as outfile:\\r\\n itemwriter = csv.writer(outfile, delimiter=\\\",\\\")\\r\\n for item in list:\\r\\n itemwriter.writerow(item)\",\n \"def write_list_to_file(myList, filename):\\r\\n\\r\\n with open(filename, \\\"w\\\") as outfile:\\r\\n for entries in myList:\\r\\n outfile.write(entries)\\r\\n\\t\\t\\t# add a return after each line\\r\\n outfile.write(\\\"\\\\n\\\")\",\n \"def write_list(args, file_list):\\n if not args.listfile.endswith(\\\".txt\\\"):\\n args.listfile += \\\".txt\\\"\\n outputfile = open(args.listfile, 'w')\\n for name in file_list:\\n outputfile.write(name)\\n outputfile.write(\\\"\\\\n\\\")\\n outputfile.close()\",\n \"def writexml(file):\\n OUTFILE=open(file,\\\"w\\\")\\n doc = xml.dom.minidom.Document()\\n\\n # Create the <dec_reg_list> base element\\n decl_reg_list = doc.createElement(\\\"decl_reg_list\\\")\\n doc.appendChild(decl_reg_list)\\n\\n regname_old=\\\"\\\"\\n rows.pop(0)\\n for row in rows:\\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\\n if regname != regname_old:\\n # Create the register element\\n register = doc.createElement(\\\"register\\\")\\n register.setAttribute(\\\"name\\\", regname)\\n register.setAttribute(\\\"offset\\\", offset)\\n if default != \\\"\\\" : register.setAttribute(\\\"default\\\", default)\\n register.setAttribute(\\\"type\\\", regtype)\\n if expose_reg == \\\"1\\\": register.setAttribute(\\\"usr\\\", expose_reg)\\n if depth != \\\"\\\": register.setAttribute(\\\"size\\\", depth)\\n if incsz != \\\"\\\": register.setAttribute(\\\"incsz\\\", incsz)\\n text = doc.createTextNode(regdesc)\\n register.appendChild(text)\\n decl_reg_list.appendChild(register)\\n \\n # Create the field element\\n if bitname != \\\"\\\":\\n field = doc.createElement(\\\"field\\\")\\n field.setAttribute(\\\"name\\\", bitname)\\n if loc !=\\\"\\\": field.setAttribute(\\\"loc\\\", addcolon(loc))\\n if bittype != \\\"\\\": field.setAttribute(\\\"type\\\", bittype)\\n if bitdesc != \\\"\\\":\\n text = doc.createTextNode(bitdesc)\\n field.appendChild(text)\\n register.appendChild(field)\\n regname_old = regname\\n\\n\\n # Print our newly created XML\\n #print doc.toprettyxml(indent=\\\" \\\")\\n #OUTFILE.write(doc.saveXML(decl_reg_list))\\n OUTFILE.write(doc.toprettyxml(indent=\\\" \\\"))\\n OUTFILE.close()\",\n \"def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\\r\\n if attribute_name in (\\\"IllegalCharacters\\\", \\\"Months\\\"):\\r\\n write_empty = True\\r\\n dictionary = getattr(self, attribute_name)\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n for key in dictionary:\\r\\n if dictionary[key] or write_empty:\\r\\n xmlwriter.WriteStartElement(\\\"Item\\\")\\r\\n xmlwriter.WriteStartAttribute(\\\"Name\\\")\\r\\n xmlwriter.WriteValue(key)\\r\\n xmlwriter.WriteEndAttribute()\\r\\n xmlwriter.WriteStartAttribute(\\\"Value\\\")\\r\\n xmlwriter.WriteValue(dictionary[key])\\r\\n xmlwriter.WriteEndAttribute()\\r\\n xmlwriter.WriteEndElement()\\r\\n xmlwriter.WriteEndElement()\",\n \"def save_list_to_file(the_list, filepath):\\n with open(filepath, 'w') as file_handler:\\n for item in the_list:\\n file_handler.write(\\\"{}\\\\n\\\".format(item))\",\n \"def export_list_to_xacro(list, filename):\\n global robot, OUTPUT\\n doc = Document()\\n root = doc.createElement('robot')\\n doc.appendChild(root)\\n root.setAttribute(\\\"xmlns:xacro\\\", \\\"http://www.ros.org/wiki/xacro\\\")\\n print ('exporting ' + os.path.basename(filename))\\n for string in list:\\n for link in robot.links:\\n if robot.links[link].name.find(string) != -1:\\n root.appendChild(robot.links[link].to_xml(doc))\\n for joint in robot.joints:\\n if robot.joints[joint].child == robot.links[link].name:\\n root.appendChild(robot.joints[joint].to_xml(doc))\\n write_comments_in_xacro(doc, filename)\",\n \"def write_list_to_file(file_name: str, list_name: List[str]):\\n # Write to a file, overwriting the old contents\\n file = open(file_name, 'w')\\n\\n # Loop through the list, append a newline character to each line\\n for item in list_name:\\n file.writelines(item + '\\\\n')\\n\\n # Close the file\\n file.close()\",\n \"def write(file_path, tag_list, attr_name='kMDItemUserTags'):\\n tag_data = ['<string>{}</string>'.format(tag) for tag in tag_list]\\n tag_data.insert(0, ('<!DOCTYPE plist PUBLIC'\\n '\\\"-//Apple//DTD PLIST 1.0//EN\\\"'\\n '\\\"http://www.apple.com/DTDs/PropertyList-1.0.dtd\\\">'\\n '<plist version=\\\"1.0\\\"><array>'))\\n tag_data.append('</array></plist>')\\n tag_text = ''.join(tag_data)\\n\\n xattr = \\\"com.apple.metadata:{}\\\".format(attr_name)\\n # Other attributes you might want to try:\\n # ['kMDItemOMUserTags', 'kOMUserTags',\\n # 'kMDItemkeywords', 'kMDItemFinderComment']\\n cmd = ['xattr',\\n '-w',\\n xattr,\\n tag_text.encode(\\\"utf8\\\"),\\n file_path]\\n return utils.run_process(cmd)\",\n \"def write_list(l, fname):\\n thefile = open(fname, \\\"w\\\")\\n for line in l:\\n thefile.write(\\\"%s\\\\n\\\" % line)\\n thefile.close()\",\n \"def write_list(self, register, data):\\n raise NotImplementedError\",\n \"def write_into_file(name, liste):\\n file = open(name, \\\"w\\\")\\n for item in liste:\\n file.write(item)\\n file.write('\\\\n')\\n file.close()\",\n \"def write_list(self):\\n with open(self.path, 'w') as file:\\n for i in map(self.addziros, range(1, int(str(1) + self.number_length * '0') + 1)):\\n file.write(i + '\\\\n')\\n file.close()\",\n \"def createXMLFile(list, stock_symbol, market):\\n \\n stock = ET.Element(\\\"stock\\\")\\n \\n stock.set(\\\"source\\\", 'yahoo finance')\\n exchange = ET.SubElement(stock, \\\"exchange\\\")\\n exchange.set(\\\"market\\\", market)\\n \\n for s in list: \\n \\n if s.array[0] == 'Date' or list[0].array[0] != 'Date':\\n continue\\n dividend_date = ET.SubElement(exchange, \\\"dividend_date\\\")\\n dividend_date.set(\\\"date\\\", s.array[0])\\n \\n price = ET.SubElement(dividend_date, \\\"price\\\")\\n price.text = s.array[1]\\n \\n \\n indent(stock)\\n tree = ET.ElementTree(stock)\\n \\n tree.write(\\\"dividend_history.xml\\\", xml_declaration=True, encoding='utf-8', method=\\\"xml\\\")\\n print 'xml created for ' + stock_symbol\",\n \"def __writeToFile(self, filePath, lst): \\n \\n if not self.outDir is None: \\n filePath = os.path.join(self.outDir, filePath) \\n \\n open(filePath,'a').writelines(lst)\",\n \"def write_list_to_file(input_list, output_folder, delimiter=\\\" \\\", header=None):\\n with open(output_folder, 'w') as doc_out:\\n if header:\\n doc_out.write(delimiter.join(header) + \\\"\\\\n\\\")\\n for element in input_list:\\n doc_out.write(delimiter.join([str(i) for i in element]) + \\\"\\\\n\\\")\",\n \"def write_to_xml(filename, xmlelement):\\n xmlpretty = prettify(xmlelement)\\n with open(filename, 'w') as f:\\n f.write(xmlpretty)\",\n \"def write_list_to_file(program, list_to_write):\\n with open(program.split('.')[0] + \\\".output.json\\\", 'a+') as output_file:\\n output_file.write(json.dumps(list_to_write, indent=3, sort_keys=False))\",\n \"def write(self):\\r\\n for prop in self.prpnames:\\r\\n elem = SubElement(self._root, prop)\\r\\n data = self.__getattribute__(prop)\\r\\n if self.prpnames[prop]['type'] == \\\"text\\\":\\r\\n elem.text = data\\r\\n elif self.prpnames[prop]['type'] == 'list':\\r\\n for x in data:\\r\\n SubElement(elem, 'regel').text = x\\r\\n elif self.prpnames[prop]['type'] == 'attr':\\r\\n elem.set(self.prpnames[prop]['naam'], data)\\r\\n tree = ElementTree(self._root)\\r\\n tree.write(self._fn)\\r\\n if not self.exists:\\r\\n self.exists = True\",\n \"def toXML( self, indent = '', **kwargs ) :\\n\\n return( '\\\\n'.join( self.toXMLList( **kwargs ) ) )\",\n \"def write_package_list(_, package_list, *args):\\n logger.debug(\\\"Attempting to write package list\\\")\\n try:\\n with open(Base.get_package_list_filepath(), \\\"w\\\") as file:\\n yaml.dump(package_list, file, sort_keys=True)\\n except:\\n logger.exception(\\\"Could not write package list\\\")\\n exit(1)\\n logger.debug(\\\"Packages written to file\\\")\",\n \"def writexml(self, writer, indent=\\\"\\\", add_indent=\\\"\\\", new_line=\\\"\\\"):\\n writer.write(indent + \\\"<\\\" + self.tagName)\\n attrs = self._get_attributes()\\n\\n for a_name in attrs.keys():\\n writer.write(\\\" %s=\\\\\\\"\\\" % a_name)\\n self.write_data(writer, attrs[a_name].value)\\n writer.write(\\\"\\\\\\\"\\\")\\n if self.childNodes:\\n writer.write(\\\">\\\")\\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\\n self.childNodes[0].writexml(writer, '', '', '')\\n else:\\n writer.write(new_line)\\n for node in self.childNodes:\\n node.writexml(writer, indent + add_indent, add_indent, new_line)\\n writer.write(indent)\\n writer.write(\\\"</%s>%s\\\" % (self.tagName, new_line))\\n else:\\n writer.write(\\\"/>%s\\\" % new_line)\",\n \"def write_2D_list(self, list_name, statistics):\\n filename = os.getcwd() + list_name + \\\".csv\\\"\\n print(filename)\\n os.makedirs(os.path.dirname(filename), exist_ok=True)\\n with open(filename, mode='w+', newline='',encoding='utf8') as list_file:\\n list_writer = csv.writer(list_file, delimiter=',', quotechar='\\\"', quoting=csv.QUOTE_MINIMAL)\\n for item in statistics:\\n list_writer.writerow(item)\",\n \"def save_to_xml(self, xwriter):\\r\\n\\r\\n xwriter.WriteStartElement(\\\"Profile\\\")\\r\\n xwriter.WriteAttributeString(\\\"Name\\\", self.Name)\\r\\n xwriter.WriteStartAttribute(\\\"Version\\\")\\r\\n xwriter.WriteValue(self.Version)\\r\\n xwriter.WriteEndAttribute()\\r\\n\\r\\n for var_name in self.__dict__:\\r\\n var_type = type(getattr(self, var_name))\\r\\n\\r\\n if var_type is str and var_name != \\\"Name\\\":\\r\\n self.write_string_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is bool:\\r\\n self.write_bool_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is dict:\\r\\n self.write_dict_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is list and var_name != \\\"ExcludeRules\\\":\\r\\n self.write_list_to_xml(var_name, xwriter)\\r\\n\\r\\n xwriter.WriteStartElement(\\\"ExcludeRules\\\")\\r\\n xwriter.WriteAttributeString(\\\"Operator\\\", self.ExcludeOperator)\\r\\n xwriter.WriteAttributeString(\\\"ExcludeMode\\\", self.ExcludeMode)\\r\\n for rule in self.ExcludeRules:\\r\\n if rule:\\r\\n rule.save_xml(xwriter)\\r\\n xwriter.WriteEndElement()\\r\\n \\r\\n xwriter.WriteEndElement()\",\n \"def write_list_to_file(ls, save_path):\\n # Open in appendation mode given that this function may be called multiple\\n # times on the same file (positive and negative sentiment are in separate\\n # directories).\\n out_file = open(save_path, \\\"w+\\\")\\n for example in ls:\\n out_file.write(example)\\n out_file.write('\\\\n')\",\n \"def writeXml(self):\\n curdir = os.getcwd()\\n os.chdir(self.Imagedir)\\n allImageLists = [self.sciImlist, self.ctxImlist, self.wgtImlist, self.rmsImlist]\\n \\n for imlist in allImageLists:\\n for im in imlist:\\n file = xmlUtil.markupImage(im,dataset=self.obsName)\\n \\n # Don't write these images as output of this module, which\\n # really doesn't have any.\\n \\n #if file not in self.outputList.keys():\\n # self.outputList[file] = [im]\\n \\n os.chdir(curdir)\\n return\",\n \"def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\\r\\n string = getattr(self, attribute_name)\\r\\n if string or write_empty:\\r\\n xmlwriter.WriteElementString(attribute_name, string)\",\n \"def write_bool_to_xml(self, attribute_name, xmlwriter):\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n xmlwriter.WriteValue(getattr(self, attribute_name))\\r\\n xmlwriter.WriteEndElement()\",\n \"def SaveListFile(file,lst):\\n\\tlst = [str(i) +\\\"\\\\n\\\" for i in lst]\\n\\tif len(lst) == 0:\\n\\t\\treturn\\n\\twith open(file,'w') as f:\\n\\t\\tf.writelines(lst)\\n\\treturn lst\",\n \"def write(self, *items):\\n for item in items:\\n if type(item) == list:\\n for _item in item:\\n self.write(_item)\\n elif type(item) == str:\\n self.include(item)\\n else:\\n item.write(self)\",\n \"def write_lis_lis(lis_lis,filename,cols=[]):\\n lis_lis = [[str(l) for l in lis]\\n for lis in lis_lis] # trans every element to str\\n #make all inner lists of the same length\\n inner_lis_max_len = max(len(lis) for lis in lis_lis)\\n lis_lis = [lis + (inner_lis_max_len - len(lis)) * [''] for lis in lis_lis]\\n #make element in the same list have the same length\\n aligned = []\\n for lis in lis_lis:\\n width = max([len(l) for l in lis])\\n lis = [l + (width - len(l)) * ' ' for l in lis]\\n aligned.append(lis)\\n new_lis_lis = [';'.join([aligned[i][j] for i in range(len(aligned))]) for j in range(len(aligned[0]))]\\n with open(filename+'.txt','w') as w_f:\\n if cols:\\n print >> w_f,'\\\\t;'.join(cols)\\n for l in new_lis_lis:\\n print >> w_f,l\",\n \"def write_to(self, fp):\\n fp.write('<')\\n fp.write(self.tag)\\n for k, v in self.attrs.iteritems():\\n fp.write(' ')\\n fp.write(k),\\n fp.write('=\\\"')\\n fp.write(xml_escape(v))\\n fp.write('\\\"')\\n if len(self.contents) == 0:\\n fp.write(' />')\\n else:\\n fp.write('>')\\n for item in self.contents:\\n if isinstance(item, basestring):\\n item = xml_escape(item)\\n fp.write(item)\\n elif isinstance(item, Markup) or isinstance(item, Element):\\n item.write_to(fp)\\n else:\\n raise TypeError('Item %r must be either a string, '\\n '``Element``, or ``Markup``' % item)\\n fp.write('</')\\n fp.write(self.tag)\\n fp.write('>')\",\n \"def persist_list_to_csv(liste, nom_fichier):\\n with open(nom_fichier, 'w') as f:\\n for elem in liste :\\n f.write(\\\"{}\\\\n\\\".format(elem))\",\n \"def write_list(self, data, delimiter=\\\"\\\\n\\\"):\\n if self.check_list_exists(data):\\n with opened_w_error(self.filename, self.lock, \\\"a\\\") as (f, err):\\n if err:\\n logging.error(\\\"File '%s'. Error: %s\\\", self.filename, err)\\n else:\\n f.write(delimiter.join(data))\\n else:\\n logging.error(\\\"Data isn't list or it's not contains elements\\\")\",\n \"def writeAttributes(self, *args):\\n return _libsbml.ASTBasePlugin_writeAttributes(self, *args)\",\n \"def writeAttribute(self, *args):\\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\\n\\n\\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)\",\n \"def dump(self, filename, plist):\\r\\n xml = self.tostring(plist)\\r\\n fd = open(filename, 'wb')\\r\\n try:\\r\\n fd.write(xml)\\r\\n finally:\\r\\n fd.close()\",\n \"def save_output(output_list):\\n wb = Workbook()\\n ws1 = wb.active\\n ws1.title = 'Sheet1'\\n # Create title for columns\\n columns_titles = ['Name', 'Condition description', 'birad[0]','birad[1]','birad[2]','birad[3]','birad[4]','birad[5]','birad[6]','Relevant modalities',\\n 'Relevant findings', 'Unique findings','Additional info',\\n 'Parameters', 50, 30, 10, 5, 1,'Pathogenomonic', 'Negative',\\n 'Ignore', 'Associated conditions', 'Differential diagnosis', 'Notes']\\n ws1.append(columns_titles)\\n # Create list for output file\\n for ol in output_list:\\n for o in ol:\\n cr_list = create_list(o)\\n ws1.append(cr_list)\\n wb.save(filename=OUTPUT_FILE)\",\n \"def get_xml(self):\\n xml = svgwrite.etree.etree.Element(self.elementname)\\n if self.debug:\\n self.validator.check_all_svg_attribute_values(self.elementname, self.attribs)\\n for attribute, value in self.attribs.items():\\n # filter 'None' values\\n if value is not None:\\n value = self.value_to_string(value)\\n if value: # just add not empty attributes\\n xml.set(attribute, value)\\n \\n for element in self.elements:\\n xml.append(element)\\n return xml\",\n \"def write_string_list_to_file(string_list, filename):\\n with open(filename, 'w') as f:\\n for element in string_list:\\n f.write(element+'\\\\n')\",\n \"def write_sequence(list):\\n pass\",\n \"def write_graph_list(name, graph_list, data_root):\\n\\n data_path = os.path.join(data_root, name)\\n if not os.path.exists(data_path):\\n os.makedirs(data_path)\\n\\n # compute right number of trailing zeros for file names\\n format_positions = ceil(log10(len(graph_list)))\\n\\n for i, g in enumerate(graph_list):\\n lines = nx.generate_gml(g)\\n\\n # stupid networkx requires labels to be equal to node ids.\\n # we need to fix this\\n def sanitize_labels(x):\\n def getint(v:str):\\n return int(v.strip('\\\"'))\\n if x.find('label') == -1:\\n return x + '\\\\n'\\n else:\\n v = x[10:]\\n label = g.node[getint(v)]['label']\\n return f' label \\\"{label}\\\"\\\\n'\\n\\n fixed_lines = map(sanitize_labels, lines)\\n\\n f = open(os.path.join(data_path, f'{i:0{format_positions}d}.gml'), 'w')\\n f.writelines(fixed_lines)\\n f.close()\",\n \"def write_to_text_file(directory, filename, data_structure_to_be_written_out):\\n if os.path.exists(directory + filename):\\n print(f\\\"Writing out list to {filename}....\\\\n\\\")\\n with open(directory + filename, \\\"w\\\") as filehandle:\\n if isinstance(data_structure_to_be_written_out, list):\\n filehandle.writelines(\\n f\\\"{item}\\\\n\\\" for item in data_structure_to_be_written_out\\n )\\n elif isinstance(data_structure_to_be_written_out, dict):\\n filehandle.writelines(\\n f\\\"{key} {data_structure_to_be_written_out[key]}\\\\n\\\"\\n for key in data_structure_to_be_written_out.keys()\\n )\",\n \"def toXML(self):\\n return self._xmlpre+\\\"\\\\n\\\".join(map(lambda f:f.toXML(),self._items))+self._xmlpost\",\n \"def xmlWrite(self, xmlWriter, font, value, name, attrs):\\n raise NotImplementedError(self)\",\n \"def save_double_list(list1, list2, filename):\\r\\n the_file = open(filename, \\\"wb\\\")\\r\\n try:\\r\\n writer = csv.writer(the_file)\\r\\n if len(list1)!=len(list2):\\r\\n raise Exception(\\\"Saving a double list : The list have not the same length !\\\")\\r\\n for i in range(len(list1)):\\r\\n writer.writerow( (list1[i], list2[i]) ) \\r\\n finally:\\r\\n the_file.close()\",\n \"def save_list_to_file(content: list, dst_path: str, append=False) -> None:\\n with io.open(file=dst_path, mode=\\\"a\\\" if append else \\\"w\\\", encoding='utf-8') as destination_file:\\n for element in content:\\n destination_file.write(element + \\\"\\\\n\\\")\",\n \"def writeXmlForDET(filename,trigger,listDict,stopInfo):\\n # trigger == 'variable trigger'\\n # Variables == 'variables changed in the branch control logic block'\\n # associated_pb = 'CDF' in case multibranch needs to be performed\\n # stopInfo {'end_time': end simulation time (already stopped), 'end_ts': end time step}\\n root=ET.Element('Branch_info')\\n root.set(\\\"end_time\\\",str(stopInfo['end_time']))\\n if \\\"end_ts\\\" in stopInfo.keys():\\n root.set(\\\"end_ts\\\",str(stopInfo['end_ts']))\\n triggerNode=ET.SubElement(root,\\\"Distribution_trigger\\\")\\n triggerNode.set(\\\"name\\\",trigger)\\n for varInfo in listDict:\\n var=ET.SubElement(triggerNode,'Variable')\\n var.text=varInfo['name']\\n var.set('type',varInfo['type'])\\n var.set('old_value',str(varInfo['old_value']))\\n var.set('actual_value',str(varInfo['new_value']))\\n if 'associated_pb' in varInfo.keys():\\n var.set('probability',str(varInfo['associated_pb']))\\n with open(filename,'w') as fileObject:\\n fileObject.write(minidom.parseString(ET.tostring(root, 'utf-8')).toprettyxml(indent=\\\"\\\\t\\\"))\",\n \"def CSVWriter (iterable, outLoc, header=\\\"\\\", ):\\n if not iterable:\\n print (\\\"nothing to write\\\")\\n return 0\\n\\n out = open(outLoc, 'w')\\n\\n if header:\\n out.write(header+'\\\\n')\\n\\n #Only works if iterable is a nested list\\n for member in iterable:\\n for item in member:\\n out.write(str(item)+',')\\n out.write('\\\\n')\\n\\n print(\\\"write to \\\"+outLoc+\\\" successful.\\\")\\n return 1\",\n \"def __writeElement(fd, elm):\\n\\n print(\\n \\\" <td>\\\",\\n \\\" <h4>\\\" + elm[\\\"name\\\"] + \\\"</h4>\\\",\\n \\\" <ul>\\\",\\n \\\" <li>\\\" + str(elm[\\\"number\\\"]) + \\\"</li>\\\",\\n \\\" <li>\\\" + elm[\\\"small\\\"] + \\\"</li>\\\",\\n \\\" <li>\\\" + str(elm[\\\"molar\\\"]) + \\\"</li>\\\",\\n \\\" </ul>\\\",\\n \\\" </td>\\\",\\n sep=\\\"\\\\n\\\",\\n file=fd\\n )\",\n \"def write_listing(listing, dep, pom):\\n print(f'| {dep.groupId} | {dep.artifactId} | {dep.version} ', end='', file=listing)\\n print(f'| {pom.authors} ', end='', file=listing)\\n print(f'| {pom.license.name} | {pom.license.url} ', end='', file=listing)\\n print(f'|', file=listing)\",\n \"def xmlwrite(self, doc, filename):\\n pathname = os.path.join(self.session.session_dir, filename)\\n f = open(pathname, \\\"w\\\")\\n doc.writexml(writer=f, indent=\\\"\\\", addindent=\\\" \\\", newl=\\\"\\\\n\\\", encoding=\\\"UTF-8\\\")\\n f.close()\",\n \"def write_the_contents_to_the_same_file(self):\\n if not len(self.student_list):\\n print('There is no contents to write')\\n return\\n\\n if self._filename is None:\\n self._filename = self.input_filename()\\n\\n with open(self._filename, 'w') as OUT:\\n OUT.write(self.student_list.to_csv(date_format='%Y-%m-%d',\\n sep='\\\\t', header=False, columns=self.columns_to_save))\\n print(f'Data are saved into {self._filename!r}')\",\n \"def toXML_strList( self, indent = \\\"\\\", **kwargs ) :\\n\\n attrs = self.standardXML_attributes( )\\n return [ '%s<%s path=\\\"%s\\\"%s/>' % ( indent, self.moniker, self.path, attrs ) ]\",\n \"def write_nested_string_list_to_file(string_list, filename):\\n with open(filename, 'w') as f:\\n for i in range(0,len(string_list)):\\n for element in string_list[i]:\\n f.write(element+'\\\\t'+str(i)+'\\\\n')\",\n \"def xml(self, indent):\\n if self.__commentOut:\\n prefix = \\\"<!--\\\"\\n suffix = \\\" -->\\\"\\n else:\\n prefix = \\\"\\\"\\n suffix = \\\"\\\"\\n hubs = self.__stringMap.keys()\\n if self.OMIT_HUB_NUMBER or len(hubs) != 1:\\n nStr = \\\"\\\"\\n else:\\n nStr = \\\" hub=\\\\\\\"%d\\\\\\\"\\\" % hubs[0]\\n return \\\"%s%s<domConfigList%s>%s</domConfigList>%s\\\" % \\\\\\n (prefix, indent, nStr, self.__fileName, suffix)\",\n \"def writeToTempXml(self):\\n name = self.fileToProcess.name\\n all_tokens = ET.Element(\\\"tokens\\\")\\n for token in self.tokensTable:\\n if token.getType() == KEYWORD:\\n keyword = ET.SubElement(all_tokens, \\\"keyword\\\")\\n keyword.text = ' '+token.getValue()+' '\\n elif token.getType() == IDENTIFIER:\\n identifier = ET.SubElement(all_tokens, \\\"identifier\\\")\\n identifier.text = ' '+token.getValue()+' '\\n elif token.getType() == SYMBOL:\\n symbol = ET.SubElement(all_tokens, \\\"symbol\\\")\\n symbol.text = ' '+token.getValue()+' '\\n elif token.getType() == STRING_CONST:\\n stringConstant = ET.SubElement(all_tokens, \\\"stringConstant\\\")\\n stringConstant.text = ' '+token.getValue()+' '\\n elif token.getType() == INT_CONST:\\n integerConstant = ET.SubElement(all_tokens, \\\"integerConstant\\\")\\n integerConstant.text = ' '+token.getValue()+' '\\n tree = ET.ElementTree(all_tokens)\\n tree.write(name + 'T' + '.xml')\",\n \"def output_to_file(utilist, filepath=\\\"demo.csv\\\"):\\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\\n with open(filepath, \\\"a\\\") as f:\\n f.write(utilist + \\\"\\\\n\\\")\",\n \"def write(self):\\n o = []\\n print self.comment\\n o.append('% ' + re.sub(\\\"\\\\n\\\", \\\"\\\\n% \\\", self.comment))\\n o.append(\\\"@relation \\\" + self.esc(self.relation))\\n for a in self.attributes:\\n at = self.attribute_types[a]\\n if at == 'numeric':\\n o.append(\\\"@attribute \\\" + self.esc(a) + \\\" numeric\\\")\\n elif at == 'string':\\n o.append(\\\"@attribute \\\" + self.esc(a) + \\\" string\\\")\\n elif at == 'nominal':\\n o.append(\\\"@attribute \\\" + self.esc(a) +\\n \\\" {\\\" + ','.join(self.attribute_data[a]) + \\\"}\\\")\\n else:\\n raise \\\"Type \\\" + at + \\\" not supported for writing!\\\"\\n o.append(\\\"\\\\n@data\\\")\\n for d in self.data:\\n line = []\\n for e, a in zip(d, self.attributes):\\n at = self.attribute_types[a]\\n if at == 'numeric':\\n line.append(str(e))\\n elif at == 'string':\\n line.append(self.esc(e))\\n elif at == 'nominal':\\n line.append(e)\\n else:\\n raise \\\"Type \\\" + at + \\\" not supported for writing!\\\"\\n o.append(','.join(line))\\n return \\\"\\\\n\\\".join(o) + \\\"\\\\n\\\"\",\n \"def compile_parameter_list(self):\\n\\n\\t\\txml = self.tokenizer.keyword() + self.tokenizer.identifier()\\n\\n\\t\\tself.outfile.write(xml)\\n\\n\\t\\tif self.tokenizer.get_token() == ',':\\n\\t\\t\\tself.outfile.write(self.tokenizer.symbol())\\n\\t\\t\\tself.compile_parameter_list()\",\n \"def debugDumpAttrList(self, output, depth):\\n libxml2mod.xmlDebugDumpAttrList(output, self._o, depth)\",\n \"def writeAttributes(self, *args):\\n return _libsbml.MultiASTPlugin_writeAttributes(self, *args)\",\n \"def list_to_file(l, file_name):\\r\\n fw = open(file_name, 'w', encoding = 'utf-8')\\r\\n fw.write('\\\\n'.join(l))\\r\\n fw.close()\",\n \"def write_xml(self, xmlfile):\\n system.xml.write_file(xmlfile, self.status, 'status')\",\n \"def writeToFile(self, outfile):\\n self.finalizePlot()\\n\\n kk = self.mElements.keys()\\n kk.sort()\\n kk.reverse()\\n\\n ## make sure the image size is ok\\n min_x, min_y, max_x, max_y = 0, 0, 0, 0\\n \\n for k in kk:\\n for e in self.mElements[k]:\\n for x in ('x', 'x2', 'x1'):\\n if x in e.attributes:\\n v = e.attributes[x] \\n min_x = min(min_x, v )\\n max_x = max(max_x, v )\\n for y in ('y', 'y2', 'y1'):\\n if y in e.attributes:\\n v = e.attributes[y]\\n min_y = min(min_y, v )\\n max_y = max(max_y, v )\\n\\n min_x, min_y = int(math.floor(min_x)), int(math.floor(min_y))\\n max_x, max_y = int(math.floor(max_x)), int(math.floor(max_y)) \\n\\n for k in kk:\\n for e in self.mElements[k]:\\n for x in ('x', 'x2', 'x1'):\\n if x in e.attributes:\\n e.attributes[x] -= min_x\\n for x in ('y', 'y2', 'y1'):\\n if y in e.attributes:\\n e.attributes[y] -= min_y\\n\\n ## now add all the elements\\n self.mRoot = SVGdraw.drawing()\\n self.mDraw = SVGdraw.svg( (0, 0, self.mPageWidth - min_x, self.mPageHeight - min_y ) , \\\"100%\\\", \\\"100%\\\" )\\n \\n for k in kk:\\n for e in self.mElements[k]:\\n self.mDraw.addElement( e )\\n \\n self.mRoot.setSVG(self.mDraw)\\n\\n tfile = tempfile.mktemp()\\n \\n self.mRoot.toXml( tfile )\\n\\n lines = open(tfile,\\\"r\\\").readlines()\\n \\n outfile.write(string.join(lines,\\\"\\\"))\\n outfile.write(\\\"\\\\n\\\")\\n \\n os.remove(tfile)\",\n \"def write_config(filename, data):\\n\\n # Encode data\\n desiredgraphs = ET.Element('desiredgraphs')\\n\\n for graph in data:\\n curr_graph = ET.SubElement(desiredgraphs, 'graph', {key: value for key, value in graph.items() if not isinstance(value, list) and value})\\n for key, lst in [(key, value) for key, value in graph.items() if isinstance(value, list) and value]:\\n for item in lst:\\n ET.SubElement(curr_graph, key, {key: value for key, value in item.items() if value})\\n\\n # Write\\n with open(filename, 'w') as g:\\n g.write(xml_to_string(desiredgraphs))\",\n \"def saveToXml(self) -> org.jdom.Element:\\n ...\",\n \"def write_feed(self, elements, context, filename=None, feed_type='atom'):\\n old_locale = locale.setlocale(locale.LC_ALL)\\n locale.setlocale(locale.LC_ALL, 'C')\\n try:\\n self.site_url = context.get('SITEURL', get_relative_path(filename))\\n self.feed_url= '%s/%s' % (self.site_url, filename)\\n\\n feed = self._create_new_feed(feed_type, context)\\n\\n for item in elements:\\n self._add_item_to_the_feed(feed, item)\\n\\n if filename:\\n complete_path = os.path.join(self.output_path, filename)\\n try:\\n os.makedirs(os.path.dirname(complete_path))\\n except Exception:\\n pass\\n fp = open(complete_path, 'w')\\n feed.write(fp, 'utf-8')\\n print u' [ok] writing %s' % complete_path\\n\\n fp.close()\\n return feed\\n finally:\\n locale.setlocale(locale.LC_ALL, old_locale)\",\n \"def writeList2File(filename, array, overwrite=False, separator=';'):\\n mode = 'a'\\n if overwrite:\\n mode = 'w'\\n file = open(filename, mode)\\n file.write(separator.join(map(str,array)) + '\\\\n')\",\n \"def write_xml(self, filepath=None, escapeNewline=True, indent=False):\\n\\n if not filepath:\\n filepath = self.mFilePath\\n\\n if indent:\\n self.indent(self.tree.getroot())\\n\\n output = StringIO()\\n\\n self.tree.write(output, encoding=\\\"UTF-8\\\")\\n\\n outFile = open(filepath, \\\"w\\\")\\n if escapeNewline:\\n # we need to make sure newline 
 is written correctly\\n print >> outFile, re.sub(\\\"###newline_escape###\\\", \\\"
\\\", output.getvalue())\\n else:\\n print >> outFile, output.getvalue()\\n\\n outFile.close\",\n \"def test_save_list_to_file(self):\\n task_list = TaskList()\\n task1 = Task()\\n output_file_path = self.testing_files[0]\\n task1.set_name('Test Task One')\\n task1.set_minutes(30)\\n task1.set_notes('This is a great test task')\\n task_list.add_task(task1)\\n\\n self.assert_equal(True, task_list.save_to_file(output_file_path))\\n self.assert_equal(True, os.path.isfile(output_file_path))\",\n \"def write_list_file(output_file, clip_list_arr):\\n list_file = output_file+'_clip_list.txt'\\n print \\\"list_file: \\\", list_file\\n f = open(list_file, 'w')\\n for clip in clip_list_arr:\\n line = 'file '+clip\\n f.write(\\\"%s\\\\n\\\" % line)\\n # Add in a divider movie between clips? (it could go here)\\n f.close()\\n # print 'list_file', list_file\\n # print clip_list_arr\\n\\n return list_file\",\n \"def list_to_file(itemlist, filename):\\n # Create dir if needed\\n dir_path = os.path.dirname(filename)\\n if not os.path.isdir(dir_path):\\n os.makedirs(dir_path)\\n\\n # Delete existing file\\n if os.path.exists(filename):\\n os.remove(filename)\\n\\n # itemlist.sort()\\n\\n # Write new file\\n with open(filename, 'w') as f:\\n fname = os.path.basename(filename)\\n if 'local' in fname:\\n f.write('# Local development dependencies go here\\\\n-r base.txt\\\\n\\\\n')\\n if 'production' in fname:\\n f.write('# Pro-tip: Try not to put anything here. Avoid dependencies in production that aren\\\\'t in development.\\\\n-r base.txt\\\\n\\\\n')\\n if 'test' in fname:\\n f.write('# Test dependencies go here.\\\\n-r base.txt\\\\n\\\\n')\\n if 'subdependencies' in fname:\\n f.write('# Sub-dependencies (i.e. most likely dependencies of top level dependencies).\\\\n-r base.txt\\\\n\\\\n')\\n for item in itemlist:\\n f.write('%s\\\\n' % item)\",\n \"def writeXmlOutput(self, output, prettyname):\\n\\n # make an alias\\n w = output.write\\n\\n # bumf that says file was autogenerated\\n self.writeHeaderOutput((\\\"<!--\\\",\\\"-->\\\"), output, prettyname)\\n\\n # build the first table\\n w('<Chapter anchor=\\\"Summary.0\\\">\\\\n')\\n w(' <Title>Summary</Title>\\\\n')\\n w(' <Description anchor=\\\"Summary.1\\\">\\\\n')\\n w(xmlTableStart(\\\"SymbolList\\\", 3, [\\\"Symbol\\\", \\\"Type\\\", \\\"Description\\\"]))\\n\\n for d in self.data:\\n # now just print it out\\n line = d.buildSimpleXmlDeclaration()\\n if len(line) != 0:\\n w(line+\\\"\\\\n\\\\n\\\")\\n\\n w(xmlTableEnd('Symbol list'))\\n w(' </Description>\\\\n')\\n w('</Chapter>\\\\n\\\\n\\\\n')\\n\\n w('<Chapter anchor=\\\"Baton.0\\\">\\\\n')\\n w(' <Title>Baton</Title>\\\\n')\\n w(' <Description anchor=\\\"Baton.1\\\">\\\\n')\\n w(' </Description>\\\\n')\\n for d in self.data:\\n # now just print it out\\n line = d.buildFullXmlDeclaration(1)\\n if len(line) != 0:\\n output.write(line+\\\"\\\\n\\\\n\\\")\\n\\n w('</Chapter>\\\\n\\\\n\\\\n')\",\n \"def write_csv(fname, olist):\\n ofile = open(fname, \\\"wb\\\")\\n writer = csv.writer(ofile, delimiter=',', quotechar='\\\"',\\n quoting=csv.QUOTE_ALL)\\n writer.writerows(olist)\",\n \"def setListDoc(self, list):\\n if list is None: list__o = None\\n else: list__o = list._o\\n libxml2mod.xmlSetListDoc(list__o, self._o)\",\n \"def writeCSV(path,aList):\\n\\twith open(path,'wb') as w:\\n\\t\\ta = csv.writer(w, delimiter = ',')\\n\\t\\ta.writerows(aList)\\n\\tw.close()\",\n \"def toXMLList( self, indent = '', **kwargs ) :\\n\\n indent2 = indent + kwargs.get( 'incrementalIndent', ' ' )\\n\\n attributesStr = baseModule.xDataFunctional.attributesToXMLAttributeStr( self )\\n if( None in self.fixedDomain( ) ) : attributesStr += ' domainMin=\\\"%s\\\" domainMax=\\\"%s\\\"' % ( self.domainMin, self.domainMax )\\n if( self.lowerIndex != 0 ) : attributesStr += ' lowerIndex=\\\"%s\\\"' % self.lowerIndex\\n\\n # FIXME: converting self.coefficients to values for printing. Should it be stored as values in the first place?\\n coefs = valuesModule.values( self.coefficients, valueType = self.valueType, sep = self.__sep )\\n\\n XMLList = [ '%s<%s%s>' % ( indent, self.moniker, attributesStr ) ]\\n if( self.isPrimaryXData( ) and ( self.axes is not None ) ) :\\n XMLList += self.axes.toXMLList( indent2, **kwargs )\\n XMLList += coefs.toXMLList( indent2, **kwargs )\\n XMLList[-1] += '</%s>' % self.moniker\\n return XMLList\\n else:\\n XMLList += coefs.toXMLList( '', **kwargs )\\n XMLList[-1] += '</%s>' % self.moniker\\n return( [ ''.join( XMLList ) ] )\",\n \"def writeXMLDecl(self):\\n return _libsbml.XMLOutputStream_writeXMLDecl(self)\",\n \"def to_xml(self, f=None):\\r\\n if not f:\\r\\n from tempfile import TemporaryFile\\r\\n f = TemporaryFile()\\r\\n print >> f, '<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>'\\r\\n print >> f, '<Domain id=\\\"%s\\\">' % self.name\\r\\n for item in self:\\r\\n print >> f, '\\\\t<Item id=\\\"%s\\\">' % item.name\\r\\n for k in item:\\r\\n print >> f, '\\\\t\\\\t<attribute id=\\\"%s\\\">' % k\\r\\n values = item[k]\\r\\n if not isinstance(values, list):\\r\\n values = [values]\\r\\n for value in values:\\r\\n print >> f, '\\\\t\\\\t\\\\t<value><![CDATA[',\\r\\n if isinstance(value, unicode):\\r\\n value = value.encode('utf-8', 'replace')\\r\\n else:\\r\\n value = unicode(value, errors='replace').encode('utf-8', 'replace')\\r\\n f.write(value)\\r\\n print >> f, ']]></value>'\\r\\n print >> f, '\\\\t\\\\t</attribute>'\\r\\n print >> f, '\\\\t</Item>'\\r\\n print >> f, '</Domain>'\\r\\n f.flush()\\r\\n f.seek(0)\\r\\n return f\",\n \"def write_to_xml(dictData, metadata, xmlfile):\\n\\tfout = codecs.open(xmlfile, 'w', 'utf-8')\\n\\tfout.write('<?xml version = \\\"1.0\\\" encoding = \\\"UTF-8\\\" standalone = \\\"no\\\" ?>\\\\n')\\n\\tfout.write('<?xml-stylesheet type=\\\"text/xsl\\\" href=\\\"maketable.xsl\\\"?>\\\\n')\\n\\tfout.write('<root>\\\\n')\\n\\tfout.write('<meta>\\\\n')\\n\\tfor key, value in metadata.items():\\n\\t\\tfout.write('<' + key + '>' + value + '</' + key + '>\\\\n')\\n\\tfout.write('</meta>\\\\n')\\n\\tfout.write('<content>\\\\n')\\n\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\\n\\t\\txmlline = ''\\n\\t\\txmlline += '<word><headword>' + hw + '</headword><meanings>'\\n\\t\\tfor meaning in meanings:\\n\\t\\t\\txmlline += '<m>' + meaning + '</m>'\\n\\t\\txmlline += '</meanings>'\\n\\t\\txmlline += '<verse>'\\n\\t\\tlines = verse.split('<BR>')\\n\\t\\tfor line in lines:\\n\\t\\t\\txmlline += '<line>' + line + '</line>'\\n\\t\\txmlline += '</verse>'\\n\\t\\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\\n\\t\\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\\n\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\tfout.write(xmlline + '\\\\n')\\n\\t\\txmlline = ''\\n\\tfout.write('</content>\\\\n</root>')\\n\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('XML file generated. Success!')\\n\\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))\",\n \"def writeExtensionElements(self, *args):\\n return _libsbml.SBase_writeExtensionElements(self, *args)\",\n \"def savealist(alist, filename):\\n out = open(filename, \\\"w\\\")\\n for i in alist:\\n out.write(str(i) + \\\"\\\\n\\\") # if i is numeric\\n out.close()\",\n \"def save_lists_to_file(filename, elev_list, dist_list):\\n import numpy as np\\n\\n np.save(file=filename,arr=np.array([elev_list, dist_list]))\",\n \"def htmlList(contents, attr='', listType='ul'):\\n return '<%s%s>\\\\n%s</%s>\\\\n' % (listType,sep(attr),contents,listType)\",\n \"def create_xml_regression(lfiles, lsbj, foxml):\\n\\n impl = xml.dom.minidom.getDOMImplementation()\\n doc = impl.createDocument(None, \\\"some_tag\\\", None)\\n top_element = doc.documentElement\\n\\n e = doc.createElement('subject')\\n e.setAttribute('id', 'case')\\n\\n for i, fn in enumerate(lfiles):\\n v = doc.createElement('visit')\\n v.setAttribute('id', \\\"subj{}\\\".format(i))\\n\\n f = doc.createElement('filename')\\n f.setAttribute('object_id', \\\"face\\\")\\n t = doc.createTextNode(fn)\\n f.appendChild(t)\\n\\n a = doc.createElement('age')\\n x = doc.createTextNode(str(lsbj[i][\\\"age\\\"]))\\n a.appendChild(x)\\n\\n\\n v.appendChild(f)\\n v.appendChild(a)\\n e.appendChild(v)\\n\\n top_element.appendChild(e)\\n\\n with open(foxml, \\\"w\\\") as fo:\\n fo.write(doc.toprettyxml())\",\n \"def write_csv(list_file, path):\\n\\n\\twith open(path, 'w') as f:\\n\\t\\twriter = csv.writer(f, delimiter=',')\\n\\t\\tfor i in list_file:\\n\\t\\t\\twriter.writerow(i)\",\n \"def writeItemsToCSV(self, fileName, itemList):\\n\\t\\twith open(fileName, 'w') as csvFile:\\n\\t\\t csvWriter = csv.writer(csvFile, delimiter=',')\\n\\t\\t # Column titles\\n\\t\\t csvWriter.writerow([\\\"Brandname\\\",\\\"Productname\\\",\\\"Colors\\\",\\\"Sizes\\\",\\\"Description\\\",\\\"Materials\\\",\\\"Maintenance\\\",\\\"RegularPrice\\\",\\\"CurrentPrice\\\"])\\n\\t\\t for item in itemList:\\n\\t\\t csvWriter.writerow(list(item))\",\n \"def write(xml, outf):\\n my_dom = dom.parseString(tostring(xml))\\n f = open(outf, 'w')\\n PrettyPrint(my_dom, f, encoding='utf-8')\\n f.close()\",\n \"def __att_to_xml(self, doc, parent, att_name, att):\\r\\n \\r\\n if att == None:\\r\\n e=doc.createElementNS(None, att_name)\\r\\n parent.appendChild(e)\\r\\n e.appendChild(doc.createTextNode(''))\\r\\n elif type(att) == str or type(att) == unicode or type(att) == int:\\r\\n e=doc.createElementNS(None, att_name)\\r\\n parent.appendChild(e)\\r\\n e.appendChild(doc.createTextNode('%s' % att))\\r\\n #e.setAttributeNS(None, \\\"class\\\", \\\"sample\\\")\\r\\n elif hasattr(att, 'to_xml'):\\r\\n # Looks like an entity object, so call it's to_xml and append it\\r\\n child_frag = att.to_xml(doc)\\r\\n parent.appendChild(child_frag)\\r\\n elif type(att) == list or type(att) == tuple:\\r\\n # Looks like an ordered collection\\r\\n col = doc.createElementNS(None, att_name)\\r\\n parent.appendChild(col)\\r\\n for item in att:\\r\\n self.__att_to_xml(doc, col, att_name, item)\\r\\n elif isinstance(att, dict):\\r\\n # Looks like a dictionary\\r\\n col = doc.createElementNS(None, att_name)\\r\\n parent.appendChild(col)\\r\\n att_keys = att.keys()\\r\\n att_keys.sort()\\r\\n for item in att_keys:\\r\\n self.__att_to_xml(doc, col, item, att[item])\\r\\n else:\\r\\n # Not sure, so treat it like a string\\r\\n e=doc.createElementNS(None, att_name)\\r\\n parent.appendChild(e)\\r\\n e.appendChild(doc.createTextNode('%s' % att))\",\n \"def XMLWrite(one, two, three, four, five, six, seven, eight):\\n filePath = \\\"/mnt/RAM/kanban.xml\\\"\\n xmlFile = open(filePath, 'w')\\n\\n xmlFile.write('<kanbanShelf>\\\\n')\\n xmlFile.write(' <one>%s</one>\\\\n' % one)\\n xmlFile.write(' <two>%s</two>\\\\n' % two)\\n xmlFile.write(' <three>%s</three>\\\\n' % three)\\n xmlFile.write(' <four>%s</four>\\\\n' % four)\\n xmlFile.write(' <five>%s</five>\\\\n' % five)\\n xmlFile.write(' <six>%s</six>\\\\n' % six)\\n xmlFile.write(' <seven>%s</seven>\\\\n' % seven)\\n xmlFile.write(' <eight>%s</eight>\\\\n' % eight)\\n xmlFile.write('</kanbanShelf>')\",\n \"def write_manifests( file_lists, target_dir, output_dir ):\\n for i, lst in enumerate( file_lists ):\\n with open( os.path.join( output_dir, \\\"manifest-{}.txt\\\".format( i ) ), \\\"w\\\" ) as fout:\\n for r in lst:\\n fout.write( insert_rsync_marker( r, target_dir ) + \\\"\\\\n\\\" )\",\n \"def save_list(todo_list, save_location):\\r\\n data_file_w = open(save_location,\\r\\n \\\"w\\\") # open the save file and clear the data from it\\r\\n data_file_w.write(\\\"Warning: The Todo-List Program will not be able to \\\"\\r\\n \\\"load this save file if it is incorrectly modified. \\\"\\r\\n \\\"Modify at your own risk. The structure is Entry \\\"\\r\\n \\\"Text, Entry Priority as a number, Entry Group as a \\\"\\r\\n \\\"number (Not Yet Utilized, but necessary), and Entry \\\"\\r\\n \\\"Visibility as a boolean, each on a separate line, a \\\"\\r\\n \\\"single line gap in between, and the \\\"\\r\\n \\\"very first line is skipped\\\\n\\\")\\r\\n for item in todo_list:\\r\\n data_file_w.write(\\\"{0}\\\\n{1}\\\\n{2}\\\\n{3}\\\\n\\\\n\\\".format(item.text,\\r\\n str(item.priority),\\r\\n str(item.group),\\r\\n str(item.visible)))\\r\\n data_file_w.close()\\r\\n return\",\n \"def to_XML(self, selected=None, long_output=False):\\n\\n\\t\\twith self.lock:\\n\\t\\t\\tif selected is None:\\n\\t\\t\\t\\tusers = self\\n\\t\\t\\telse:\\n\\t\\t\\t\\tusers = selected\\n\\n\\t\\t\\tassert ltrace(TRACE_USERS, '| to_XML(%r)' % users)\\n\\n\\t\\treturn ('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\\\n'\\n\\t\\t\\t\\t\\t'<users-list>\\\\n'\\n\\t\\t\\t\\t\\t'%s\\\\n'\\n\\t\\t\\t\\t\\t'</users-list>\\\\n') % '\\\\n'.join(\\n\\t\\t\\t\\t\\t\\tuser.to_XML() for user in users)\",\n \"def write(self):\\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\\\"UTF-8\\\")\\n with open(self.xml_file, 'w') as f:\\n f.write(temp_string)\\n # f = open(self.xml_file, \\\"w\\\")\\n # f.write(temp_string)\\n # f.close()\",\n \"def writeStrListToFile(ldata, filePath, delem=\\\",\\\"):\\n\\twith open(filePath, \\\"w\\\") as fh:\\n\\t\\tfor r in ldata:\\n\\t\\t\\tif type(r) == list:\\n\\t\\t\\t\\tr = delem.join(r)\\n\\t\\t\\tfh.write(r + \\\"\\\\n\\\")\",\n \"def save_list(lines, filename):\\n data = '\\\\n'.join(lines)\\n file = open(filename, 'w')\\n file.write(data)\\n file.close()\",\n \"def export(tako_list, filename):\\n for tak in tako_list:\\n tak = tak[0]\\n l1 = [tak.ident, \\\"a\\\"]\\n for gen in tak.genome.weightchr_a:\\n l1.append(gen.ident)\\n l1.append(gen.weight)\\n l1.append(gen.mut_rate)\\n l1.append(gen.dom)\\n f = os.path.join(\\\"Data\\\", (filename[:-4] + \\\" gene data.csv\\\"))\\n with open(f, 'a', newline=\\\"\\\") as csvfile:\\n writ = csv.writer(csvfile)\\n writ.writerow(l1)\\n if len(tak.genome.weightchr_b) != 0:\\n l2 = [tak.ident, \\\"b\\\"]\\n for gen in tak.genome.weightchr_b:\\n l2.append(gen.ident)\\n l2.append(gen.weight)\\n l2.append(gen.mut_rate)\\n l2.append(gen.dom) \\n writ.writerow(l2)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6497926","0.6048247","0.60110986","0.5987103","0.59648246","0.5957363","0.5939886","0.57804435","0.5734696","0.57158196","0.5674186","0.5656518","0.56491786","0.55900544","0.55637085","0.55263245","0.546913","0.54400545","0.54337585","0.54084957","0.53953457","0.5391533","0.5352481","0.5325219","0.53213125","0.5320877","0.5298301","0.52947754","0.5280493","0.5278833","0.5250389","0.5248374","0.52288836","0.5214739","0.5205457","0.5185285","0.51837546","0.5170067","0.516368","0.5159655","0.5156011","0.5155446","0.51532406","0.5145831","0.51378834","0.5128459","0.5117289","0.5113759","0.51103735","0.5097552","0.5091781","0.50781804","0.50736237","0.50663584","0.50594646","0.5046566","0.5039886","0.5025295","0.5015735","0.50059867","0.5001954","0.4996435","0.49939367","0.4990322","0.4988934","0.4975747","0.4975348","0.49651277","0.49420357","0.4938541","0.49359873","0.49241358","0.492345","0.49216706","0.49184042","0.49162897","0.49048707","0.49047685","0.49033016","0.49018264","0.4897876","0.48967677","0.48952812","0.48845053","0.48842004","0.4881048","0.48766664","0.4874924","0.48604548","0.4857586","0.48559865","0.485559","0.48509392","0.48482922","0.48414496","0.4839563","0.4835092","0.48198482","0.48194185","0.48156157"],"string":"[\n \"0.6497926\",\n \"0.6048247\",\n \"0.60110986\",\n \"0.5987103\",\n \"0.59648246\",\n \"0.5957363\",\n \"0.5939886\",\n \"0.57804435\",\n \"0.5734696\",\n \"0.57158196\",\n \"0.5674186\",\n \"0.5656518\",\n \"0.56491786\",\n \"0.55900544\",\n \"0.55637085\",\n \"0.55263245\",\n \"0.546913\",\n \"0.54400545\",\n \"0.54337585\",\n \"0.54084957\",\n \"0.53953457\",\n \"0.5391533\",\n \"0.5352481\",\n \"0.5325219\",\n \"0.53213125\",\n \"0.5320877\",\n \"0.5298301\",\n \"0.52947754\",\n \"0.5280493\",\n \"0.5278833\",\n \"0.5250389\",\n \"0.5248374\",\n \"0.52288836\",\n \"0.5214739\",\n \"0.5205457\",\n \"0.5185285\",\n \"0.51837546\",\n \"0.5170067\",\n \"0.516368\",\n \"0.5159655\",\n \"0.5156011\",\n \"0.5155446\",\n \"0.51532406\",\n \"0.5145831\",\n \"0.51378834\",\n \"0.5128459\",\n \"0.5117289\",\n \"0.5113759\",\n \"0.51103735\",\n \"0.5097552\",\n \"0.5091781\",\n \"0.50781804\",\n \"0.50736237\",\n \"0.50663584\",\n \"0.50594646\",\n \"0.5046566\",\n \"0.5039886\",\n \"0.5025295\",\n \"0.5015735\",\n \"0.50059867\",\n \"0.5001954\",\n \"0.4996435\",\n \"0.49939367\",\n \"0.4990322\",\n \"0.4988934\",\n \"0.4975747\",\n \"0.4975348\",\n \"0.49651277\",\n \"0.49420357\",\n \"0.4938541\",\n \"0.49359873\",\n \"0.49241358\",\n \"0.492345\",\n \"0.49216706\",\n \"0.49184042\",\n \"0.49162897\",\n \"0.49048707\",\n \"0.49047685\",\n \"0.49033016\",\n \"0.49018264\",\n \"0.4897876\",\n \"0.48967677\",\n \"0.48952812\",\n \"0.48845053\",\n \"0.48842004\",\n \"0.4881048\",\n \"0.48766664\",\n \"0.4874924\",\n \"0.48604548\",\n \"0.4857586\",\n \"0.48559865\",\n \"0.485559\",\n \"0.48509392\",\n \"0.48482922\",\n \"0.48414496\",\n \"0.4839563\",\n \"0.4835092\",\n \"0.48198482\",\n \"0.48194185\",\n \"0.48156157\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8491795"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11586,"cells":{"query":{"kind":"string","value":"Writes a string to an xml file in the form of string attribute_name>The name of the string attribute to write. xmlwriter>The xml writer to write with. write_empty>A bool of whether to write empty strings to the xml file. Default is write empty strings."},"document":{"kind":"string","value":"def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\r\n string = getattr(self, attribute_name)\r\n if string or write_empty:\r\n xmlwriter.WriteElementString(attribute_name, string)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_write_string():\n buf = make_buffer()\n writer = XmlWriter(buf)\n writer.write_element('value', 'myvalue')\n writer.flush()\n assert_equals(decode_buffer(buf), '<value>myvalue</value>')","def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n if attribute_name in (\"IllegalCharacters\", \"Months\"):\r\n write_empty = True\r\n dictionary = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for key in dictionary:\r\n if dictionary[key] or write_empty:\r\n xmlwriter.WriteStartElement(\"Item\")\r\n xmlwriter.WriteStartAttribute(\"Name\")\r\n xmlwriter.WriteValue(key)\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteStartAttribute(\"Value\")\r\n xmlwriter.WriteValue(dictionary[key])\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteEndElement()\r\n xmlwriter.WriteEndElement()","def write(self, str: str, /) -> None:","def write_output_file(self, xml_text, xml_file):\n xml_fo = open(xml_file, 'w')\n xml_fo.write(xml_text+'</xml>')\n xml_fo.close()\n return","def write(self):\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\"UTF-8\")\n with open(self.xml_file, 'w') as f:\n f.write(temp_string)\n # f = open(self.xml_file, \"w\")\n # f.write(temp_string)\n # f.close()","def write_to_xml(filename, xmlelement):\n xmlpretty = prettify(xmlelement)\n with open(filename, 'w') as f:\n f.write(xmlpretty)","def xmlWrite(self, xmlWriter, font, value, name, attrs):\n raise NotImplementedError(self)","def write_xml(self, filepath=None, escapeNewline=True, indent=False):\n\n if not filepath:\n filepath = self.mFilePath\n\n if indent:\n self.indent(self.tree.getroot())\n\n output = StringIO()\n\n self.tree.write(output, encoding=\"UTF-8\")\n\n outFile = open(filepath, \"w\")\n if escapeNewline:\n # we need to make sure newline 
 is written correctly\n print >> outFile, re.sub(\"###newline_escape###\", \"
\", output.getvalue())\n else:\n print >> outFile, output.getvalue()\n\n outFile.close","def writeString(self, str):\n ret = libxml2mod.xmlOutputBufferWriteString(self._o, str)\n return ret","def writeFile(self, filename):\n s = ET.tostring(self._root)\n\n #Remove all formatting\n s = s.replace('\\n','')\n s = s.replace('\\t','')\n s = s.replace('\\r','')\n\n f = open(filename, 'w')\n f.write(minidom.parseString(s).toprettyxml())\n f.close()","def writetree(self, string):\n self.treebuf.write(string)\n if self.animal != None:\n self.animal.writetree(string)","def exportXml ( w, xml ):\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n rawText = xml\n pattern = re.compile (r'[^\\S ]+')\n text = re.sub ( pattern, \"\", rawText )\n reparsed = MD.parseString ( text )\n w.write ( reparsed.toprettyxml ( indent = \"\\t\", encoding = \"UTF-8\" ) )","def write_file(name_file, string):\n with open(name_file, 'w') as file:\n file.write(string)","def test_write_defaults(self):\n xml = (\n u'<?xml version=\"1.0\" encoding=\"utf-8\"?>'\n u'<DocRoot>'\n u'<Elem1>默认جذ</Elem1>'\n u'<Elem2/>'\n u'</DocRoot>'\n )\n io_string = six.StringIO()\n self.builder.write_doc(io_string)\n if six.PY2:\n self.assertEqual(xml.encode('utf-8'), io_string.getvalue())\n else:\n self.assertEqual(xml, io_string.getvalue())","def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n attribute_list = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for item in attribute_list:\r\n if item or write_empty:\r\n xmlwriter.WriteElementString(\"Item\", item)\r\n xmlwriter.WriteEndElement()","def write_bool_to_xml(self, attribute_name, xmlwriter):\r\n xmlwriter.WriteStartElement(attribute_name)\r\n xmlwriter.WriteValue(getattr(self, attribute_name))\r\n xmlwriter.WriteEndElement()","def writexml(self, writer, indent=\"\", add_indent=\"\", new_line=\"\"):\n writer.write(indent + \"<\" + self.tagName)\n attrs = self._get_attributes()\n\n for a_name in attrs.keys():\n writer.write(\" %s=\\\"\" % a_name)\n self.write_data(writer, attrs[a_name].value)\n writer.write(\"\\\"\")\n if self.childNodes:\n writer.write(\">\")\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\n self.childNodes[0].writexml(writer, '', '', '')\n else:\n writer.write(new_line)\n for node in self.childNodes:\n node.writexml(writer, indent + add_indent, add_indent, new_line)\n writer.write(indent)\n writer.write(\"</%s>%s\" % (self.tagName, new_line))\n else:\n writer.write(\"/>%s\" % new_line)","def write(self, string):\n self.__file.write(string)","def writeFile(string_to_write: str, outfile: str):\n with open(outfile, \"w\") as f:\n f.write(string_to_write)","def xmlwrite(self, doc, filename):\n pathname = os.path.join(self.session.session_dir, filename)\n f = open(pathname, \"w\")\n doc.writexml(writer=f, indent=\"\", addindent=\" \", newl=\"\\n\", encoding=\"UTF-8\")\n f.close()","def _write_to_file(self, string):\n with open(self.p.base_dir + '/' + self.p.filename, 'w') as f:\n f.write(string)","def write_file(filename, string):\n import sys\n #ugly fix, hopefully we can find a better one\n if sys.version_info[0] >= 3:\n with open(filename, 'w', encoding=\"utf-8\") as f:\n f.write(string)\n else:\n with open(filename, 'w') as f:\n f.write(string.encode(\"utf-8\"))","def write(writer: BitStreamWriter, value: str) -> None:\n\n writer.writeString(value)","def WriteStringToFile(string, filepath):\n with open(filepath, 'w') as file_handle:\n file_handle.write(string)","def XMLWrite(one, two, three, four, five, six, seven, eight):\n filePath = \"/mnt/RAM/kanban.xml\"\n xmlFile = open(filePath, 'w')\n\n xmlFile.write('<kanbanShelf>\\n')\n xmlFile.write(' <one>%s</one>\\n' % one)\n xmlFile.write(' <two>%s</two>\\n' % two)\n xmlFile.write(' <three>%s</three>\\n' % three)\n xmlFile.write(' <four>%s</four>\\n' % four)\n xmlFile.write(' <five>%s</five>\\n' % five)\n xmlFile.write(' <six>%s</six>\\n' % six)\n xmlFile.write(' <seven>%s</seven>\\n' % seven)\n xmlFile.write(' <eight>%s</eight>\\n' % eight)\n xmlFile.write('</kanbanShelf>')","def XMLWriter(\n fd,\n encoding=\"utf-8\",\n pretty=True,\n compactempty=True,\n indentation=_DEFAULT_INDENTATION\n):\n return _document(fd, encoding, pretty, compactempty, indentation)","def write(self, filename):\n with open(filename, \"w\") as f:\n f.write(self.get_string())","def write(self, filename):\n with open(filename, \"w\") as f:\n f.write(self.get_string())","def save(string, file):\n\n save_file = open(file, 'w')\n save_file.write(string)\n save_file.close()","def save(self, save_path=None):\n if self._xml is None:\n raise IOError(\"There's nothing to save\")\n\n path = self._path_to_xml if save_path is None else save_path\n\n with open(path, 'w') as f:\n rough_string = Et.tostring(self._xml, 'utf-8')\n par = etree.XMLParser(remove_blank_text=True)\n elem = etree.XML(rough_string, parser=par)\n parsed = minidom.parseString(etree.tostring(elem))\n f.write(parsed.toprettyxml(indent=\" \"))","def string_to_file(path_to_file, string_to_write):\n\t\twith open(path_to_file, 'w+') as f:\n\t\t\tf.write(string_to_write)","def saving_file(xml):\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))","def write_file(file,dir_name):\n opened_file = open(dir_name + '/%s'%file,'w')\n opened_file.write('<?xml version=\"1.0\"?>\\n')\n return opened_file","def save_to_file(self, string):\n with open(self.output_path, \"w\") as text_file:\n text_file.write(string)\n print \"Saved to file \" + self.output_path","def write(self, filename):\n \n return self.model.write(filename,xml_declaration=True, encoding='utf-8')","def write(xml, outf):\n my_dom = dom.parseString(tostring(xml))\n f = open(outf, 'w')\n PrettyPrint(my_dom, f, encoding='utf-8')\n f.close()","def write_file(filename=\"\", text=\"\"):\n with open(filename, \"w\") as f:\n return(f.write(text))","def write_file(filename=\"\", text=\"\"):\n with open(filename, 'w') as f:\n return f.write(text)","def XMLWrite(self, one, two, three, four, five, six, seven, eight):\n filePath = \"/mnt/RAM/kanban.xml\"\n xmlFile = open(filePath, 'w')\n \n xmlFile.write('<kanban>\\n')\n xmlFile.write(' <n1>%s</n1>\\n' % one)\n xmlFile.write(' <n2>%s</n2>\\n' % two)\n xmlFile.write(' <n3>%s</n3>\\n' % three)\n xmlFile.write(' <n4>%s</n4>\\n' % four)\n xmlFile.write(' <n5>%s</n5>\\n' % five)\n xmlFile.write(' <n6>%s</n6>\\n' % six)\n xmlFile.write(' <n7>%s</n7>\\n' % seven)\n xmlFile.write(' <n8>%s</n8>\\n' % eight)\n xmlFile.write('</kanban>')","def save_string(cls, name, string):\n fd = open(cls.dirpath + name + '.txt', 'wb')\n fd.write(string)\n fd.close()","def writeFile( str_, *args ):\n filePath = path.join( *args )\n with open( filePath, 'w' ) as fd:\n fd.write(str_)","def save(self, filename=None):\n f = filename if filename else self.path\n etree.register_namespace('', TEI)\n etree.register_namespace('mith', MITH)\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')","def write_to(self, fp):\n fp.write('<')\n fp.write(self.tag)\n for k, v in self.attrs.iteritems():\n fp.write(' ')\n fp.write(k),\n fp.write('=\"')\n fp.write(xml_escape(v))\n fp.write('\"')\n if len(self.contents) == 0:\n fp.write(' />')\n else:\n fp.write('>')\n for item in self.contents:\n if isinstance(item, basestring):\n item = xml_escape(item)\n fp.write(item)\n elif isinstance(item, Markup) or isinstance(item, Element):\n item.write_to(fp)\n else:\n raise TypeError('Item %r must be either a string, '\n '``Element``, or ``Markup``' % item)\n fp.write('</')\n fp.write(self.tag)\n fp.write('>')","def write_file(filename=\"\", text=\"\"):\n with open(filename, 'w') as fl:\n wr = fl.write(text)\n return wr","def write_open_tag(self, outfile, cur_ind):\n outfile.write('{}<{}'.format(cur_ind, self.tag))\n if self.attributes != {}:\n for key, value in self.attributes.items():\n outfile.write(' {}=\"{}\"'.format(key, value))\n outfile.write(' /> ')","def write_file(filename=\"\", text=\"\"):\n with open(filename, 'w', encoding='utf-8') as f:\n return f.write(text)","def write(mmElement, file_or_filename):\n etElem = revert(mmElement)\n xmlTree = ET.ElementTree(etElem)\n xmlTree.write(file_or_filename)","def writexml(self, writer, indent=\"\", addindent=\"\", newl=\"\"):\n if self.childNodes and len(self.childNodes) == 1 and\\\n self.childNodes[0].nodeType == xml.dom.minidom.Node.TEXT_NODE:\n writer.write(indent)\n KmlElement._original_element.writexml(self, writer)\n writer.write(newl)\n else:\n KmlElement._original_element.writexml(self, writer, indent, addindent, newl)","def write_file(filename=\"\", text=\"\"):\n with open(filename, mode=\"w\", encoding=\"utf-8\") as m:\n return m.write(text)","def write_xml(self, xmlfile):\n system.xml.write_file(xmlfile, self.status, 'status')","def write(file, string, codec='utf8'):\n if isinstance(string, str):\n file.write(string)\n elif isinstance(string, unicode):\n file.write(string.encode(codec))","def write(self):\r\n for prop in self.prpnames:\r\n elem = SubElement(self._root, prop)\r\n data = self.__getattribute__(prop)\r\n if self.prpnames[prop]['type'] == \"text\":\r\n elem.text = data\r\n elif self.prpnames[prop]['type'] == 'list':\r\n for x in data:\r\n SubElement(elem, 'regel').text = x\r\n elif self.prpnames[prop]['type'] == 'attr':\r\n elem.set(self.prpnames[prop]['naam'], data)\r\n tree = ElementTree(self._root)\r\n tree.write(self._fn)\r\n if not self.exists:\r\n self.exists = True","def writeToString(self, *args):\n return _libsbml.SBMLWriter_writeToString(self, *args)","def write(file_path, kml_str):\n\n fa.text_writer(file_path, kml_str)","def test_with_no_encoding(self):\n xml = (\n u'<?xml version=\"1.0\"?>'\n u'<DocRoot>'\n u'<Elem1>默认جذ</Elem1>'\n u'<Elem2/>'\n u'</DocRoot>'\n )\n io_string = six.StringIO()\n self.builder.dom_element.write_doc(io_string, encoding=None)\n # NOTE Exact test, no encoding of comparison XML doc string\n self.assertEqual(xml, io_string.getvalue())","def out(filename, s):\n\tf = open(filename, 'w')\n\tf.write(s)\n\tf.close()","def export_to_file(self, filename):\n if len(filename.split(\".\")) == 1:\n filename += \".xml\"\n xmlstring = self._dommodel.toprettyxml(\" \", \"\\n\")\n with open(filename, \"w\") as f:\n f.write(xmlstring)","def writeAttribute(self, *args):\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\n\n\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)","def write(name, keyword, domain, citation, author, description, species, version, contact, license, values, output):\n write_namespace(\n name, keyword, domain, author, citation, values,\n namespace_description=description,\n namespace_species=species,\n namespace_version=version,\n author_contact=contact,\n author_copyright=license,\n file=output,\n )","def write():\n pass","def write(name, keyword, domain, citation, author, description, species, version, contact, license, values,\n functions, output, value_prefix):\n write_namespace(\n name, keyword, domain, author, citation, values,\n namespace_description=description,\n namespace_species=species,\n namespace_version=version,\n author_contact=contact,\n author_copyright=license,\n functions=functions,\n file=output,\n value_prefix=value_prefix\n )","def to_xml_file(self, xml_file_path):\n s = self.to_xml()\n with open(xml_file_path, \"w+b\") as f:\n f.write(s)","def write(self, s):\n ...","def writeSBMLToString(*args):\n return _libsbml.writeSBMLToString(*args)","def write_open_tag(self, outfile, cur_ind):\n outfile.write('{}<{}'.format(cur_ind, self.tag))\n if self.attributes != {}:\n for key, value in self.attributes.items():\n outfile.write(' {}=\"{}\"'.format(key, value))\n outfile.write('>')","def saveSessionToXML(self, filename):\r\n xmlStr = self.createXMLStr()\r\n \r\n #Write to the file\r\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\r\n xmlFile = open(filename, 'w')\r\n xmlFile.write(xmlStr)\r\n xmlFile.close()","def save_svg(string, file_name):\n file_handle = file(file_name, \"w\")\n file_handle.write(string)\n file_handle.close()","def writeAVUToXMLFile(outfile, target, attribute, value, unit=None):\n outfile.write('\\t<AVU>\\n')\n outfile.write(\"\\t\\t<Target>%s</Target>\\n\" % (escape(target),))\n outfile.write(\"\\t\\t<Attribute>%s</Attribute>\\n\" % (escape(attribute),) )\n outfile.write(\"\\t\\t<Value>%s</Value>\\n\" % (escape(value),) )\n if unit:\n outfile.write(\"\\t\\t<Unit>%s</Unit>\\n\" % (unit,) )\n else:\n outfile.write('\\t\\t<Unit />\\n')\n outfile.write('\\t</AVU>\\n')","def write_to_xml(dictData, metadata, xmlfile):\n\tfout = codecs.open(xmlfile, 'w', 'utf-8')\n\tfout.write('<?xml version = \"1.0\" encoding = \"UTF-8\" standalone = \"no\" ?>\\n')\n\tfout.write('<?xml-stylesheet type=\"text/xsl\" href=\"maketable.xsl\"?>\\n')\n\tfout.write('<root>\\n')\n\tfout.write('<meta>\\n')\n\tfor key, value in metadata.items():\n\t\tfout.write('<' + key + '>' + value + '</' + key + '>\\n')\n\tfout.write('</meta>\\n')\n\tfout.write('<content>\\n')\n\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\n\t\txmlline = ''\n\t\txmlline += '<word><headword>' + hw + '</headword><meanings>'\n\t\tfor meaning in meanings:\n\t\t\txmlline += '<m>' + meaning + '</m>'\n\t\txmlline += '</meanings>'\n\t\txmlline += '<verse>'\n\t\tlines = verse.split('<BR>')\n\t\tfor line in lines:\n\t\t\txmlline += '<line>' + line + '</line>'\n\t\txmlline += '</verse>'\n\t\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\n\t\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\n\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\tfout.write(xmlline + '\\n')\n\t\txmlline = ''\n\tfout.write('</content>\\n</root>')\n\tfout.close()\n\n\t# Give some summary to the user\n\tprint('XML file generated. Success!')\n\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))","def creer_fichier(nom_file):\n fichier = open(nom_file, 'w')\n fichier.write(\"<?xml version='1.0' encoding='UTF-8' standalone='yes'?>\\n\")\n fichier.close()","def writeFile(self, filename):\n\n s = self.asString()\n if os.access(filename, os.F_OK):\n raise RuntimeError(\"file %s already exists -- not overwritten.\" % (filename))\n \n f = file(filename, \"w\")\n f.write(s)\n f.close()","def append_string_to_textfile(filename, string):\n filepath = root + filename\n with open(filepath, 'a+') as file:\n file.write(string + \"\\n\")","def write(self, string):\n\n if not self.enabled:\n return\n\n try:\n print(string, end=\"\", file=self.fp)\n except UnicodeEncodeError:\n print(\"(encoding error occured here.)\", file=self.fp)","def writexml(file):\n OUTFILE=open(file,\"w\")\n doc = xml.dom.minidom.Document()\n\n # Create the <dec_reg_list> base element\n decl_reg_list = doc.createElement(\"decl_reg_list\")\n doc.appendChild(decl_reg_list)\n\n regname_old=\"\"\n rows.pop(0)\n for row in rows:\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\n if regname != regname_old:\n # Create the register element\n register = doc.createElement(\"register\")\n register.setAttribute(\"name\", regname)\n register.setAttribute(\"offset\", offset)\n if default != \"\" : register.setAttribute(\"default\", default)\n register.setAttribute(\"type\", regtype)\n if expose_reg == \"1\": register.setAttribute(\"usr\", expose_reg)\n if depth != \"\": register.setAttribute(\"size\", depth)\n if incsz != \"\": register.setAttribute(\"incsz\", incsz)\n text = doc.createTextNode(regdesc)\n register.appendChild(text)\n decl_reg_list.appendChild(register)\n \n # Create the field element\n if bitname != \"\":\n field = doc.createElement(\"field\")\n field.setAttribute(\"name\", bitname)\n if loc !=\"\": field.setAttribute(\"loc\", addcolon(loc))\n if bittype != \"\": field.setAttribute(\"type\", bittype)\n if bitdesc != \"\":\n text = doc.createTextNode(bitdesc)\n field.appendChild(text)\n register.appendChild(field)\n regname_old = regname\n\n\n # Print our newly created XML\n #print doc.toprettyxml(indent=\" \")\n #OUTFILE.write(doc.saveXML(decl_reg_list))\n OUTFILE.write(doc.toprettyxml(indent=\" \"))\n OUTFILE.close()","def writeXml(self):\n text = u' type=\"%s\"' % self.typeName\n if self.format:\n text += u' format=\"%s\"' % escape(self.format, treedoc.escDict)\n if self.prefix:\n text += u' prefix=\"%s\"' % escape(self.prefix, treedoc.escDict)\n if self.suffix:\n text += u' suffix=\"%s\"' % escape(self.suffix, treedoc.escDict)\n if self.html:\n text += u' html=\"y\"'\n if self.isRequired:\n text += u' required=\"y\"'\n if self.hidden:\n text += u' hidden=\"y\"'\n if self.numLines > 1:\n text += u' lines=\"%d\"' % self.numLines\n if self.initDefault:\n text += u' init=\"%s\"' % escape(self.initDefault, treedoc.escDict)\n if self.linkAltField:\n text += u' linkalt=\"%s\"' % escape(self.linkAltField,\n treedoc.escDict)\n return text","def write_file(filename=\"\", text=\"\"):\n with open(filename, mode='w', encoding=\"utf-8\") as myFile:\n chars_written = myFile.write(text)\n return chars_written","def write_file(filename=\"\", text=\"\"):\n\n with open(filename, 'w') as file_:\n\n file_.write(text)\n return len(text)\n file_.close()","def write(self, file_or_filename):\n etMap = revert(self)\n xmlTree = ET.ElementTree(etMap)\n xmlTree.write(file_or_filename)","def html_simple_element(string, tag, attr_str=\"\"):\n if attr_str != \"\":\n attr_str = \" \" + attr_str\n return \"<\" + tag + attr_str + \">\" + string + \"</\" + tag + \">\"","def append_write(filename=\"\", text=\"\"):\n with open(filename, 'a', encoding=\"UTF8\") as f:\n return f.write(str(text))","def writeCleanXML(self, xmlFile, xmlData, indent='', addindent='\\t', newl='\\n', encoding = 'utf-8'):\n file = open(xmlFile, \"w\" ) #@ReservedAssignment\n xmlData.writexml(file, indent, addindent, newl, encoding)\n file.close()\n self.removeEmptyLines(xmlFile)","def fwrite(filename, text):\n basedir = os.path.dirname(filename)\n if not os.path.isdir(basedir):\n os.makedirs(basedir)\n\n with open(filename, 'w') as f:\n f.write(text)","def append_write(filename=\"\", text=\"\"):\n with open(filename, mode=\"a\") as file:\n return file.write(text)","def append_write(filename=\"\", text=\"\"):\n with open(filename, 'a', encoding='utf-8') as MyFile:\n return(MyFile.write(text))","def write_data(writer, data):\n if data:\n data = data.replace(\"&\", \"&\").replace(\"<\", \"<\").replace(\"\\\"\", \""\").replace(\">\", \">\")\n data = data.replace(\"'\", \"'\")\n writer.write(data)","def test_default_indent_and_newline(self):\n self.builder.dom_element.write_doc(self.iobytes, indent=True)\n self.assertEqual(\n u'<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n'\n u'<DocRoot>\\n'\n u' <Elem1>默认جذ</Elem1>\\n'\n u' <Elem2/>\\n'\n u'</DocRoot>\\n'.encode('utf-8'),\n self.iobytes.getvalue())","def print_xml(elem, outfile=None):\n if outfile:\n f = codecs.open(outfile, 'w', 'utf_8')\n else:\n f = sys.stdout\n\n f.write(prettify(elem))\n f.close()","def write_sbml(sbml, filename):\n change_modified_date(sbml)\n writer = libsbml.SBMLWriter()\n writer.writeSBMLToFile(sbml, filename)","def append_write(filename=\"\", text=\"\"):\n with open(filename, 'a') as f:\n return f.write(text)","def write_evt(evt, ev_id):\n print(\"Writing xml file\")\n print(\"../../refined_events/{:}.xml\".format(ev_id))\n evt.write(\"../../refined_events/{:}.xml\".format(ev_id), format=\"QUAKEML\")","def append_write(filename=\"\", text=\"\"):\n with open(filename, \"a\") as file:\n return file.write(text)","def append_write(filename=\"\", text=\"\"):\n with open(filename, 'a') as f:\n a = f.write(str(text))\n return a","def append_write(filename=\"\", text=\"\"):\n with open(filename, \"a\", encoding='utf-8') as the_file:\n return(the_file.write(text))","def write(self, s):\n pass","def append_write(filename=\"\", text=\"\"):\n with open(filename, mode=\"a+\") as f:\n return f.write(text)","def write_amr_string_to_file(self):\n dir_path = os.path.join(self.output_path, 'amr_string')\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n for dataset_name, dataset in self.amr_corpus.items():\n f = open(os.path.join(dir_path, dataset_name + '_amr_string.txt'), 'w')\n for doc_name, doc in dataset.items():\n for amr_id, amr_data in doc.items():\n amr_strings = self.amr_corpus[dataset_name][doc_name][amr_id]['amr_string_triples']\n for left, middle, right in amr_strings:\n if left != '':\n f.write(left+'\\n')\n if right != '':\n f.write(right+'\\n')\n f.close()","def writeDictToXMLFile(outfile, target, dict):\n targetStr = \"\\t\\t<Target>%s</Target>\\n\" % (escape(target),)\n for key in dict.keys():\n outfile.write('\\t<AVU>\\n')\n outfile.write(targetStr)\n outfile.write(\"\\t\\t<Attribute>%s</Attribute>\\n\" % (escape(key),) )\n outfile.write(\"\\t\\t<Value>%s</Value>\\n\" % (escape(dict[key]),) )\n outfile.write('\\t\\t<Unit />\\n')\n outfile.write('\\t</AVU>\\n')","def Write_XML(gui): \n # lock buttons\n gui.action_lock('Lock', gui.save_button)\n \n # clear output\n gui.output_clear() \n \n # get the desired delay from the gui.\n delay_time = gui.get_delay()\n \n \n # get the desired ascii delay from the gui.\n ascii_time = gui.get_ascii_delay()\n \n \n # get the desired I2C address from the gui.\n addr = \"0x%X\" % gui.get_i2c_address()\n \n \n # get the list of commands from the gui\n command_list = gui.get_command_list()\n \n # wrap up the writing directives\n directives = pySCPI_config.write_directives(command_list, addr,\n delay_time, ascii_time)\n \n # create the xml file\n filename = create_XML(directives, gui)\n \n # update the filename display window to show the filename saved\n gui.update_filename(filename = filename) \n \n # unlock the buttons\n gui.action_lock('Unlock')","def write(self, file=None):\n if file is None:\n file = self.name + '.nbk'\n ET.ElementTree(self.root).write(file)","def print_xml(self, filename):\n\n # TODO: check what happens when input is not an xml file\n # TODO: add xmldec, processing instructions and comments\n\n xml_string = u'' # TODO: use a string buffer\n offset = 0\n stack = []\n\n for char in self.text:\n\n # any tags on the stack that can be closed?\n (stack, matching) = self._matching_closing_tags(offset, stack, [])\n for t in matching:\n xml_string += \"</%s>\" % t.name\n\n # any new opening tags?\n for t in self.source_tags.opening_tags.get(offset,[]):\n stack.append(t)\n xml_string += \"<%s%s>\" % (t.name, t.attributes_as_string())\n\n # any of those need to be closed immediately (non-consuming tags)?\n (stack, matching) = self._matching_closing_tags(offset, stack, [])\n for t in matching:\n xml_string += \"</%s>\" % t.name\n\n xml_string += escape(char)\n offset += 1\n\n fh = open(filename, 'w')\n fh.write(xml_string.encode('utf-8'))"],"string":"[\n \"def test_write_string():\\n buf = make_buffer()\\n writer = XmlWriter(buf)\\n writer.write_element('value', 'myvalue')\\n writer.flush()\\n assert_equals(decode_buffer(buf), '<value>myvalue</value>')\",\n \"def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\\r\\n if attribute_name in (\\\"IllegalCharacters\\\", \\\"Months\\\"):\\r\\n write_empty = True\\r\\n dictionary = getattr(self, attribute_name)\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n for key in dictionary:\\r\\n if dictionary[key] or write_empty:\\r\\n xmlwriter.WriteStartElement(\\\"Item\\\")\\r\\n xmlwriter.WriteStartAttribute(\\\"Name\\\")\\r\\n xmlwriter.WriteValue(key)\\r\\n xmlwriter.WriteEndAttribute()\\r\\n xmlwriter.WriteStartAttribute(\\\"Value\\\")\\r\\n xmlwriter.WriteValue(dictionary[key])\\r\\n xmlwriter.WriteEndAttribute()\\r\\n xmlwriter.WriteEndElement()\\r\\n xmlwriter.WriteEndElement()\",\n \"def write(self, str: str, /) -> None:\",\n \"def write_output_file(self, xml_text, xml_file):\\n xml_fo = open(xml_file, 'w')\\n xml_fo.write(xml_text+'</xml>')\\n xml_fo.close()\\n return\",\n \"def write(self):\\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\\\"UTF-8\\\")\\n with open(self.xml_file, 'w') as f:\\n f.write(temp_string)\\n # f = open(self.xml_file, \\\"w\\\")\\n # f.write(temp_string)\\n # f.close()\",\n \"def write_to_xml(filename, xmlelement):\\n xmlpretty = prettify(xmlelement)\\n with open(filename, 'w') as f:\\n f.write(xmlpretty)\",\n \"def xmlWrite(self, xmlWriter, font, value, name, attrs):\\n raise NotImplementedError(self)\",\n \"def write_xml(self, filepath=None, escapeNewline=True, indent=False):\\n\\n if not filepath:\\n filepath = self.mFilePath\\n\\n if indent:\\n self.indent(self.tree.getroot())\\n\\n output = StringIO()\\n\\n self.tree.write(output, encoding=\\\"UTF-8\\\")\\n\\n outFile = open(filepath, \\\"w\\\")\\n if escapeNewline:\\n # we need to make sure newline 
 is written correctly\\n print >> outFile, re.sub(\\\"###newline_escape###\\\", \\\"
\\\", output.getvalue())\\n else:\\n print >> outFile, output.getvalue()\\n\\n outFile.close\",\n \"def writeString(self, str):\\n ret = libxml2mod.xmlOutputBufferWriteString(self._o, str)\\n return ret\",\n \"def writeFile(self, filename):\\n s = ET.tostring(self._root)\\n\\n #Remove all formatting\\n s = s.replace('\\\\n','')\\n s = s.replace('\\\\t','')\\n s = s.replace('\\\\r','')\\n\\n f = open(filename, 'w')\\n f.write(minidom.parseString(s).toprettyxml())\\n f.close()\",\n \"def writetree(self, string):\\n self.treebuf.write(string)\\n if self.animal != None:\\n self.animal.writetree(string)\",\n \"def exportXml ( w, xml ):\\n assert str ( type ( xml ) ) == \\\"<type 'str'>\\\"\\n rawText = xml\\n pattern = re.compile (r'[^\\\\S ]+')\\n text = re.sub ( pattern, \\\"\\\", rawText )\\n reparsed = MD.parseString ( text )\\n w.write ( reparsed.toprettyxml ( indent = \\\"\\\\t\\\", encoding = \\\"UTF-8\\\" ) )\",\n \"def write_file(name_file, string):\\n with open(name_file, 'w') as file:\\n file.write(string)\",\n \"def test_write_defaults(self):\\n xml = (\\n u'<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>'\\n u'<DocRoot>'\\n u'<Elem1>默认جذ</Elem1>'\\n u'<Elem2/>'\\n u'</DocRoot>'\\n )\\n io_string = six.StringIO()\\n self.builder.write_doc(io_string)\\n if six.PY2:\\n self.assertEqual(xml.encode('utf-8'), io_string.getvalue())\\n else:\\n self.assertEqual(xml, io_string.getvalue())\",\n \"def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\\r\\n attribute_list = getattr(self, attribute_name)\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n for item in attribute_list:\\r\\n if item or write_empty:\\r\\n xmlwriter.WriteElementString(\\\"Item\\\", item)\\r\\n xmlwriter.WriteEndElement()\",\n \"def write_bool_to_xml(self, attribute_name, xmlwriter):\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n xmlwriter.WriteValue(getattr(self, attribute_name))\\r\\n xmlwriter.WriteEndElement()\",\n \"def writexml(self, writer, indent=\\\"\\\", add_indent=\\\"\\\", new_line=\\\"\\\"):\\n writer.write(indent + \\\"<\\\" + self.tagName)\\n attrs = self._get_attributes()\\n\\n for a_name in attrs.keys():\\n writer.write(\\\" %s=\\\\\\\"\\\" % a_name)\\n self.write_data(writer, attrs[a_name].value)\\n writer.write(\\\"\\\\\\\"\\\")\\n if self.childNodes:\\n writer.write(\\\">\\\")\\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\\n self.childNodes[0].writexml(writer, '', '', '')\\n else:\\n writer.write(new_line)\\n for node in self.childNodes:\\n node.writexml(writer, indent + add_indent, add_indent, new_line)\\n writer.write(indent)\\n writer.write(\\\"</%s>%s\\\" % (self.tagName, new_line))\\n else:\\n writer.write(\\\"/>%s\\\" % new_line)\",\n \"def write(self, string):\\n self.__file.write(string)\",\n \"def writeFile(string_to_write: str, outfile: str):\\n with open(outfile, \\\"w\\\") as f:\\n f.write(string_to_write)\",\n \"def xmlwrite(self, doc, filename):\\n pathname = os.path.join(self.session.session_dir, filename)\\n f = open(pathname, \\\"w\\\")\\n doc.writexml(writer=f, indent=\\\"\\\", addindent=\\\" \\\", newl=\\\"\\\\n\\\", encoding=\\\"UTF-8\\\")\\n f.close()\",\n \"def _write_to_file(self, string):\\n with open(self.p.base_dir + '/' + self.p.filename, 'w') as f:\\n f.write(string)\",\n \"def write_file(filename, string):\\n import sys\\n #ugly fix, hopefully we can find a better one\\n if sys.version_info[0] >= 3:\\n with open(filename, 'w', encoding=\\\"utf-8\\\") as f:\\n f.write(string)\\n else:\\n with open(filename, 'w') as f:\\n f.write(string.encode(\\\"utf-8\\\"))\",\n \"def write(writer: BitStreamWriter, value: str) -> None:\\n\\n writer.writeString(value)\",\n \"def WriteStringToFile(string, filepath):\\n with open(filepath, 'w') as file_handle:\\n file_handle.write(string)\",\n \"def XMLWrite(one, two, three, four, five, six, seven, eight):\\n filePath = \\\"/mnt/RAM/kanban.xml\\\"\\n xmlFile = open(filePath, 'w')\\n\\n xmlFile.write('<kanbanShelf>\\\\n')\\n xmlFile.write(' <one>%s</one>\\\\n' % one)\\n xmlFile.write(' <two>%s</two>\\\\n' % two)\\n xmlFile.write(' <three>%s</three>\\\\n' % three)\\n xmlFile.write(' <four>%s</four>\\\\n' % four)\\n xmlFile.write(' <five>%s</five>\\\\n' % five)\\n xmlFile.write(' <six>%s</six>\\\\n' % six)\\n xmlFile.write(' <seven>%s</seven>\\\\n' % seven)\\n xmlFile.write(' <eight>%s</eight>\\\\n' % eight)\\n xmlFile.write('</kanbanShelf>')\",\n \"def XMLWriter(\\n fd,\\n encoding=\\\"utf-8\\\",\\n pretty=True,\\n compactempty=True,\\n indentation=_DEFAULT_INDENTATION\\n):\\n return _document(fd, encoding, pretty, compactempty, indentation)\",\n \"def write(self, filename):\\n with open(filename, \\\"w\\\") as f:\\n f.write(self.get_string())\",\n \"def write(self, filename):\\n with open(filename, \\\"w\\\") as f:\\n f.write(self.get_string())\",\n \"def save(string, file):\\n\\n save_file = open(file, 'w')\\n save_file.write(string)\\n save_file.close()\",\n \"def save(self, save_path=None):\\n if self._xml is None:\\n raise IOError(\\\"There's nothing to save\\\")\\n\\n path = self._path_to_xml if save_path is None else save_path\\n\\n with open(path, 'w') as f:\\n rough_string = Et.tostring(self._xml, 'utf-8')\\n par = etree.XMLParser(remove_blank_text=True)\\n elem = etree.XML(rough_string, parser=par)\\n parsed = minidom.parseString(etree.tostring(elem))\\n f.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def string_to_file(path_to_file, string_to_write):\\n\\t\\twith open(path_to_file, 'w+') as f:\\n\\t\\t\\tf.write(string_to_write)\",\n \"def saving_file(xml):\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def write_file(file,dir_name):\\n opened_file = open(dir_name + '/%s'%file,'w')\\n opened_file.write('<?xml version=\\\"1.0\\\"?>\\\\n')\\n return opened_file\",\n \"def save_to_file(self, string):\\n with open(self.output_path, \\\"w\\\") as text_file:\\n text_file.write(string)\\n print \\\"Saved to file \\\" + self.output_path\",\n \"def write(self, filename):\\n \\n return self.model.write(filename,xml_declaration=True, encoding='utf-8')\",\n \"def write(xml, outf):\\n my_dom = dom.parseString(tostring(xml))\\n f = open(outf, 'w')\\n PrettyPrint(my_dom, f, encoding='utf-8')\\n f.close()\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, \\\"w\\\") as f:\\n return(f.write(text))\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'w') as f:\\n return f.write(text)\",\n \"def XMLWrite(self, one, two, three, four, five, six, seven, eight):\\n filePath = \\\"/mnt/RAM/kanban.xml\\\"\\n xmlFile = open(filePath, 'w')\\n \\n xmlFile.write('<kanban>\\\\n')\\n xmlFile.write(' <n1>%s</n1>\\\\n' % one)\\n xmlFile.write(' <n2>%s</n2>\\\\n' % two)\\n xmlFile.write(' <n3>%s</n3>\\\\n' % three)\\n xmlFile.write(' <n4>%s</n4>\\\\n' % four)\\n xmlFile.write(' <n5>%s</n5>\\\\n' % five)\\n xmlFile.write(' <n6>%s</n6>\\\\n' % six)\\n xmlFile.write(' <n7>%s</n7>\\\\n' % seven)\\n xmlFile.write(' <n8>%s</n8>\\\\n' % eight)\\n xmlFile.write('</kanban>')\",\n \"def save_string(cls, name, string):\\n fd = open(cls.dirpath + name + '.txt', 'wb')\\n fd.write(string)\\n fd.close()\",\n \"def writeFile( str_, *args ):\\n filePath = path.join( *args )\\n with open( filePath, 'w' ) as fd:\\n fd.write(str_)\",\n \"def save(self, filename=None):\\n f = filename if filename else self.path\\n etree.register_namespace('', TEI)\\n etree.register_namespace('mith', MITH)\\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')\",\n \"def write_to(self, fp):\\n fp.write('<')\\n fp.write(self.tag)\\n for k, v in self.attrs.iteritems():\\n fp.write(' ')\\n fp.write(k),\\n fp.write('=\\\"')\\n fp.write(xml_escape(v))\\n fp.write('\\\"')\\n if len(self.contents) == 0:\\n fp.write(' />')\\n else:\\n fp.write('>')\\n for item in self.contents:\\n if isinstance(item, basestring):\\n item = xml_escape(item)\\n fp.write(item)\\n elif isinstance(item, Markup) or isinstance(item, Element):\\n item.write_to(fp)\\n else:\\n raise TypeError('Item %r must be either a string, '\\n '``Element``, or ``Markup``' % item)\\n fp.write('</')\\n fp.write(self.tag)\\n fp.write('>')\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'w') as fl:\\n wr = fl.write(text)\\n return wr\",\n \"def write_open_tag(self, outfile, cur_ind):\\n outfile.write('{}<{}'.format(cur_ind, self.tag))\\n if self.attributes != {}:\\n for key, value in self.attributes.items():\\n outfile.write(' {}=\\\"{}\\\"'.format(key, value))\\n outfile.write(' /> ')\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'w', encoding='utf-8') as f:\\n return f.write(text)\",\n \"def write(mmElement, file_or_filename):\\n etElem = revert(mmElement)\\n xmlTree = ET.ElementTree(etElem)\\n xmlTree.write(file_or_filename)\",\n \"def writexml(self, writer, indent=\\\"\\\", addindent=\\\"\\\", newl=\\\"\\\"):\\n if self.childNodes and len(self.childNodes) == 1 and\\\\\\n self.childNodes[0].nodeType == xml.dom.minidom.Node.TEXT_NODE:\\n writer.write(indent)\\n KmlElement._original_element.writexml(self, writer)\\n writer.write(newl)\\n else:\\n KmlElement._original_element.writexml(self, writer, indent, addindent, newl)\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as m:\\n return m.write(text)\",\n \"def write_xml(self, xmlfile):\\n system.xml.write_file(xmlfile, self.status, 'status')\",\n \"def write(file, string, codec='utf8'):\\n if isinstance(string, str):\\n file.write(string)\\n elif isinstance(string, unicode):\\n file.write(string.encode(codec))\",\n \"def write(self):\\r\\n for prop in self.prpnames:\\r\\n elem = SubElement(self._root, prop)\\r\\n data = self.__getattribute__(prop)\\r\\n if self.prpnames[prop]['type'] == \\\"text\\\":\\r\\n elem.text = data\\r\\n elif self.prpnames[prop]['type'] == 'list':\\r\\n for x in data:\\r\\n SubElement(elem, 'regel').text = x\\r\\n elif self.prpnames[prop]['type'] == 'attr':\\r\\n elem.set(self.prpnames[prop]['naam'], data)\\r\\n tree = ElementTree(self._root)\\r\\n tree.write(self._fn)\\r\\n if not self.exists:\\r\\n self.exists = True\",\n \"def writeToString(self, *args):\\n return _libsbml.SBMLWriter_writeToString(self, *args)\",\n \"def write(file_path, kml_str):\\n\\n fa.text_writer(file_path, kml_str)\",\n \"def test_with_no_encoding(self):\\n xml = (\\n u'<?xml version=\\\"1.0\\\"?>'\\n u'<DocRoot>'\\n u'<Elem1>默认جذ</Elem1>'\\n u'<Elem2/>'\\n u'</DocRoot>'\\n )\\n io_string = six.StringIO()\\n self.builder.dom_element.write_doc(io_string, encoding=None)\\n # NOTE Exact test, no encoding of comparison XML doc string\\n self.assertEqual(xml, io_string.getvalue())\",\n \"def out(filename, s):\\n\\tf = open(filename, 'w')\\n\\tf.write(s)\\n\\tf.close()\",\n \"def export_to_file(self, filename):\\n if len(filename.split(\\\".\\\")) == 1:\\n filename += \\\".xml\\\"\\n xmlstring = self._dommodel.toprettyxml(\\\" \\\", \\\"\\\\n\\\")\\n with open(filename, \\\"w\\\") as f:\\n f.write(xmlstring)\",\n \"def writeAttribute(self, *args):\\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\\n\\n\\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)\",\n \"def write(name, keyword, domain, citation, author, description, species, version, contact, license, values, output):\\n write_namespace(\\n name, keyword, domain, author, citation, values,\\n namespace_description=description,\\n namespace_species=species,\\n namespace_version=version,\\n author_contact=contact,\\n author_copyright=license,\\n file=output,\\n )\",\n \"def write():\\n pass\",\n \"def write(name, keyword, domain, citation, author, description, species, version, contact, license, values,\\n functions, output, value_prefix):\\n write_namespace(\\n name, keyword, domain, author, citation, values,\\n namespace_description=description,\\n namespace_species=species,\\n namespace_version=version,\\n author_contact=contact,\\n author_copyright=license,\\n functions=functions,\\n file=output,\\n value_prefix=value_prefix\\n )\",\n \"def to_xml_file(self, xml_file_path):\\n s = self.to_xml()\\n with open(xml_file_path, \\\"w+b\\\") as f:\\n f.write(s)\",\n \"def write(self, s):\\n ...\",\n \"def writeSBMLToString(*args):\\n return _libsbml.writeSBMLToString(*args)\",\n \"def write_open_tag(self, outfile, cur_ind):\\n outfile.write('{}<{}'.format(cur_ind, self.tag))\\n if self.attributes != {}:\\n for key, value in self.attributes.items():\\n outfile.write(' {}=\\\"{}\\\"'.format(key, value))\\n outfile.write('>')\",\n \"def saveSessionToXML(self, filename):\\r\\n xmlStr = self.createXMLStr()\\r\\n \\r\\n #Write to the file\\r\\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\\r\\n xmlFile = open(filename, 'w')\\r\\n xmlFile.write(xmlStr)\\r\\n xmlFile.close()\",\n \"def save_svg(string, file_name):\\n file_handle = file(file_name, \\\"w\\\")\\n file_handle.write(string)\\n file_handle.close()\",\n \"def writeAVUToXMLFile(outfile, target, attribute, value, unit=None):\\n outfile.write('\\\\t<AVU>\\\\n')\\n outfile.write(\\\"\\\\t\\\\t<Target>%s</Target>\\\\n\\\" % (escape(target),))\\n outfile.write(\\\"\\\\t\\\\t<Attribute>%s</Attribute>\\\\n\\\" % (escape(attribute),) )\\n outfile.write(\\\"\\\\t\\\\t<Value>%s</Value>\\\\n\\\" % (escape(value),) )\\n if unit:\\n outfile.write(\\\"\\\\t\\\\t<Unit>%s</Unit>\\\\n\\\" % (unit,) )\\n else:\\n outfile.write('\\\\t\\\\t<Unit />\\\\n')\\n outfile.write('\\\\t</AVU>\\\\n')\",\n \"def write_to_xml(dictData, metadata, xmlfile):\\n\\tfout = codecs.open(xmlfile, 'w', 'utf-8')\\n\\tfout.write('<?xml version = \\\"1.0\\\" encoding = \\\"UTF-8\\\" standalone = \\\"no\\\" ?>\\\\n')\\n\\tfout.write('<?xml-stylesheet type=\\\"text/xsl\\\" href=\\\"maketable.xsl\\\"?>\\\\n')\\n\\tfout.write('<root>\\\\n')\\n\\tfout.write('<meta>\\\\n')\\n\\tfor key, value in metadata.items():\\n\\t\\tfout.write('<' + key + '>' + value + '</' + key + '>\\\\n')\\n\\tfout.write('</meta>\\\\n')\\n\\tfout.write('<content>\\\\n')\\n\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\\n\\t\\txmlline = ''\\n\\t\\txmlline += '<word><headword>' + hw + '</headword><meanings>'\\n\\t\\tfor meaning in meanings:\\n\\t\\t\\txmlline += '<m>' + meaning + '</m>'\\n\\t\\txmlline += '</meanings>'\\n\\t\\txmlline += '<verse>'\\n\\t\\tlines = verse.split('<BR>')\\n\\t\\tfor line in lines:\\n\\t\\t\\txmlline += '<line>' + line + '</line>'\\n\\t\\txmlline += '</verse>'\\n\\t\\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\\n\\t\\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\\n\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\tfout.write(xmlline + '\\\\n')\\n\\t\\txmlline = ''\\n\\tfout.write('</content>\\\\n</root>')\\n\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('XML file generated. Success!')\\n\\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))\",\n \"def creer_fichier(nom_file):\\n fichier = open(nom_file, 'w')\\n fichier.write(\\\"<?xml version='1.0' encoding='UTF-8' standalone='yes'?>\\\\n\\\")\\n fichier.close()\",\n \"def writeFile(self, filename):\\n\\n s = self.asString()\\n if os.access(filename, os.F_OK):\\n raise RuntimeError(\\\"file %s already exists -- not overwritten.\\\" % (filename))\\n \\n f = file(filename, \\\"w\\\")\\n f.write(s)\\n f.close()\",\n \"def append_string_to_textfile(filename, string):\\n filepath = root + filename\\n with open(filepath, 'a+') as file:\\n file.write(string + \\\"\\\\n\\\")\",\n \"def write(self, string):\\n\\n if not self.enabled:\\n return\\n\\n try:\\n print(string, end=\\\"\\\", file=self.fp)\\n except UnicodeEncodeError:\\n print(\\\"(encoding error occured here.)\\\", file=self.fp)\",\n \"def writexml(file):\\n OUTFILE=open(file,\\\"w\\\")\\n doc = xml.dom.minidom.Document()\\n\\n # Create the <dec_reg_list> base element\\n decl_reg_list = doc.createElement(\\\"decl_reg_list\\\")\\n doc.appendChild(decl_reg_list)\\n\\n regname_old=\\\"\\\"\\n rows.pop(0)\\n for row in rows:\\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\\n if regname != regname_old:\\n # Create the register element\\n register = doc.createElement(\\\"register\\\")\\n register.setAttribute(\\\"name\\\", regname)\\n register.setAttribute(\\\"offset\\\", offset)\\n if default != \\\"\\\" : register.setAttribute(\\\"default\\\", default)\\n register.setAttribute(\\\"type\\\", regtype)\\n if expose_reg == \\\"1\\\": register.setAttribute(\\\"usr\\\", expose_reg)\\n if depth != \\\"\\\": register.setAttribute(\\\"size\\\", depth)\\n if incsz != \\\"\\\": register.setAttribute(\\\"incsz\\\", incsz)\\n text = doc.createTextNode(regdesc)\\n register.appendChild(text)\\n decl_reg_list.appendChild(register)\\n \\n # Create the field element\\n if bitname != \\\"\\\":\\n field = doc.createElement(\\\"field\\\")\\n field.setAttribute(\\\"name\\\", bitname)\\n if loc !=\\\"\\\": field.setAttribute(\\\"loc\\\", addcolon(loc))\\n if bittype != \\\"\\\": field.setAttribute(\\\"type\\\", bittype)\\n if bitdesc != \\\"\\\":\\n text = doc.createTextNode(bitdesc)\\n field.appendChild(text)\\n register.appendChild(field)\\n regname_old = regname\\n\\n\\n # Print our newly created XML\\n #print doc.toprettyxml(indent=\\\" \\\")\\n #OUTFILE.write(doc.saveXML(decl_reg_list))\\n OUTFILE.write(doc.toprettyxml(indent=\\\" \\\"))\\n OUTFILE.close()\",\n \"def writeXml(self):\\n text = u' type=\\\"%s\\\"' % self.typeName\\n if self.format:\\n text += u' format=\\\"%s\\\"' % escape(self.format, treedoc.escDict)\\n if self.prefix:\\n text += u' prefix=\\\"%s\\\"' % escape(self.prefix, treedoc.escDict)\\n if self.suffix:\\n text += u' suffix=\\\"%s\\\"' % escape(self.suffix, treedoc.escDict)\\n if self.html:\\n text += u' html=\\\"y\\\"'\\n if self.isRequired:\\n text += u' required=\\\"y\\\"'\\n if self.hidden:\\n text += u' hidden=\\\"y\\\"'\\n if self.numLines > 1:\\n text += u' lines=\\\"%d\\\"' % self.numLines\\n if self.initDefault:\\n text += u' init=\\\"%s\\\"' % escape(self.initDefault, treedoc.escDict)\\n if self.linkAltField:\\n text += u' linkalt=\\\"%s\\\"' % escape(self.linkAltField,\\n treedoc.escDict)\\n return text\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, mode='w', encoding=\\\"utf-8\\\") as myFile:\\n chars_written = myFile.write(text)\\n return chars_written\",\n \"def write_file(filename=\\\"\\\", text=\\\"\\\"):\\n\\n with open(filename, 'w') as file_:\\n\\n file_.write(text)\\n return len(text)\\n file_.close()\",\n \"def write(self, file_or_filename):\\n etMap = revert(self)\\n xmlTree = ET.ElementTree(etMap)\\n xmlTree.write(file_or_filename)\",\n \"def html_simple_element(string, tag, attr_str=\\\"\\\"):\\n if attr_str != \\\"\\\":\\n attr_str = \\\" \\\" + attr_str\\n return \\\"<\\\" + tag + attr_str + \\\">\\\" + string + \\\"</\\\" + tag + \\\">\\\"\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'a', encoding=\\\"UTF8\\\") as f:\\n return f.write(str(text))\",\n \"def writeCleanXML(self, xmlFile, xmlData, indent='', addindent='\\\\t', newl='\\\\n', encoding = 'utf-8'):\\n file = open(xmlFile, \\\"w\\\" ) #@ReservedAssignment\\n xmlData.writexml(file, indent, addindent, newl, encoding)\\n file.close()\\n self.removeEmptyLines(xmlFile)\",\n \"def fwrite(filename, text):\\n basedir = os.path.dirname(filename)\\n if not os.path.isdir(basedir):\\n os.makedirs(basedir)\\n\\n with open(filename, 'w') as f:\\n f.write(text)\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, mode=\\\"a\\\") as file:\\n return file.write(text)\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'a', encoding='utf-8') as MyFile:\\n return(MyFile.write(text))\",\n \"def write_data(writer, data):\\n if data:\\n data = data.replace(\\\"&\\\", \\\"&\\\").replace(\\\"<\\\", \\\"<\\\").replace(\\\"\\\\\\\"\\\", \\\""\\\").replace(\\\">\\\", \\\">\\\")\\n data = data.replace(\\\"'\\\", \\\"'\\\")\\n writer.write(data)\",\n \"def test_default_indent_and_newline(self):\\n self.builder.dom_element.write_doc(self.iobytes, indent=True)\\n self.assertEqual(\\n u'<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>\\\\n'\\n u'<DocRoot>\\\\n'\\n u' <Elem1>默认جذ</Elem1>\\\\n'\\n u' <Elem2/>\\\\n'\\n u'</DocRoot>\\\\n'.encode('utf-8'),\\n self.iobytes.getvalue())\",\n \"def print_xml(elem, outfile=None):\\n if outfile:\\n f = codecs.open(outfile, 'w', 'utf_8')\\n else:\\n f = sys.stdout\\n\\n f.write(prettify(elem))\\n f.close()\",\n \"def write_sbml(sbml, filename):\\n change_modified_date(sbml)\\n writer = libsbml.SBMLWriter()\\n writer.writeSBMLToFile(sbml, filename)\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'a') as f:\\n return f.write(text)\",\n \"def write_evt(evt, ev_id):\\n print(\\\"Writing xml file\\\")\\n print(\\\"../../refined_events/{:}.xml\\\".format(ev_id))\\n evt.write(\\\"../../refined_events/{:}.xml\\\".format(ev_id), format=\\\"QUAKEML\\\")\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, \\\"a\\\") as file:\\n return file.write(text)\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, 'a') as f:\\n a = f.write(str(text))\\n return a\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, \\\"a\\\", encoding='utf-8') as the_file:\\n return(the_file.write(text))\",\n \"def write(self, s):\\n pass\",\n \"def append_write(filename=\\\"\\\", text=\\\"\\\"):\\n with open(filename, mode=\\\"a+\\\") as f:\\n return f.write(text)\",\n \"def write_amr_string_to_file(self):\\n dir_path = os.path.join(self.output_path, 'amr_string')\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\\n for dataset_name, dataset in self.amr_corpus.items():\\n f = open(os.path.join(dir_path, dataset_name + '_amr_string.txt'), 'w')\\n for doc_name, doc in dataset.items():\\n for amr_id, amr_data in doc.items():\\n amr_strings = self.amr_corpus[dataset_name][doc_name][amr_id]['amr_string_triples']\\n for left, middle, right in amr_strings:\\n if left != '':\\n f.write(left+'\\\\n')\\n if right != '':\\n f.write(right+'\\\\n')\\n f.close()\",\n \"def writeDictToXMLFile(outfile, target, dict):\\n targetStr = \\\"\\\\t\\\\t<Target>%s</Target>\\\\n\\\" % (escape(target),)\\n for key in dict.keys():\\n outfile.write('\\\\t<AVU>\\\\n')\\n outfile.write(targetStr)\\n outfile.write(\\\"\\\\t\\\\t<Attribute>%s</Attribute>\\\\n\\\" % (escape(key),) )\\n outfile.write(\\\"\\\\t\\\\t<Value>%s</Value>\\\\n\\\" % (escape(dict[key]),) )\\n outfile.write('\\\\t\\\\t<Unit />\\\\n')\\n outfile.write('\\\\t</AVU>\\\\n')\",\n \"def Write_XML(gui): \\n # lock buttons\\n gui.action_lock('Lock', gui.save_button)\\n \\n # clear output\\n gui.output_clear() \\n \\n # get the desired delay from the gui.\\n delay_time = gui.get_delay()\\n \\n \\n # get the desired ascii delay from the gui.\\n ascii_time = gui.get_ascii_delay()\\n \\n \\n # get the desired I2C address from the gui.\\n addr = \\\"0x%X\\\" % gui.get_i2c_address()\\n \\n \\n # get the list of commands from the gui\\n command_list = gui.get_command_list()\\n \\n # wrap up the writing directives\\n directives = pySCPI_config.write_directives(command_list, addr,\\n delay_time, ascii_time)\\n \\n # create the xml file\\n filename = create_XML(directives, gui)\\n \\n # update the filename display window to show the filename saved\\n gui.update_filename(filename = filename) \\n \\n # unlock the buttons\\n gui.action_lock('Unlock')\",\n \"def write(self, file=None):\\n if file is None:\\n file = self.name + '.nbk'\\n ET.ElementTree(self.root).write(file)\",\n \"def print_xml(self, filename):\\n\\n # TODO: check what happens when input is not an xml file\\n # TODO: add xmldec, processing instructions and comments\\n\\n xml_string = u'' # TODO: use a string buffer\\n offset = 0\\n stack = []\\n\\n for char in self.text:\\n\\n # any tags on the stack that can be closed?\\n (stack, matching) = self._matching_closing_tags(offset, stack, [])\\n for t in matching:\\n xml_string += \\\"</%s>\\\" % t.name\\n\\n # any new opening tags?\\n for t in self.source_tags.opening_tags.get(offset,[]):\\n stack.append(t)\\n xml_string += \\\"<%s%s>\\\" % (t.name, t.attributes_as_string())\\n\\n # any of those need to be closed immediately (non-consuming tags)?\\n (stack, matching) = self._matching_closing_tags(offset, stack, [])\\n for t in matching:\\n xml_string += \\\"</%s>\\\" % t.name\\n\\n xml_string += escape(char)\\n offset += 1\\n\\n fh = open(filename, 'w')\\n fh.write(xml_string.encode('utf-8'))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6247003","0.58558315","0.5849161","0.57865465","0.5765893","0.5722448","0.5617337","0.5580295","0.55794567","0.554683","0.55136317","0.55108947","0.54859346","0.54719436","0.5470503","0.54250485","0.53961504","0.5380822","0.5374993","0.53526664","0.5339592","0.5307603","0.529733","0.52938664","0.5293728","0.523522","0.52173513","0.52173513","0.5216224","0.5183375","0.51742166","0.51605874","0.5109411","0.5099646","0.50941837","0.50904566","0.5086038","0.5076724","0.5032658","0.5018256","0.50048727","0.49959135","0.49927434","0.4984989","0.49637392","0.4924736","0.49218658","0.4914309","0.49082497","0.49033642","0.4890298","0.4889121","0.48774126","0.48559394","0.48552924","0.4842777","0.48398745","0.48238617","0.48229134","0.48114777","0.48100203","0.48081034","0.48075315","0.480124","0.47956616","0.47905472","0.4784276","0.47801572","0.477982","0.47716334","0.476437","0.47631714","0.47516328","0.47449672","0.47403798","0.47374","0.47358876","0.47303703","0.47209883","0.47181743","0.47141123","0.47000405","0.46746448","0.46696416","0.46662408","0.4663689","0.46572927","0.4652124","0.46453473","0.4639896","0.46335518","0.46215641","0.46140552","0.46072453","0.46054804","0.460259","0.45987335","0.4590262","0.4585005","0.45781606"],"string":"[\n \"0.6247003\",\n \"0.58558315\",\n \"0.5849161\",\n \"0.57865465\",\n \"0.5765893\",\n \"0.5722448\",\n \"0.5617337\",\n \"0.5580295\",\n \"0.55794567\",\n \"0.554683\",\n \"0.55136317\",\n \"0.55108947\",\n \"0.54859346\",\n \"0.54719436\",\n \"0.5470503\",\n \"0.54250485\",\n \"0.53961504\",\n \"0.5380822\",\n \"0.5374993\",\n \"0.53526664\",\n \"0.5339592\",\n \"0.5307603\",\n \"0.529733\",\n \"0.52938664\",\n \"0.5293728\",\n \"0.523522\",\n \"0.52173513\",\n \"0.52173513\",\n \"0.5216224\",\n \"0.5183375\",\n \"0.51742166\",\n \"0.51605874\",\n \"0.5109411\",\n \"0.5099646\",\n \"0.50941837\",\n \"0.50904566\",\n \"0.5086038\",\n \"0.5076724\",\n \"0.5032658\",\n \"0.5018256\",\n \"0.50048727\",\n \"0.49959135\",\n \"0.49927434\",\n \"0.4984989\",\n \"0.49637392\",\n \"0.4924736\",\n \"0.49218658\",\n \"0.4914309\",\n \"0.49082497\",\n \"0.49033642\",\n \"0.4890298\",\n \"0.4889121\",\n \"0.48774126\",\n \"0.48559394\",\n \"0.48552924\",\n \"0.4842777\",\n \"0.48398745\",\n \"0.48238617\",\n \"0.48229134\",\n \"0.48114777\",\n \"0.48100203\",\n \"0.48081034\",\n \"0.48075315\",\n \"0.480124\",\n \"0.47956616\",\n \"0.47905472\",\n \"0.4784276\",\n \"0.47801572\",\n \"0.477982\",\n \"0.47716334\",\n \"0.476437\",\n \"0.47631714\",\n \"0.47516328\",\n \"0.47449672\",\n \"0.47403798\",\n \"0.47374\",\n \"0.47358876\",\n \"0.47303703\",\n \"0.47209883\",\n \"0.47181743\",\n \"0.47141123\",\n \"0.47000405\",\n \"0.46746448\",\n \"0.46696416\",\n \"0.46662408\",\n \"0.4663689\",\n \"0.46572927\",\n \"0.4652124\",\n \"0.46453473\",\n \"0.4639896\",\n \"0.46335518\",\n \"0.46215641\",\n \"0.46140552\",\n \"0.46072453\",\n \"0.46054804\",\n \"0.460259\",\n \"0.45987335\",\n \"0.4590262\",\n \"0.4585005\",\n \"0.45781606\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8222901"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11587,"cells":{"query":{"kind":"string","value":"Writes a boolean to an xml file in the form of true/false attribute_name>The name of the attribute to write. xmlwriter>The xml writer to write with."},"document":{"kind":"string","value":"def write_bool_to_xml(self, attribute_name, xmlwriter):\r\n xmlwriter.WriteStartElement(attribute_name)\r\n xmlwriter.WriteValue(getattr(self, attribute_name))\r\n xmlwriter.WriteEndElement()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def writeAttribute(self, *args):\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\n\n\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)","def writeAttributeBool(self, *args):\n return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)","def write(writer: BitStreamWriter, value: bool) -> None:\n\n writer.writeBool(value)","def write_boolean(self, boolean: bool) -> None:\n self.write(bytearray([bool(boolean)]))","def _writeBool(self, val):\n self.__writeValue(self.boolFormat, val)","def writeBoolean(self, value: bool):\n self.writeByte(1 if value else 0)","def write_bool(self, b: bool) -> None:\n self.buffer += struct.pack(\"<?\", b)","def bool_attr(attr):\n if attr.lower() == \"true\":\n val = True\n elif attr.lower() == \"false\":\n val = False\n else:\n raise EzXMLError(\"Must be \"\\\n \"'true' or 'false'. Not %s\" % (attr))\n return val","def htmlIsBooleanAttr(name):\n ret = libxml2mod.htmlIsBooleanAttr(name)\n return ret","def set(self, attr, value=True):\n if type(value) == bool:\n self.__dict__['_'+attr] = value\n print attr, \"set to\", value\n else:\n print 'Value must be a bool, either \"True\" or \"False\" (no quotes)!'","def set_boolean(x):\n\n if x:\n return \"True\"\n else:\n return \"False\"","def set_boolean(dict, name, elem):\n node = elem.find(name)\n if node is not None:\n if node.text.lower() == 'yes':\n dict[name] = True\n elif node.text.lower() == 'no':\n dict[name] = False","def write_xml(self, xmlfile):\n system.xml.write_file(xmlfile, self.status, 'status')","def saveState(self):\n e = xml.Element(self.type)\n e.attrib['lastUpdate'] = str(clock.now())\n e.attrib['name'] = self.name\n #e.attrib['status'] = ('true' if self.status else 'false')\n return e","def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\r\n string = getattr(self, attribute_name)\r\n if string or write_empty:\r\n xmlwriter.WriteElementString(attribute_name, string)","def update_bool(file_path):\n with open(\n file_path, 'r'\n ) as the_result_file_from_spark_for_read_and_abbr_not_allowed_by_pylint:\n content = the_result_file_from_spark_for_read_and_abbr_not_allowed_by_pylint.read(\n )\n update = content.replace('true', 'True').replace('false', 'False')\n with open(\n file_path,\n 'w') as the_result_file_from_spark_for_write_and_abbr_not_allowed:\n the_result_file_from_spark_for_write_and_abbr_not_allowed.write(update)","def set_bool_attribute(self, id: str, b: Optional[bool]):\n self.set_attribute(id, None if not b else ConstInt(1))","def xmlWrite(self, xmlWriter, font, value, name, attrs):\n raise NotImplementedError(self)","def boolean_function(bool_variable):\n\tif bool_variable:\n\t\treturn \"The boolean variable is True\"\n\telse:\n\t\treturn \"The boolean variable is False\"","def write_flag(path): \r\n f = open(path, \"r+\")\r\n line = f.readlines()\r\n line[1] = 'flag = 1'\r\n s=''.join(line) \r\n f.seek(0)\r\n f.write(s)\r\n f.close()","def get_xml_bool_attribute(elem, attribute, default=None):\n value = elem.get(attribute, default)\n if value is None:\n raise XMLSchemaKeyError(attribute)\n elif value in ('true', '1') or value is True:\n return True\n elif value in ('false', '0') or value is False:\n return False\n else:\n raise XMLSchemaTypeError(\"an XML boolean value is required for attribute %r\" % attribute)","def save_to_xml(self, xwriter):\r\n\r\n xwriter.WriteStartElement(\"Profile\")\r\n xwriter.WriteAttributeString(\"Name\", self.Name)\r\n xwriter.WriteStartAttribute(\"Version\")\r\n xwriter.WriteValue(self.Version)\r\n xwriter.WriteEndAttribute()\r\n\r\n for var_name in self.__dict__:\r\n var_type = type(getattr(self, var_name))\r\n\r\n if var_type is str and var_name != \"Name\":\r\n self.write_string_to_xml(var_name, xwriter)\r\n\r\n elif var_type is bool:\r\n self.write_bool_to_xml(var_name, xwriter)\r\n\r\n elif var_type is dict:\r\n self.write_dict_to_xml(var_name, xwriter)\r\n\r\n elif var_type is list and var_name != \"ExcludeRules\":\r\n self.write_list_to_xml(var_name, xwriter)\r\n\r\n xwriter.WriteStartElement(\"ExcludeRules\")\r\n xwriter.WriteAttributeString(\"Operator\", self.ExcludeOperator)\r\n xwriter.WriteAttributeString(\"ExcludeMode\", self.ExcludeMode)\r\n for rule in self.ExcludeRules:\r\n if rule:\r\n rule.save_xml(xwriter)\r\n xwriter.WriteEndElement()\r\n \r\n xwriter.WriteEndElement()","def validate_boolean_attribute(tag, attribute_name, attribute_value):\n if not attribute_value:\n return\n\n if not isinstance(attribute_value, bool):\n raise AttributeError('<{tag}>: {attribute_name} attribute should be a '\n 'boolean value.'\n .format(tag=tag, attribute_name=attribute_name))","def bool_to_on_off(boolean: bool):\n if boolean:\n return \"on\"\n return \"off\"","def xmlwrite(self, doc, filename):\n pathname = os.path.join(self.session.session_dir, filename)\n f = open(pathname, \"w\")\n doc.writexml(writer=f, indent=\"\", addindent=\" \", newl=\"\\n\", encoding=\"UTF-8\")\n f.close()","def get_boolean_attribute_value(attrs, attr_name):\n return 1 if attrs.get(attr_name, 0) in [\"True\", \"1\"] else 0","def convertToString(boolean: bool) -> str:\n ...","def boolean(self, state, label=None):\n self.savepos()\n label = self._colorize(label, fg = \"base0\")\n\n msg = (self._colorize(\"☑\", fg = \"green\") if state else self._colorize(\"☒\", fg = \"red\")) + \" \" + label\n\n self.out.write(msg)\n self.restorepos()","def XMLWrite(self, one, two, three, four, five, six, seven, eight):\n filePath = \"/mnt/RAM/kanban.xml\"\n xmlFile = open(filePath, 'w')\n \n xmlFile.write('<kanban>\\n')\n xmlFile.write(' <n1>%s</n1>\\n' % one)\n xmlFile.write(' <n2>%s</n2>\\n' % two)\n xmlFile.write(' <n3>%s</n3>\\n' % three)\n xmlFile.write(' <n4>%s</n4>\\n' % four)\n xmlFile.write(' <n5>%s</n5>\\n' % five)\n xmlFile.write(' <n6>%s</n6>\\n' % six)\n xmlFile.write(' <n7>%s</n7>\\n' % seven)\n xmlFile.write(' <n8>%s</n8>\\n' % eight)\n xmlFile.write('</kanban>')","def XMLWrite(one, two, three, four, five, six, seven, eight):\n filePath = \"/mnt/RAM/kanban.xml\"\n xmlFile = open(filePath, 'w')\n\n xmlFile.write('<kanbanShelf>\\n')\n xmlFile.write(' <one>%s</one>\\n' % one)\n xmlFile.write(' <two>%s</two>\\n' % two)\n xmlFile.write(' <three>%s</three>\\n' % three)\n xmlFile.write(' <four>%s</four>\\n' % four)\n xmlFile.write(' <five>%s</five>\\n' % five)\n xmlFile.write(' <six>%s</six>\\n' % six)\n xmlFile.write(' <seven>%s</seven>\\n' % seven)\n xmlFile.write(' <eight>%s</eight>\\n' % eight)\n xmlFile.write('</kanbanShelf>')","def _format_bool_(value):\n\n from ocgis.util.helpers import format_bool\n\n return format_bool(value)","def to_es_bool(boolean_value):\n return '1' if boolean_value else '0'","def get_attr_bool(self, name, default=False):\n v = self.get_attr(name)\n if v is None:\n return default\n if v.lower() in [\"t\", \"true\", \"y\", \"yes\", \"1\"]:\n return True\n else:\n return False","def _set_bool(name, value, context):\n if name in os.environ:\n envval = os.environ.get(name).lower()\n if envval in [\"1\", \"true\", \"y\", \"yes\"]:\n context[name] = True\n elif envval in [\"0\", \"false\", \"n\", \"no\"]:\n context[name] = False\n else:\n raise ValueError(f\"{name} is a boolean, cannot match '{os.environ[name]}'\")\n\n _set_default(name, value, context)","def boolToString(aBoolean):\n if aBoolean:\n return \"true\"\n return \"false\"","def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n if attribute_name in (\"IllegalCharacters\", \"Months\"):\r\n write_empty = True\r\n dictionary = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for key in dictionary:\r\n if dictionary[key] or write_empty:\r\n xmlwriter.WriteStartElement(\"Item\")\r\n xmlwriter.WriteStartAttribute(\"Name\")\r\n xmlwriter.WriteValue(key)\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteStartAttribute(\"Value\")\r\n xmlwriter.WriteValue(dictionary[key])\r\n xmlwriter.WriteEndAttribute()\r\n xmlwriter.WriteEndElement()\r\n xmlwriter.WriteEndElement()","def bool_on_off(value):\n return 'on' if value else 'off'","def __boolToChar(self, boolean):\n\n if boolean:\n return '1'\n else:\n return '0'","def add_boolean(self, name, **kwargs):\n self.add(Flags.BooleanFlag(name, **kwargs))","def setbool(self, strcommand, value):\n command = ct.c_wchar_p(strcommand)\n value = ct.c_bool(value)\n self.lib.AT_SetBool(self.AT_H, command, value)","def testBoolValue(self):\n objectID = uuid4()\n user = createUser(u'username', u'password', u'User',\n u'user@example.com')\n namespace = createNamespace(user, u'name')\n tag = createTag(user, namespace, u'tag')\n self.store.add(TagValue(user.id, tag.id, objectID, True))","def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n attribute_list = getattr(self, attribute_name)\r\n xmlwriter.WriteStartElement(attribute_name)\r\n for item in attribute_list:\r\n if item or write_empty:\r\n xmlwriter.WriteElementString(\"Item\", item)\r\n xmlwriter.WriteEndElement()","def write_xml(self, filepath=None, escapeNewline=True, indent=False):\n\n if not filepath:\n filepath = self.mFilePath\n\n if indent:\n self.indent(self.tree.getroot())\n\n output = StringIO()\n\n self.tree.write(output, encoding=\"UTF-8\")\n\n outFile = open(filepath, \"w\")\n if escapeNewline:\n # we need to make sure newline 
 is written correctly\n print >> outFile, re.sub(\"###newline_escape###\", \"
\", output.getvalue())\n else:\n print >> outFile, output.getvalue()\n\n outFile.close","def write(self, filename):\n \n return self.model.write(filename,xml_declaration=True, encoding='utf-8')","def convertToBoolean(boolean: bool) -> bool:\n ...","def register_bool(self, name, short=None, default=None, group=None, help=None):\n self._register(name, self._parse_bool, short=short, default=default,\n group=group, help=help)","def forge_bool(value: bool) -> bytes:\n return b'\\xff' if value else b'\\x00'","def write_to_xml(filename, xmlelement):\n xmlpretty = prettify(xmlelement)\n with open(filename, 'w') as f:\n f.write(xmlpretty)","def boolean(self, boolean):\n\n self._boolean = boolean","def __init__(self, boolean_name, boolean_value):\n self._boolean_name = process_for_latex(boolean_name)\n self._boolean_value = boolean_value","def writeTypedClassadAttrToFile(fd, attr_name, attr_value):\n if isinstance(attr_value, (int, long, float)):\n # don't quote numeric values\n fd.write('%s = %s\\n' % (attr_name, attr_value))\n else:\n escaped_value = string.replace(string.replace(str(attr_value), '\"', '\\\\\"'), '\\n', '\\\\n')\n fd.write('%s = \"%s\"\\n' % (attr_name, escaped_value))","def export(self, value):\n \n self._export = bool(value)","def onoff(boolean):\n if boolean:\n return \"on\"\n else:\n return \"off\"","def _BoolOp(self, t):\n self.write(\"(\")\n s = \" %s \" % self.boolops[t.op.__class__]\n interleave(lambda: self.write(s), self.dispatch, t.values)\n self.write(\")\")","def to_xml_file(self, xml_file_path):\n s = self.to_xml()\n with open(xml_file_path, \"w+b\") as f:\n f.write(s)","def give_me_a_boolean():\n return True\n pass","def write_output_file(self, xml_text, xml_file):\n xml_fo = open(xml_file, 'w')\n xml_fo.write(xml_text+'</xml>')\n xml_fo.close()\n return","def saveSessionToXML(self, filename):\r\n xmlStr = self.createXMLStr()\r\n \r\n #Write to the file\r\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\r\n xmlFile = open(filename, 'w')\r\n xmlFile.write(xmlStr)\r\n xmlFile.close()","def save_file(self, force=False): # type: (bool) -> bool\n if self._modified or force:\n logging.info('Cyra is writing your config to %s' % self._file)\n\n with open(self._file, 'w') as f:\n f.write(self.export_toml())\n\n self._modified = False\n return True\n return False","def write(self, global_dep: GlobalDependencies) -> bool:","def save_config(logfolder, file_name = \"configuration\"):\n print(\"Save configuration to: \", logfolder)\n root = ET.Element(\"conf\")\n flg = ET.SubElement(root, \"flags\")\n \n flags_dict = FLAGS.__dict__['__flags']\n for f in flags_dict:\n #print f, flags_dict[f]\n ET.SubElement(flg, f, name=f).text = str(flags_dict[f])\n tree = ET.ElementTree(root)\n tree.write(os.path.join(logfolder,file_name+\".xml\"), encoding=\"us-ascii\", xml_declaration=True, method=\"xml\")","def write_to_xml(dictData, metadata, xmlfile):\n\tfout = codecs.open(xmlfile, 'w', 'utf-8')\n\tfout.write('<?xml version = \"1.0\" encoding = \"UTF-8\" standalone = \"no\" ?>\\n')\n\tfout.write('<?xml-stylesheet type=\"text/xsl\" href=\"maketable.xsl\"?>\\n')\n\tfout.write('<root>\\n')\n\tfout.write('<meta>\\n')\n\tfor key, value in metadata.items():\n\t\tfout.write('<' + key + '>' + value + '</' + key + '>\\n')\n\tfout.write('</meta>\\n')\n\tfout.write('<content>\\n')\n\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\n\t\txmlline = ''\n\t\txmlline += '<word><headword>' + hw + '</headword><meanings>'\n\t\tfor meaning in meanings:\n\t\t\txmlline += '<m>' + meaning + '</m>'\n\t\txmlline += '</meanings>'\n\t\txmlline += '<verse>'\n\t\tlines = verse.split('<BR>')\n\t\tfor line in lines:\n\t\t\txmlline += '<line>' + line + '</line>'\n\t\txmlline += '</verse>'\n\t\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\n\t\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\n\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\tfout.write(xmlline + '\\n')\n\t\txmlline = ''\n\tfout.write('</content>\\n</root>')\n\tfout.close()\n\n\t# Give some summary to the user\n\tprint('XML file generated. Success!')\n\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))","def boolToText(boolval):\n ret = libxml2mod.xmlBoolToText(boolval)\n return ret","def write(self, file_or_filename):\n etMap = revert(self)\n xmlTree = ET.ElementTree(etMap)\n xmlTree.write(file_or_filename)","def print_xml(tree, file):\n tree.write(file, encoding=\"utf-8\", xml_declaration=True)","def test_command_edit_info_boolean_flags():\n def f(inputfile):\n with tempfile.NamedTemporaryFile() as tmp:\n shutil.copy(inputfile, tmp.name)\n\n for flag in (\"write_protected\", \"synchronized\", \"cleaned\"):\n for true_value, false_value in ((\"1\", \"0\"),\n (\"yes\", \"no\"),\n (\"YES\", \"No\"),\n (\"true\", \"false\"),\n (\"tRuE\", \"FaLsE\")):\n wozardry.parse_args([\"edit\", \"-i\", \"%s:%s\" % (flag, true_value), tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[flag] == True\n wozardry.parse_args([\"edit\", \"-i\", \"%s:%s\" % (flag, false_value), tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[flag] == False\n f(kValid1)\n f(kValid2)","def to_bool(name, default=False):\n return as_bool(get(name), default=default)","def format_bool(b):\n return \"YES\" if b else \"NO\"","def saveSettings(self):\n e = constrain.saveSettings(self)\n e.attrib['status'] = ('true' if self.status else 'false')\n return e","def boolval(space, w_obj):\n return space.newbool(space.is_true(w_obj))","def to_xml(self, scene_dir: str) -> Tuple[Et.Element, bool]:\n raise NotImplementedError","def test_human_readable_boolean_true():\n true_value = True\n true_value_human_readable = util.get_human_readable_boolean(true_value)\n assert true_value_human_readable == \"Yes, it is!\"","def save_config(FLAGS, logfolder, file_name = \"configuration\"):\n print(\"Save configuration to: {}\".format(logfolder))\n root = ET.Element(\"conf\")\n flg = ET.SubElement(root, \"flags\")\n \n flags_dict=FLAGS.__dict__\n for f in sorted(flags_dict.keys()):\n # print f, flags_dict[f]\n e = ET.SubElement(flg, f, name=f) \n e.text = str(flags_dict[f])\n e.tail = \"\\n \"\n tree = ET.ElementTree(root)\n tree.write(os.path.join(logfolder,file_name+\".xml\"), encoding=\"us-ascii\", xml_declaration=True, method=\"xml\")","def _create_flag_file(self, content):\n if not os.path.exists(self.syspaths.flag_dir):\n try:\n os.makedirs(self.syspaths.flag_dir)\n except Exception:\n sys.stderr.write(\"Failed to create flag directory '%s': %s\\n\" %\n (self.syspaths.flag_dir, sys.exc_info()[1]))\n raise\n\n try:\n with open(self.syspaths.flag_file, \"w\") as f:\n f.write(content)\n except Exception:\n sys.stderr.write(\"Failed to create flag file '%s': %s\\n\" %\n (self.syspaths.flag_file, sys.exc_info()[1]))\n raise","def test_human_readable_boolean_false():\n # TODO: add a test case that follows the provided example","def get_bool(self, name, default=False):\n return self.get_as(self.parse_bool, name, default, value_type=bool)","def _get_bool(element, name, context, default=None):\n\n value = element.get(name)\n try:\n value = int(value)\n except:\n value = default\n\n _assert(value is not None,\n \"Missing or invalid boolean value of '{0}.{1}'.\".format(context,\n name))\n\n return bool(value)","def __bool__(self):\n return _libsbml.string___bool__(self)","def boolean_flag(parser, name, default=False, help=None):\n dest = name.replace('-', '_')\n parser.add_argument(\"--\" + name, action=\"store_true\", default=default, dest=dest, help=help)\n parser.add_argument(\"--no-\" + name, action=\"store_false\", dest=dest)","def aria_bool(value: Optional[bool]) -> Optional[str]:\n\n if value is None:\n return None\n elif value is True:\n return \"true\"\n elif value is False:\n return \"false\"\n else:\n raise ValueError(str(value))","def writexml(file):\n OUTFILE=open(file,\"w\")\n doc = xml.dom.minidom.Document()\n\n # Create the <dec_reg_list> base element\n decl_reg_list = doc.createElement(\"decl_reg_list\")\n doc.appendChild(decl_reg_list)\n\n regname_old=\"\"\n rows.pop(0)\n for row in rows:\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\n if regname != regname_old:\n # Create the register element\n register = doc.createElement(\"register\")\n register.setAttribute(\"name\", regname)\n register.setAttribute(\"offset\", offset)\n if default != \"\" : register.setAttribute(\"default\", default)\n register.setAttribute(\"type\", regtype)\n if expose_reg == \"1\": register.setAttribute(\"usr\", expose_reg)\n if depth != \"\": register.setAttribute(\"size\", depth)\n if incsz != \"\": register.setAttribute(\"incsz\", incsz)\n text = doc.createTextNode(regdesc)\n register.appendChild(text)\n decl_reg_list.appendChild(register)\n \n # Create the field element\n if bitname != \"\":\n field = doc.createElement(\"field\")\n field.setAttribute(\"name\", bitname)\n if loc !=\"\": field.setAttribute(\"loc\", addcolon(loc))\n if bittype != \"\": field.setAttribute(\"type\", bittype)\n if bitdesc != \"\":\n text = doc.createTextNode(bitdesc)\n field.appendChild(text)\n register.appendChild(field)\n regname_old = regname\n\n\n # Print our newly created XML\n #print doc.toprettyxml(indent=\" \")\n #OUTFILE.write(doc.saveXML(decl_reg_list))\n OUTFILE.write(doc.toprettyxml(indent=\" \"))\n OUTFILE.close()","def to_bool(data, true_label, **kwargs):\n return Component(\n \"ToBool\",\n arguments={\n 'data': Component.of(data),\n 'true_label': Component.of(true_label)\n },\n options={\n \n },\n constraints=kwargs)","def export_trips_xml(self, filepath=None, encoding='UTF-8',\n ids_vtype_exclude=[]):\n if filepath is None:\n filepath = self.get_tripfilepath()\n print 'export_trips_xml', filepath\n try:\n fd = open(filepath, 'w')\n except:\n print 'WARNING in write_obj_to_xml: could not open', filepath\n return False\n\n xmltag, xmltag_item, attrname_id = self.xmltag\n fd.write('<?xml version=\"1.0\" encoding=\"%s\"?>\\n' % encoding)\n fd.write(xm.begin(xmltag))\n indent = 2\n\n ids_trip = self.times_depart.get_ids_sorted()\n ids_vtype = self.ids_vtype[ids_trip]\n #ids_vtypes_exclude = self.ids_vtype.get_ids_from_indices(vtypes_exclude)\n\n inds_selected = np.ones(len(ids_vtype), np.bool)\n for id_vtype in ids_vtype_exclude:\n inds_selected[ids_vtype == id_vtype] = False\n ids_trip_selected = ids_trip[inds_selected]\n ids_vtype_selected = set(ids_vtype[inds_selected])\n #ids_vtypes_selected = set(ids_vtypes).difference(ids_vtypes_exclude)\n\n self.parent.vtypes.write_xml(fd, indent=indent,\n ids=ids_vtype_selected,\n is_print_begin_end=False)\n\n self.write_xml(fd, indent=indent,\n ids=ids_trip_selected,\n attrconfigs_excluded=[self.routes, self.ids_routes],\n is_print_begin_end=False)\n\n fd.write(xm.end(xmltag))\n fd.close()\n return filepath","def booltostr(value: bool) -> Any:\n\n if isinstance(value, bool):\n return \"True\" if value else \"False\"\n return value","def _true_false_to_string(v):\n \n if not isinstance(v,bool):\n raise InstrumentParameterException('Value %s is not a bool.' % str(v))\n if v:\n return 'y'\n else:\n return 'n'","def _bool_encode(self, d):\n for k, v in d.items():\n if isinstance(v, bool):\n d[k] = str(v).lower()\n \n return d","def explicit_bool(value: bool) -> bool:\n return value","def as_bool(self):\n return InvenTree.helpers.str2bool(self.value)","def get_bool2(self):\n pass","def is_bool(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_bool)","def writable(self):\n return True","def setWriteComment(*args):\n return _libsbml.XMLOutputStream_setWriteComment(*args)","def test_boolstyle_round_trip(self):\n def do_round_trip(trueval, falseval, invalid=False):\n logger.debug('Exporting to csv file: {} with bool style {},{}'.format(tempfile.name, trueval, falseval))\n _, err, _ = self.run_cqlsh(cmds=\"COPY ks.testbooleans TO '{}' WITH BOOLSTYLE='{}, {}'\"\n .format(tempfile.name, trueval, falseval))\n if invalid:\n expected_err = \"Invalid boolean styles [{}, {}]\".format(\n ', '.join([\"'{}'\".format(s.strip()) for s in trueval.split(',')]),\n ', '.join([\"'{}'\".format(s.strip()) for s in falseval.split(',')]))\n assert expected_err in err\n return\n\n tempfile_rows_as_list = list(csv_rows(tempfile.name))\n assert [['0', falseval], ['1', trueval]] == sorted(tempfile_rows_as_list)\n exported_results = list(self.session.execute(\"SELECT * FROM testbooleans\"))\n\n logger.debug('Importing from csv file: {}'.format(tempfile.name))\n self.session.execute('TRUNCATE ks.testbooleans')\n self.run_cqlsh(cmds=\"COPY ks.testbooleans FROM '{}' WITH BOOLSTYLE='{}, {}'\"\n .format(tempfile.name, trueval, falseval))\n\n imported_results = list(self.session.execute(\"SELECT * FROM testbooleans\"))\n assert sorted(exported_results) == sorted(imported_results)\n\n self.prepare()\n self.session.execute(\"\"\"\n CREATE TABLE testbooleans (\n a int PRIMARY KEY,\n b boolean\n )\"\"\")\n\n insert_statement = self.session.prepare(\"INSERT INTO testbooleans (a, b) VALUES (?, ?)\")\n self.session.execute(insert_statement, [0, False])\n self.session.execute(insert_statement, [1, True])\n tempfile = self.get_temp_file()\n\n do_round_trip('True', 'False')\n do_round_trip('TRUE', 'FALSE')\n do_round_trip('yes', 'no')\n do_round_trip('1', '0')\n do_round_trip('TRUE', 'no')\n do_round_trip('True', '0')\n\n do_round_trip('TRUE', 'TRUE', invalid=True)\n do_round_trip('TRUE', '', invalid=True)\n do_round_trip('', 'FALSE', invalid=True)\n do_round_trip('', '', invalid=True)\n do_round_trip('yes, no', 'maybe', invalid=True)","def pretty_bool(value):\r\n bool_dict = [True, \"True\", \"true\", \"T\", \"t\", \"1\"]\r\n return value in bool_dict","def getBool(string):\n return (True)","def get_truefalse(truefalse):\n return 'True' if truefalse else 'False'","def writetif(self,outputname,):\n pass","def writexml(self, writer, indent=\"\", add_indent=\"\", new_line=\"\"):\n writer.write(indent + \"<\" + self.tagName)\n attrs = self._get_attributes()\n\n for a_name in attrs.keys():\n writer.write(\" %s=\\\"\" % a_name)\n self.write_data(writer, attrs[a_name].value)\n writer.write(\"\\\"\")\n if self.childNodes:\n writer.write(\">\")\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\n self.childNodes[0].writexml(writer, '', '', '')\n else:\n writer.write(new_line)\n for node in self.childNodes:\n node.writexml(writer, indent + add_indent, add_indent, new_line)\n writer.write(indent)\n writer.write(\"</%s>%s\" % (self.tagName, new_line))\n else:\n writer.write(\"/>%s\" % new_line)","def f_boolean(node, pos, size, context, v):\n if xpath.tools.nodesetp(v):\n return len(v) > 0\n elif xpath.tools.numberp(v):\n if v == 0 or v != v:\n return False\n return True\n elif xpath.tools.stringp(v):\n return v != ''\n\n return v","def get_string(self):\n boolean_value_string = str(bool(self._boolean_value)).lower()\n return BOOLEAN_TEMPLATE.substitute(\n boolean_name = str(self._boolean_name),\n boolean_value = boolean_value_string)"],"string":"[\n \"def writeAttribute(self, *args):\\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\\n\\n\\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)\",\n \"def writeAttributeBool(self, *args):\\n return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\",\n \"def write(writer: BitStreamWriter, value: bool) -> None:\\n\\n writer.writeBool(value)\",\n \"def write_boolean(self, boolean: bool) -> None:\\n self.write(bytearray([bool(boolean)]))\",\n \"def _writeBool(self, val):\\n self.__writeValue(self.boolFormat, val)\",\n \"def writeBoolean(self, value: bool):\\n self.writeByte(1 if value else 0)\",\n \"def write_bool(self, b: bool) -> None:\\n self.buffer += struct.pack(\\\"<?\\\", b)\",\n \"def bool_attr(attr):\\n if attr.lower() == \\\"true\\\":\\n val = True\\n elif attr.lower() == \\\"false\\\":\\n val = False\\n else:\\n raise EzXMLError(\\\"Must be \\\"\\\\\\n \\\"'true' or 'false'. Not %s\\\" % (attr))\\n return val\",\n \"def htmlIsBooleanAttr(name):\\n ret = libxml2mod.htmlIsBooleanAttr(name)\\n return ret\",\n \"def set(self, attr, value=True):\\n if type(value) == bool:\\n self.__dict__['_'+attr] = value\\n print attr, \\\"set to\\\", value\\n else:\\n print 'Value must be a bool, either \\\"True\\\" or \\\"False\\\" (no quotes)!'\",\n \"def set_boolean(x):\\n\\n if x:\\n return \\\"True\\\"\\n else:\\n return \\\"False\\\"\",\n \"def set_boolean(dict, name, elem):\\n node = elem.find(name)\\n if node is not None:\\n if node.text.lower() == 'yes':\\n dict[name] = True\\n elif node.text.lower() == 'no':\\n dict[name] = False\",\n \"def write_xml(self, xmlfile):\\n system.xml.write_file(xmlfile, self.status, 'status')\",\n \"def saveState(self):\\n e = xml.Element(self.type)\\n e.attrib['lastUpdate'] = str(clock.now())\\n e.attrib['name'] = self.name\\n #e.attrib['status'] = ('true' if self.status else 'false')\\n return e\",\n \"def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):\\r\\n string = getattr(self, attribute_name)\\r\\n if string or write_empty:\\r\\n xmlwriter.WriteElementString(attribute_name, string)\",\n \"def update_bool(file_path):\\n with open(\\n file_path, 'r'\\n ) as the_result_file_from_spark_for_read_and_abbr_not_allowed_by_pylint:\\n content = the_result_file_from_spark_for_read_and_abbr_not_allowed_by_pylint.read(\\n )\\n update = content.replace('true', 'True').replace('false', 'False')\\n with open(\\n file_path,\\n 'w') as the_result_file_from_spark_for_write_and_abbr_not_allowed:\\n the_result_file_from_spark_for_write_and_abbr_not_allowed.write(update)\",\n \"def set_bool_attribute(self, id: str, b: Optional[bool]):\\n self.set_attribute(id, None if not b else ConstInt(1))\",\n \"def xmlWrite(self, xmlWriter, font, value, name, attrs):\\n raise NotImplementedError(self)\",\n \"def boolean_function(bool_variable):\\n\\tif bool_variable:\\n\\t\\treturn \\\"The boolean variable is True\\\"\\n\\telse:\\n\\t\\treturn \\\"The boolean variable is False\\\"\",\n \"def write_flag(path): \\r\\n f = open(path, \\\"r+\\\")\\r\\n line = f.readlines()\\r\\n line[1] = 'flag = 1'\\r\\n s=''.join(line) \\r\\n f.seek(0)\\r\\n f.write(s)\\r\\n f.close()\",\n \"def get_xml_bool_attribute(elem, attribute, default=None):\\n value = elem.get(attribute, default)\\n if value is None:\\n raise XMLSchemaKeyError(attribute)\\n elif value in ('true', '1') or value is True:\\n return True\\n elif value in ('false', '0') or value is False:\\n return False\\n else:\\n raise XMLSchemaTypeError(\\\"an XML boolean value is required for attribute %r\\\" % attribute)\",\n \"def save_to_xml(self, xwriter):\\r\\n\\r\\n xwriter.WriteStartElement(\\\"Profile\\\")\\r\\n xwriter.WriteAttributeString(\\\"Name\\\", self.Name)\\r\\n xwriter.WriteStartAttribute(\\\"Version\\\")\\r\\n xwriter.WriteValue(self.Version)\\r\\n xwriter.WriteEndAttribute()\\r\\n\\r\\n for var_name in self.__dict__:\\r\\n var_type = type(getattr(self, var_name))\\r\\n\\r\\n if var_type is str and var_name != \\\"Name\\\":\\r\\n self.write_string_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is bool:\\r\\n self.write_bool_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is dict:\\r\\n self.write_dict_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is list and var_name != \\\"ExcludeRules\\\":\\r\\n self.write_list_to_xml(var_name, xwriter)\\r\\n\\r\\n xwriter.WriteStartElement(\\\"ExcludeRules\\\")\\r\\n xwriter.WriteAttributeString(\\\"Operator\\\", self.ExcludeOperator)\\r\\n xwriter.WriteAttributeString(\\\"ExcludeMode\\\", self.ExcludeMode)\\r\\n for rule in self.ExcludeRules:\\r\\n if rule:\\r\\n rule.save_xml(xwriter)\\r\\n xwriter.WriteEndElement()\\r\\n \\r\\n xwriter.WriteEndElement()\",\n \"def validate_boolean_attribute(tag, attribute_name, attribute_value):\\n if not attribute_value:\\n return\\n\\n if not isinstance(attribute_value, bool):\\n raise AttributeError('<{tag}>: {attribute_name} attribute should be a '\\n 'boolean value.'\\n .format(tag=tag, attribute_name=attribute_name))\",\n \"def bool_to_on_off(boolean: bool):\\n if boolean:\\n return \\\"on\\\"\\n return \\\"off\\\"\",\n \"def xmlwrite(self, doc, filename):\\n pathname = os.path.join(self.session.session_dir, filename)\\n f = open(pathname, \\\"w\\\")\\n doc.writexml(writer=f, indent=\\\"\\\", addindent=\\\" \\\", newl=\\\"\\\\n\\\", encoding=\\\"UTF-8\\\")\\n f.close()\",\n \"def get_boolean_attribute_value(attrs, attr_name):\\n return 1 if attrs.get(attr_name, 0) in [\\\"True\\\", \\\"1\\\"] else 0\",\n \"def convertToString(boolean: bool) -> str:\\n ...\",\n \"def boolean(self, state, label=None):\\n self.savepos()\\n label = self._colorize(label, fg = \\\"base0\\\")\\n\\n msg = (self._colorize(\\\"☑\\\", fg = \\\"green\\\") if state else self._colorize(\\\"☒\\\", fg = \\\"red\\\")) + \\\" \\\" + label\\n\\n self.out.write(msg)\\n self.restorepos()\",\n \"def XMLWrite(self, one, two, three, four, five, six, seven, eight):\\n filePath = \\\"/mnt/RAM/kanban.xml\\\"\\n xmlFile = open(filePath, 'w')\\n \\n xmlFile.write('<kanban>\\\\n')\\n xmlFile.write(' <n1>%s</n1>\\\\n' % one)\\n xmlFile.write(' <n2>%s</n2>\\\\n' % two)\\n xmlFile.write(' <n3>%s</n3>\\\\n' % three)\\n xmlFile.write(' <n4>%s</n4>\\\\n' % four)\\n xmlFile.write(' <n5>%s</n5>\\\\n' % five)\\n xmlFile.write(' <n6>%s</n6>\\\\n' % six)\\n xmlFile.write(' <n7>%s</n7>\\\\n' % seven)\\n xmlFile.write(' <n8>%s</n8>\\\\n' % eight)\\n xmlFile.write('</kanban>')\",\n \"def XMLWrite(one, two, three, four, five, six, seven, eight):\\n filePath = \\\"/mnt/RAM/kanban.xml\\\"\\n xmlFile = open(filePath, 'w')\\n\\n xmlFile.write('<kanbanShelf>\\\\n')\\n xmlFile.write(' <one>%s</one>\\\\n' % one)\\n xmlFile.write(' <two>%s</two>\\\\n' % two)\\n xmlFile.write(' <three>%s</three>\\\\n' % three)\\n xmlFile.write(' <four>%s</four>\\\\n' % four)\\n xmlFile.write(' <five>%s</five>\\\\n' % five)\\n xmlFile.write(' <six>%s</six>\\\\n' % six)\\n xmlFile.write(' <seven>%s</seven>\\\\n' % seven)\\n xmlFile.write(' <eight>%s</eight>\\\\n' % eight)\\n xmlFile.write('</kanbanShelf>')\",\n \"def _format_bool_(value):\\n\\n from ocgis.util.helpers import format_bool\\n\\n return format_bool(value)\",\n \"def to_es_bool(boolean_value):\\n return '1' if boolean_value else '0'\",\n \"def get_attr_bool(self, name, default=False):\\n v = self.get_attr(name)\\n if v is None:\\n return default\\n if v.lower() in [\\\"t\\\", \\\"true\\\", \\\"y\\\", \\\"yes\\\", \\\"1\\\"]:\\n return True\\n else:\\n return False\",\n \"def _set_bool(name, value, context):\\n if name in os.environ:\\n envval = os.environ.get(name).lower()\\n if envval in [\\\"1\\\", \\\"true\\\", \\\"y\\\", \\\"yes\\\"]:\\n context[name] = True\\n elif envval in [\\\"0\\\", \\\"false\\\", \\\"n\\\", \\\"no\\\"]:\\n context[name] = False\\n else:\\n raise ValueError(f\\\"{name} is a boolean, cannot match '{os.environ[name]}'\\\")\\n\\n _set_default(name, value, context)\",\n \"def boolToString(aBoolean):\\n if aBoolean:\\n return \\\"true\\\"\\n return \\\"false\\\"\",\n \"def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\\r\\n if attribute_name in (\\\"IllegalCharacters\\\", \\\"Months\\\"):\\r\\n write_empty = True\\r\\n dictionary = getattr(self, attribute_name)\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n for key in dictionary:\\r\\n if dictionary[key] or write_empty:\\r\\n xmlwriter.WriteStartElement(\\\"Item\\\")\\r\\n xmlwriter.WriteStartAttribute(\\\"Name\\\")\\r\\n xmlwriter.WriteValue(key)\\r\\n xmlwriter.WriteEndAttribute()\\r\\n xmlwriter.WriteStartAttribute(\\\"Value\\\")\\r\\n xmlwriter.WriteValue(dictionary[key])\\r\\n xmlwriter.WriteEndAttribute()\\r\\n xmlwriter.WriteEndElement()\\r\\n xmlwriter.WriteEndElement()\",\n \"def bool_on_off(value):\\n return 'on' if value else 'off'\",\n \"def __boolToChar(self, boolean):\\n\\n if boolean:\\n return '1'\\n else:\\n return '0'\",\n \"def add_boolean(self, name, **kwargs):\\n self.add(Flags.BooleanFlag(name, **kwargs))\",\n \"def setbool(self, strcommand, value):\\n command = ct.c_wchar_p(strcommand)\\n value = ct.c_bool(value)\\n self.lib.AT_SetBool(self.AT_H, command, value)\",\n \"def testBoolValue(self):\\n objectID = uuid4()\\n user = createUser(u'username', u'password', u'User',\\n u'user@example.com')\\n namespace = createNamespace(user, u'name')\\n tag = createTag(user, namespace, u'tag')\\n self.store.add(TagValue(user.id, tag.id, objectID, True))\",\n \"def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):\\r\\n attribute_list = getattr(self, attribute_name)\\r\\n xmlwriter.WriteStartElement(attribute_name)\\r\\n for item in attribute_list:\\r\\n if item or write_empty:\\r\\n xmlwriter.WriteElementString(\\\"Item\\\", item)\\r\\n xmlwriter.WriteEndElement()\",\n \"def write_xml(self, filepath=None, escapeNewline=True, indent=False):\\n\\n if not filepath:\\n filepath = self.mFilePath\\n\\n if indent:\\n self.indent(self.tree.getroot())\\n\\n output = StringIO()\\n\\n self.tree.write(output, encoding=\\\"UTF-8\\\")\\n\\n outFile = open(filepath, \\\"w\\\")\\n if escapeNewline:\\n # we need to make sure newline 
 is written correctly\\n print >> outFile, re.sub(\\\"###newline_escape###\\\", \\\"
\\\", output.getvalue())\\n else:\\n print >> outFile, output.getvalue()\\n\\n outFile.close\",\n \"def write(self, filename):\\n \\n return self.model.write(filename,xml_declaration=True, encoding='utf-8')\",\n \"def convertToBoolean(boolean: bool) -> bool:\\n ...\",\n \"def register_bool(self, name, short=None, default=None, group=None, help=None):\\n self._register(name, self._parse_bool, short=short, default=default,\\n group=group, help=help)\",\n \"def forge_bool(value: bool) -> bytes:\\n return b'\\\\xff' if value else b'\\\\x00'\",\n \"def write_to_xml(filename, xmlelement):\\n xmlpretty = prettify(xmlelement)\\n with open(filename, 'w') as f:\\n f.write(xmlpretty)\",\n \"def boolean(self, boolean):\\n\\n self._boolean = boolean\",\n \"def __init__(self, boolean_name, boolean_value):\\n self._boolean_name = process_for_latex(boolean_name)\\n self._boolean_value = boolean_value\",\n \"def writeTypedClassadAttrToFile(fd, attr_name, attr_value):\\n if isinstance(attr_value, (int, long, float)):\\n # don't quote numeric values\\n fd.write('%s = %s\\\\n' % (attr_name, attr_value))\\n else:\\n escaped_value = string.replace(string.replace(str(attr_value), '\\\"', '\\\\\\\\\\\"'), '\\\\n', '\\\\\\\\n')\\n fd.write('%s = \\\"%s\\\"\\\\n' % (attr_name, escaped_value))\",\n \"def export(self, value):\\n \\n self._export = bool(value)\",\n \"def onoff(boolean):\\n if boolean:\\n return \\\"on\\\"\\n else:\\n return \\\"off\\\"\",\n \"def _BoolOp(self, t):\\n self.write(\\\"(\\\")\\n s = \\\" %s \\\" % self.boolops[t.op.__class__]\\n interleave(lambda: self.write(s), self.dispatch, t.values)\\n self.write(\\\")\\\")\",\n \"def to_xml_file(self, xml_file_path):\\n s = self.to_xml()\\n with open(xml_file_path, \\\"w+b\\\") as f:\\n f.write(s)\",\n \"def give_me_a_boolean():\\n return True\\n pass\",\n \"def write_output_file(self, xml_text, xml_file):\\n xml_fo = open(xml_file, 'w')\\n xml_fo.write(xml_text+'</xml>')\\n xml_fo.close()\\n return\",\n \"def saveSessionToXML(self, filename):\\r\\n xmlStr = self.createXMLStr()\\r\\n \\r\\n #Write to the file\\r\\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\\r\\n xmlFile = open(filename, 'w')\\r\\n xmlFile.write(xmlStr)\\r\\n xmlFile.close()\",\n \"def save_file(self, force=False): # type: (bool) -> bool\\n if self._modified or force:\\n logging.info('Cyra is writing your config to %s' % self._file)\\n\\n with open(self._file, 'w') as f:\\n f.write(self.export_toml())\\n\\n self._modified = False\\n return True\\n return False\",\n \"def write(self, global_dep: GlobalDependencies) -> bool:\",\n \"def save_config(logfolder, file_name = \\\"configuration\\\"):\\n print(\\\"Save configuration to: \\\", logfolder)\\n root = ET.Element(\\\"conf\\\")\\n flg = ET.SubElement(root, \\\"flags\\\")\\n \\n flags_dict = FLAGS.__dict__['__flags']\\n for f in flags_dict:\\n #print f, flags_dict[f]\\n ET.SubElement(flg, f, name=f).text = str(flags_dict[f])\\n tree = ET.ElementTree(root)\\n tree.write(os.path.join(logfolder,file_name+\\\".xml\\\"), encoding=\\\"us-ascii\\\", xml_declaration=True, method=\\\"xml\\\")\",\n \"def write_to_xml(dictData, metadata, xmlfile):\\n\\tfout = codecs.open(xmlfile, 'w', 'utf-8')\\n\\tfout.write('<?xml version = \\\"1.0\\\" encoding = \\\"UTF-8\\\" standalone = \\\"no\\\" ?>\\\\n')\\n\\tfout.write('<?xml-stylesheet type=\\\"text/xsl\\\" href=\\\"maketable.xsl\\\"?>\\\\n')\\n\\tfout.write('<root>\\\\n')\\n\\tfout.write('<meta>\\\\n')\\n\\tfor key, value in metadata.items():\\n\\t\\tfout.write('<' + key + '>' + value + '</' + key + '>\\\\n')\\n\\tfout.write('</meta>\\\\n')\\n\\tfout.write('<content>\\\\n')\\n\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dictData:\\n\\t\\txmlline = ''\\n\\t\\txmlline += '<word><headword>' + hw + '</headword><meanings>'\\n\\t\\tfor meaning in meanings:\\n\\t\\t\\txmlline += '<m>' + meaning + '</m>'\\n\\t\\txmlline += '</meanings>'\\n\\t\\txmlline += '<verse>'\\n\\t\\tlines = verse.split('<BR>')\\n\\t\\tfor line in lines:\\n\\t\\t\\txmlline += '<line>' + line + '</line>'\\n\\t\\txmlline += '</verse>'\\n\\t\\txmlline += '<verseNumber>' + verseNumDetails + '</verseNumber>'\\n\\t\\txmlline += '<pageNumber>' + pageNumDetails + '</pageNumber></word>'\\n\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\tfout.write(xmlline + '\\\\n')\\n\\t\\txmlline = ''\\n\\tfout.write('</content>\\\\n</root>')\\n\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('XML file generated. Success!')\\n\\tprint('{} metadata lines and {} content lines written to XML file.'.format(len(metadata), len(dictData)))\",\n \"def boolToText(boolval):\\n ret = libxml2mod.xmlBoolToText(boolval)\\n return ret\",\n \"def write(self, file_or_filename):\\n etMap = revert(self)\\n xmlTree = ET.ElementTree(etMap)\\n xmlTree.write(file_or_filename)\",\n \"def print_xml(tree, file):\\n tree.write(file, encoding=\\\"utf-8\\\", xml_declaration=True)\",\n \"def test_command_edit_info_boolean_flags():\\n def f(inputfile):\\n with tempfile.NamedTemporaryFile() as tmp:\\n shutil.copy(inputfile, tmp.name)\\n\\n for flag in (\\\"write_protected\\\", \\\"synchronized\\\", \\\"cleaned\\\"):\\n for true_value, false_value in ((\\\"1\\\", \\\"0\\\"),\\n (\\\"yes\\\", \\\"no\\\"),\\n (\\\"YES\\\", \\\"No\\\"),\\n (\\\"true\\\", \\\"false\\\"),\\n (\\\"tRuE\\\", \\\"FaLsE\\\")):\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"%s:%s\\\" % (flag, true_value), tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[flag] == True\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"%s:%s\\\" % (flag, false_value), tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[flag] == False\\n f(kValid1)\\n f(kValid2)\",\n \"def to_bool(name, default=False):\\n return as_bool(get(name), default=default)\",\n \"def format_bool(b):\\n return \\\"YES\\\" if b else \\\"NO\\\"\",\n \"def saveSettings(self):\\n e = constrain.saveSettings(self)\\n e.attrib['status'] = ('true' if self.status else 'false')\\n return e\",\n \"def boolval(space, w_obj):\\n return space.newbool(space.is_true(w_obj))\",\n \"def to_xml(self, scene_dir: str) -> Tuple[Et.Element, bool]:\\n raise NotImplementedError\",\n \"def test_human_readable_boolean_true():\\n true_value = True\\n true_value_human_readable = util.get_human_readable_boolean(true_value)\\n assert true_value_human_readable == \\\"Yes, it is!\\\"\",\n \"def save_config(FLAGS, logfolder, file_name = \\\"configuration\\\"):\\n print(\\\"Save configuration to: {}\\\".format(logfolder))\\n root = ET.Element(\\\"conf\\\")\\n flg = ET.SubElement(root, \\\"flags\\\")\\n \\n flags_dict=FLAGS.__dict__\\n for f in sorted(flags_dict.keys()):\\n # print f, flags_dict[f]\\n e = ET.SubElement(flg, f, name=f) \\n e.text = str(flags_dict[f])\\n e.tail = \\\"\\\\n \\\"\\n tree = ET.ElementTree(root)\\n tree.write(os.path.join(logfolder,file_name+\\\".xml\\\"), encoding=\\\"us-ascii\\\", xml_declaration=True, method=\\\"xml\\\")\",\n \"def _create_flag_file(self, content):\\n if not os.path.exists(self.syspaths.flag_dir):\\n try:\\n os.makedirs(self.syspaths.flag_dir)\\n except Exception:\\n sys.stderr.write(\\\"Failed to create flag directory '%s': %s\\\\n\\\" %\\n (self.syspaths.flag_dir, sys.exc_info()[1]))\\n raise\\n\\n try:\\n with open(self.syspaths.flag_file, \\\"w\\\") as f:\\n f.write(content)\\n except Exception:\\n sys.stderr.write(\\\"Failed to create flag file '%s': %s\\\\n\\\" %\\n (self.syspaths.flag_file, sys.exc_info()[1]))\\n raise\",\n \"def test_human_readable_boolean_false():\\n # TODO: add a test case that follows the provided example\",\n \"def get_bool(self, name, default=False):\\n return self.get_as(self.parse_bool, name, default, value_type=bool)\",\n \"def _get_bool(element, name, context, default=None):\\n\\n value = element.get(name)\\n try:\\n value = int(value)\\n except:\\n value = default\\n\\n _assert(value is not None,\\n \\\"Missing or invalid boolean value of '{0}.{1}'.\\\".format(context,\\n name))\\n\\n return bool(value)\",\n \"def __bool__(self):\\n return _libsbml.string___bool__(self)\",\n \"def boolean_flag(parser, name, default=False, help=None):\\n dest = name.replace('-', '_')\\n parser.add_argument(\\\"--\\\" + name, action=\\\"store_true\\\", default=default, dest=dest, help=help)\\n parser.add_argument(\\\"--no-\\\" + name, action=\\\"store_false\\\", dest=dest)\",\n \"def aria_bool(value: Optional[bool]) -> Optional[str]:\\n\\n if value is None:\\n return None\\n elif value is True:\\n return \\\"true\\\"\\n elif value is False:\\n return \\\"false\\\"\\n else:\\n raise ValueError(str(value))\",\n \"def writexml(file):\\n OUTFILE=open(file,\\\"w\\\")\\n doc = xml.dom.minidom.Document()\\n\\n # Create the <dec_reg_list> base element\\n decl_reg_list = doc.createElement(\\\"decl_reg_list\\\")\\n doc.appendChild(decl_reg_list)\\n\\n regname_old=\\\"\\\"\\n rows.pop(0)\\n for row in rows:\\n (regdesc,regname,offset,default,regtype,expose_reg,depth,incsz,bitdesc,bitname,loc,bittype)= row\\n if regname != regname_old:\\n # Create the register element\\n register = doc.createElement(\\\"register\\\")\\n register.setAttribute(\\\"name\\\", regname)\\n register.setAttribute(\\\"offset\\\", offset)\\n if default != \\\"\\\" : register.setAttribute(\\\"default\\\", default)\\n register.setAttribute(\\\"type\\\", regtype)\\n if expose_reg == \\\"1\\\": register.setAttribute(\\\"usr\\\", expose_reg)\\n if depth != \\\"\\\": register.setAttribute(\\\"size\\\", depth)\\n if incsz != \\\"\\\": register.setAttribute(\\\"incsz\\\", incsz)\\n text = doc.createTextNode(regdesc)\\n register.appendChild(text)\\n decl_reg_list.appendChild(register)\\n \\n # Create the field element\\n if bitname != \\\"\\\":\\n field = doc.createElement(\\\"field\\\")\\n field.setAttribute(\\\"name\\\", bitname)\\n if loc !=\\\"\\\": field.setAttribute(\\\"loc\\\", addcolon(loc))\\n if bittype != \\\"\\\": field.setAttribute(\\\"type\\\", bittype)\\n if bitdesc != \\\"\\\":\\n text = doc.createTextNode(bitdesc)\\n field.appendChild(text)\\n register.appendChild(field)\\n regname_old = regname\\n\\n\\n # Print our newly created XML\\n #print doc.toprettyxml(indent=\\\" \\\")\\n #OUTFILE.write(doc.saveXML(decl_reg_list))\\n OUTFILE.write(doc.toprettyxml(indent=\\\" \\\"))\\n OUTFILE.close()\",\n \"def to_bool(data, true_label, **kwargs):\\n return Component(\\n \\\"ToBool\\\",\\n arguments={\\n 'data': Component.of(data),\\n 'true_label': Component.of(true_label)\\n },\\n options={\\n \\n },\\n constraints=kwargs)\",\n \"def export_trips_xml(self, filepath=None, encoding='UTF-8',\\n ids_vtype_exclude=[]):\\n if filepath is None:\\n filepath = self.get_tripfilepath()\\n print 'export_trips_xml', filepath\\n try:\\n fd = open(filepath, 'w')\\n except:\\n print 'WARNING in write_obj_to_xml: could not open', filepath\\n return False\\n\\n xmltag, xmltag_item, attrname_id = self.xmltag\\n fd.write('<?xml version=\\\"1.0\\\" encoding=\\\"%s\\\"?>\\\\n' % encoding)\\n fd.write(xm.begin(xmltag))\\n indent = 2\\n\\n ids_trip = self.times_depart.get_ids_sorted()\\n ids_vtype = self.ids_vtype[ids_trip]\\n #ids_vtypes_exclude = self.ids_vtype.get_ids_from_indices(vtypes_exclude)\\n\\n inds_selected = np.ones(len(ids_vtype), np.bool)\\n for id_vtype in ids_vtype_exclude:\\n inds_selected[ids_vtype == id_vtype] = False\\n ids_trip_selected = ids_trip[inds_selected]\\n ids_vtype_selected = set(ids_vtype[inds_selected])\\n #ids_vtypes_selected = set(ids_vtypes).difference(ids_vtypes_exclude)\\n\\n self.parent.vtypes.write_xml(fd, indent=indent,\\n ids=ids_vtype_selected,\\n is_print_begin_end=False)\\n\\n self.write_xml(fd, indent=indent,\\n ids=ids_trip_selected,\\n attrconfigs_excluded=[self.routes, self.ids_routes],\\n is_print_begin_end=False)\\n\\n fd.write(xm.end(xmltag))\\n fd.close()\\n return filepath\",\n \"def booltostr(value: bool) -> Any:\\n\\n if isinstance(value, bool):\\n return \\\"True\\\" if value else \\\"False\\\"\\n return value\",\n \"def _true_false_to_string(v):\\n \\n if not isinstance(v,bool):\\n raise InstrumentParameterException('Value %s is not a bool.' % str(v))\\n if v:\\n return 'y'\\n else:\\n return 'n'\",\n \"def _bool_encode(self, d):\\n for k, v in d.items():\\n if isinstance(v, bool):\\n d[k] = str(v).lower()\\n \\n return d\",\n \"def explicit_bool(value: bool) -> bool:\\n return value\",\n \"def as_bool(self):\\n return InvenTree.helpers.str2bool(self.value)\",\n \"def get_bool2(self):\\n pass\",\n \"def is_bool(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_bool)\",\n \"def writable(self):\\n return True\",\n \"def setWriteComment(*args):\\n return _libsbml.XMLOutputStream_setWriteComment(*args)\",\n \"def test_boolstyle_round_trip(self):\\n def do_round_trip(trueval, falseval, invalid=False):\\n logger.debug('Exporting to csv file: {} with bool style {},{}'.format(tempfile.name, trueval, falseval))\\n _, err, _ = self.run_cqlsh(cmds=\\\"COPY ks.testbooleans TO '{}' WITH BOOLSTYLE='{}, {}'\\\"\\n .format(tempfile.name, trueval, falseval))\\n if invalid:\\n expected_err = \\\"Invalid boolean styles [{}, {}]\\\".format(\\n ', '.join([\\\"'{}'\\\".format(s.strip()) for s in trueval.split(',')]),\\n ', '.join([\\\"'{}'\\\".format(s.strip()) for s in falseval.split(',')]))\\n assert expected_err in err\\n return\\n\\n tempfile_rows_as_list = list(csv_rows(tempfile.name))\\n assert [['0', falseval], ['1', trueval]] == sorted(tempfile_rows_as_list)\\n exported_results = list(self.session.execute(\\\"SELECT * FROM testbooleans\\\"))\\n\\n logger.debug('Importing from csv file: {}'.format(tempfile.name))\\n self.session.execute('TRUNCATE ks.testbooleans')\\n self.run_cqlsh(cmds=\\\"COPY ks.testbooleans FROM '{}' WITH BOOLSTYLE='{}, {}'\\\"\\n .format(tempfile.name, trueval, falseval))\\n\\n imported_results = list(self.session.execute(\\\"SELECT * FROM testbooleans\\\"))\\n assert sorted(exported_results) == sorted(imported_results)\\n\\n self.prepare()\\n self.session.execute(\\\"\\\"\\\"\\n CREATE TABLE testbooleans (\\n a int PRIMARY KEY,\\n b boolean\\n )\\\"\\\"\\\")\\n\\n insert_statement = self.session.prepare(\\\"INSERT INTO testbooleans (a, b) VALUES (?, ?)\\\")\\n self.session.execute(insert_statement, [0, False])\\n self.session.execute(insert_statement, [1, True])\\n tempfile = self.get_temp_file()\\n\\n do_round_trip('True', 'False')\\n do_round_trip('TRUE', 'FALSE')\\n do_round_trip('yes', 'no')\\n do_round_trip('1', '0')\\n do_round_trip('TRUE', 'no')\\n do_round_trip('True', '0')\\n\\n do_round_trip('TRUE', 'TRUE', invalid=True)\\n do_round_trip('TRUE', '', invalid=True)\\n do_round_trip('', 'FALSE', invalid=True)\\n do_round_trip('', '', invalid=True)\\n do_round_trip('yes, no', 'maybe', invalid=True)\",\n \"def pretty_bool(value):\\r\\n bool_dict = [True, \\\"True\\\", \\\"true\\\", \\\"T\\\", \\\"t\\\", \\\"1\\\"]\\r\\n return value in bool_dict\",\n \"def getBool(string):\\n return (True)\",\n \"def get_truefalse(truefalse):\\n return 'True' if truefalse else 'False'\",\n \"def writetif(self,outputname,):\\n pass\",\n \"def writexml(self, writer, indent=\\\"\\\", add_indent=\\\"\\\", new_line=\\\"\\\"):\\n writer.write(indent + \\\"<\\\" + self.tagName)\\n attrs = self._get_attributes()\\n\\n for a_name in attrs.keys():\\n writer.write(\\\" %s=\\\\\\\"\\\" % a_name)\\n self.write_data(writer, attrs[a_name].value)\\n writer.write(\\\"\\\\\\\"\\\")\\n if self.childNodes:\\n writer.write(\\\">\\\")\\n if len(self.childNodes) == 1 and self.childNodes[0].nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):\\n self.childNodes[0].writexml(writer, '', '', '')\\n else:\\n writer.write(new_line)\\n for node in self.childNodes:\\n node.writexml(writer, indent + add_indent, add_indent, new_line)\\n writer.write(indent)\\n writer.write(\\\"</%s>%s\\\" % (self.tagName, new_line))\\n else:\\n writer.write(\\\"/>%s\\\" % new_line)\",\n \"def f_boolean(node, pos, size, context, v):\\n if xpath.tools.nodesetp(v):\\n return len(v) > 0\\n elif xpath.tools.numberp(v):\\n if v == 0 or v != v:\\n return False\\n return True\\n elif xpath.tools.stringp(v):\\n return v != ''\\n\\n return v\",\n \"def get_string(self):\\n boolean_value_string = str(bool(self._boolean_value)).lower()\\n return BOOLEAN_TEMPLATE.substitute(\\n boolean_name = str(self._boolean_name),\\n boolean_value = boolean_value_string)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74465925","0.73603874","0.7117586","0.67680955","0.66733044","0.63848156","0.62347335","0.59897983","0.591663","0.5848001","0.57468945","0.564458","0.5640521","0.5636943","0.561803","0.5609525","0.5577795","0.557284","0.55520767","0.5486341","0.54855955","0.5472363","0.5398986","0.5393929","0.53881365","0.53400695","0.53359395","0.5328683","0.53096366","0.530688","0.5298158","0.527579","0.52755976","0.52374965","0.52317363","0.51858133","0.5181992","0.5179308","0.51580626","0.5148153","0.51413447","0.51359874","0.51281035","0.51262057","0.51214176","0.51181215","0.510315","0.5092946","0.5087618","0.5085611","0.5083332","0.5082165","0.5077473","0.5059621","0.5055841","0.5018908","0.4961886","0.49557623","0.49540243","0.49516794","0.49463683","0.49434832","0.49222273","0.49214086","0.49195156","0.49116316","0.49115682","0.4910753","0.49035692","0.48934042","0.4883178","0.48811346","0.48768777","0.48718375","0.48677683","0.4866672","0.48534897","0.4851439","0.48355246","0.48331037","0.48304054","0.48295763","0.48268896","0.4821869","0.48123783","0.48123208","0.4811403","0.4809237","0.48066097","0.48034886","0.48020187","0.48005497","0.47981605","0.47924447","0.4788605","0.47823325","0.47805914","0.47756416","0.4774477","0.47743624"],"string":"[\n \"0.74465925\",\n \"0.73603874\",\n \"0.7117586\",\n \"0.67680955\",\n \"0.66733044\",\n \"0.63848156\",\n \"0.62347335\",\n \"0.59897983\",\n \"0.591663\",\n \"0.5848001\",\n \"0.57468945\",\n \"0.564458\",\n \"0.5640521\",\n \"0.5636943\",\n \"0.561803\",\n \"0.5609525\",\n \"0.5577795\",\n \"0.557284\",\n \"0.55520767\",\n \"0.5486341\",\n \"0.54855955\",\n \"0.5472363\",\n \"0.5398986\",\n \"0.5393929\",\n \"0.53881365\",\n \"0.53400695\",\n \"0.53359395\",\n \"0.5328683\",\n \"0.53096366\",\n \"0.530688\",\n \"0.5298158\",\n \"0.527579\",\n \"0.52755976\",\n \"0.52374965\",\n \"0.52317363\",\n \"0.51858133\",\n \"0.5181992\",\n \"0.5179308\",\n \"0.51580626\",\n \"0.5148153\",\n \"0.51413447\",\n \"0.51359874\",\n \"0.51281035\",\n \"0.51262057\",\n \"0.51214176\",\n \"0.51181215\",\n \"0.510315\",\n \"0.5092946\",\n \"0.5087618\",\n \"0.5085611\",\n \"0.5083332\",\n \"0.5082165\",\n \"0.5077473\",\n \"0.5059621\",\n \"0.5055841\",\n \"0.5018908\",\n \"0.4961886\",\n \"0.49557623\",\n \"0.49540243\",\n \"0.49516794\",\n \"0.49463683\",\n \"0.49434832\",\n \"0.49222273\",\n \"0.49214086\",\n \"0.49195156\",\n \"0.49116316\",\n \"0.49115682\",\n \"0.4910753\",\n \"0.49035692\",\n \"0.48934042\",\n \"0.4883178\",\n \"0.48811346\",\n \"0.48768777\",\n \"0.48718375\",\n \"0.48677683\",\n \"0.4866672\",\n \"0.48534897\",\n \"0.4851439\",\n \"0.48355246\",\n \"0.48331037\",\n \"0.48304054\",\n \"0.48295763\",\n \"0.48268896\",\n \"0.4821869\",\n \"0.48123783\",\n \"0.48123208\",\n \"0.4811403\",\n \"0.4809237\",\n \"0.48066097\",\n \"0.48034886\",\n \"0.48020187\",\n \"0.48005497\",\n \"0.47981605\",\n \"0.47924447\",\n \"0.4788605\",\n \"0.47823325\",\n \"0.47805914\",\n \"0.47756416\",\n \"0.4774477\",\n \"0.47743624\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8478797"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11588,"cells":{"query":{"kind":"string","value":"Loads the profile instance from the Xml. Xml>should be a XmlNode/XmlDocument containing a profile node."},"document":{"kind":"string","value":"def load_from_xml(self, Xml):\r\n try:\r\n #Text vars\r\n self.Name = Xml.Attributes[\"Name\"].Value\r\n\r\n if \"Version\" in Xml.Attributes:\r\n self.Version = float(Xml.Attributes[\"Version\"].Value)\r\n\r\n for var_name in self.__dict__:\r\n if type(getattr(self,var_name)) is str:\r\n self.load_text_from_xml(Xml, var_name)\r\n\r\n\r\n elif type(getattr(self,var_name)) is bool:\r\n self.load_bool_from_xml(Xml, var_name)\r\n\r\n\r\n elif type(getattr(self, var_name)) is list and var_name != \"ExcludeRules\":\r\n self.load_list_from_xml(Xml, var_name)\r\n\r\n elif type(getattr(self, var_name)) is dict:\r\n self.load_dict_from_xml(Xml, var_name)\r\n\r\n #Exclude Rules\r\n exclude_rules_node = Xml.SelectSingleNode(\"ExcludeRules\")\r\n if exclude_rules_node is not None:\r\n self.ExcludeOperator = exclude_rules_node.Attributes[\"Operator\"].Value\r\n\r\n self.ExcludeMode = exclude_rules_node.Attributes[\"ExcludeMode\"].Value\r\n\r\n for node in exclude_rules_node.ChildNodes:\r\n if node.Name == \"ExcludeRule\":\r\n try:\r\n rule = ExcludeRule(node.Attributes[\"Field\"].Value, node.Attributes[\"Operator\"].Value, node.Attributes[\"Value\"].Value)\r\n except AttributeError:\r\n rule = ExcludeRule(node.Attributes[\"Field\"].Value, node.Attributes[\"Operator\"].Value, node.Attributes[\"Text\"].Value)\r\n\r\n self.ExcludeRules.append(rule)\r\n \r\n elif node.Name == \"ExcludeGroup\":\r\n group = ExcludeGroup(node.Attributes[\"Operator\"].Value)\r\n group.load_from_xml(node)\r\n self.ExcludeRules.append(group)\r\n\r\n self.update()\r\n\r\n except Exception, ex:\r\n print ex\r\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused","def load_profile(path, profile):\n profiles = load(path)\n try:\n return profiles[profile]\n except KeyError:\n return Profile(None, None, None, None)","def load_xml(self, xml_tree):\n xml_score_part = next(iter(\n score_part\n for score_part in xml_tree.getroot().findall(\"part-list/score-part\")\n if self.get_xml_part_name(score_part) == self.name\n ), None)\n if xml_score_part is None:\n raise ValueError(\"Part {} not found in the Song.\")\n\n self.set_xml(xml_score_part, xml_tree)","def parse(self):\n details = self.details()\n\n return Profile(\n book_id=self.book_id,\n title=self.title(),\n user_id=self.user_id(),\n username=self.username(),\n summary=self.summary(),\n published=self.published(),\n updated=self.updated(),\n **details\n )","def _load_profile(self, profile_name, cwd):\n\n profile_path = self.get_profile_path(profile_name, cwd)\n try:\n text = load_user_encoded(profile_path)\n except Exception as e:\n raise ConanException(f\"Cannot load profile:\\n{e}\")\n\n # All profiles will be now rendered with jinja2 as first pass\n base_path = os.path.dirname(profile_path)\n file_path = os.path.basename(profile_path)\n context = {\"platform\": platform,\n \"os\": os,\n \"profile_dir\": base_path,\n \"profile_name\": file_path,\n \"conan_version\": conan_version}\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\n text = rtemplate.render(context)\n\n try:\n return self._recurse_load_profile(text, profile_path)\n except ConanException as exc:\n raise ConanException(\"Error reading '%s' profile: %s\" % (profile_name, exc))","def load(self, config, profile_key):\n # a none numeric argument is supposed to be 'alias'\n if re.search(r\"\\b[0-9]+\\b\", profile_key) is None:\n key = profile_key\n self.alias = key\n access_by = \"alias\"\n else:\n key = (int)(profile_key)\n self.id = key\n access_by = \"id\"\n self.logger.debug(\"==> profile will be access by his '%s'\" % access_by)\n\n # check if the profile exists into config...\n i = 0\n profile_found = False\n while (i < len(config[\"profiles\"])\n and profile_found is False):\n\n if config[\"profiles\"][i][access_by] == key:\n profile_found = True\n self.logger.debug(\"==> profile '%s' found (access by '%s')\" %\n (key, access_by))\n self.config = config[\"profiles\"][i]\n\n i += 1\n\n # ... and if not raise an exception\n if profile_found is False:\n raise ProfileLoadError(\"profile '%s' not found\" % profile_key)\n\n # set profile properties\n self.id = self.config[\"id\"]\n self.alias = self.config[\"alias\"]\n self.state_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".json\")\n self.lock_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".lock\")\n\n # set the todo queue limit based on configuration\n # - default is 1\n try:\n todo_limit_queue = self.config[\"todo_queue_limit\"]\n self.todo_queue_limit = todo_limit_queue\n except KeyError:\n self.todo_queue_limit = 1","def from_xml(self, xml):\n\n node = parseString(xml).documentElement\n self.from_node(node)","def apply(self):\r\n\r\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\r\n\r\n def saving_file(xml):\r\n \"\"\" Save user profile in xml format to temp_ dir.\"\"\"\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))\r\n\r\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\r\n \"/data/sampleProfile.xml\")\r\n\r\n # The below code will parse the sample xml file\r\n # and fill important details entered by the user.\r\n root_tree = parse_xml.getroot()\r\n root_tree[0].text = self.ssid.get()\r\n root_tree[1][0][0].text = self.ssid.get()\r\n root_tree[3].text = self.connection_mode.get().lower()\r\n security = root_tree[4][0]\r\n security[0][0].text = self.authentication.get()\r\n security[0][1].text = self.encryption.get()\r\n if self.authentication.get() != \"open\":\r\n etree.SubElement(security, \"sharedKey\")\r\n etree.SubElement(security[1], \"keyType\").text = \"passPhrase\"\r\n etree.SubElement(security[1], \"protected\").text = \"false\"\r\n etree.SubElement(security[1], \"keyMaterial\").text = self.password.get()\r\n\r\n # Save the xml file\r\n saving_file(root_tree)\r\n\r\n # Add profile to the system.\r\n temp_path = 'netsh wlan add profile filename=\"' + self.app_path + \"\\\\temp_\\\\\"\r\n output_ = subprocess.run(temp_path + file_name + '.xml\"', shell=True,\r\n capture_output=True, text=True)\r\n os.remove(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\")\r\n\r\n # If unable to add profile.\r\n if output_.returncode != 0:\r\n message = \"Sorry, Unable to add profile.\\n(You entered wrong details \" \\\r\n \"or else you don't have admin rights.)\"\r\n image_ = \"error\"\r\n\r\n else:\r\n message = \"Profile added successfully (Please Refresh)\"\r\n image_ = \"warning\"\r\n\r\n MessageBox(self.parent, message, image_)","def load_frontend_xml(xml_path):\n frontend_setting = open(xml_path, \"r\").read()\n return frontend_setting","def parse(xml):\n from System.IO import StringReader\n return load(StringReader(xml))","def load(self):\n\n pacemaker_xml_filename = ('/tmp/pacemaker_%s.xml'\n % str(uuid.uuid4()))\n\n try:\n if not os.path.exists('/usr/sbin/cibadmin'):\n return\n\n os.system(\"/usr/sbin/cibadmin --query > %s\"\n % pacemaker_xml_filename)\n\n if not os.path.exists(pacemaker_xml_filename):\n return\n\n self._xmldoc = etree.parse(pacemaker_xml_filename)\n if self._xmldoc is None:\n os.remove(pacemaker_xml_filename)\n return\n\n if not etree.iselement(self._xmldoc.getroot()):\n self._xmldoc = None\n os.remove(pacemaker_xml_filename)\n return\n\n if len(self._xmldoc.getroot()) == 0:\n self._xmldoc = None\n os.remove(pacemaker_xml_filename)\n return\n\n os.remove(pacemaker_xml_filename)\n\n except Exception:\n if os.path.exists(pacemaker_xml_filename):\n os.remove(pacemaker_xml_filename)\n\n LOG.error(\"error:\", sys.exc_info()[0])","def load_xml(self, xml):\r\n assert isinstance(xml, ET.Element)\r\n\r\n self.name = xml.attrib[\"name\"]\r\n self.description = xml.attrib[\"description\"]\r\n\r\n self.interfaces = list()\r\n interfaces_xmls = xml.findall('interface')\r\n\r\n for interface_xml in interfaces_xmls:\r\n interface = NodeInterfaceXMLFormatter()\r\n interface.load_xml(interface_xml)\r\n self.interfaces.append(interface)","def load_profile_page(self, url='', user=None):\n if user:\n url = 'http://www.linkedin.com/in/' + user\n if 'com/in/' not in url and 'sales/gmail/profile/proxy/' not in url:\n raise ValueError(\n \"Url must look like... .com/in/NAME or... '.com/sales/gmail/profile/proxy/EMAIL\")\n self.driver.get(url)\n # Wait for page to load dynamically via javascript\n try:\n myElem = WebDriverWait(self.driver, self.timeout).until(AnyEC(\n EC.presence_of_element_located(\n (By.CSS_SELECTOR, self.MAIN_SELECTOR)),\n EC.presence_of_element_located(\n (By.CSS_SELECTOR, self.ERROR_SELECTOR))\n ))\n except TimeoutException as e:\n raise ValueError(\n \"\"\"Took too long to load profile. Common problems/solutions:\n 1. Invalid LI_AT value: ensure that yours is correct (they\n update frequently)\n 2. Slow Internet: increase the time out parameter in the Scraper\n constructor\n 3. Invalid e-mail address (or user does not allow e-mail scrapes) on scrape_by_email call\n \"\"\")\n\n # Check if we got the 'profile unavailable' page\n try:\n self.driver.find_element_by_css_selector(self.MAIN_SELECTOR)\n except:\n raise ValueError(\n 'Profile Unavailable: Profile link does not match any current Linkedin Profiles')\n # Scroll to the bottom of the page incrementally to load any lazy-loaded content\n self.scroll_to_bottom()","def read_xml(self):\n pass","def getProfileInfo(self):\n doc = minidom.parse(urllib.urlopen(serverString + \"/rest/user/\" + self.name))\n for element in doc.getElementsByTagName(\"user\")[0].childNodes:\n if element.nodeType != minidom.Node.ELEMENT_NODE:\n continue\n elif element.tagName == \"status\" and int(element.firstChild.data) != 1:\n raise ServerError(element.firstChild.data)\n elif element.tagName == \"input\":\n self.name = element.firstChild.data\n elif element.tagName == \"id\":\n self.id = element.firstChild.data\n elif element.tagName == \"image\":\n self.image = element.firstChild.data\n elif element.tagName == \"tagline\":\n if element.firstChild == None:\n self.tagline = None\n else:\n self.tagline = element.firstChild.data\n elif element.tagName == \"creation\":\n self.created = datetime.datetime.strptime(element.firstChild.data[:element.firstChild.data.rfind(\".\")]+\".GMT\", \"%Y-%m-%d %H:%M:%S.%Z\")","def parse_xml_results(self, xml):\n\n dom = minidom.parseString(xml)\n \n statistics_node = dom.getElementsByTagName(\"run_statistics\")\n \n if statistics_node:\n try:\n self.run_statistics.load_statistics(statistics_node[0])\n except ValueError, IndexError:\n logger.warning('Fail to read run statistics from results.dat')","def load_profile(self):\n self.dut_testpmd.start_testpmd(\n \"Default\", \"--pkt-filter-mode=perfect --port-topology=chained \\\n --txq=%s --rxq=%s\"\n % (self.PF_QUEUE, self.PF_QUEUE))\n self.vm0_testpmd.start_testpmd(\n VM_CORES_MASK, \"--port-topology=chained --txq=%s --rxq=%s\"\n % (self.VF_QUEUE, self.VF_QUEUE))\n self.dut_testpmd.execute_cmd('port stop all')\n time.sleep(1)\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\n self.dut_testpmd.execute_cmd('ddp add 0 /tmp/gtp.pkgo,/tmp/gtp.bak')\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\n self.verify(\"Profile number is: 1\" in out,\n \"Failed to load ddp profile!!!\")\n self.dut_testpmd.execute_cmd('port start all')\n time.sleep(1)\n self.dut_testpmd.execute_cmd('set fwd rxonly')\n self.dut_testpmd.execute_cmd('set verbose 1')\n self.dut_testpmd.execute_cmd('start')\n self.vm0_testpmd.execute_cmd('set fwd rxonly')\n self.vm0_testpmd.execute_cmd('set verbose 1')\n self.vm0_testpmd.execute_cmd('start')","def load_profile_by_name(self, name):\n # set new profile as active and load data;\n # search the selector's model for a name that matches the arg\n self._selector.setCurrentIndex(self.selector_index_by_name(name))","def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\n assert isinstance(profiles, list), \"Please provide a list of profiles\"\n cache = ClientCache(self._conan_api.cache_folder)\n loader = ProfileLoader(cache)\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\n profile.conf.validate()\n cache.new_config.validate()\n # Apply the new_config to the profiles the global one, so recipes get it too\n profile.conf.rebase_conf_definition(cache.new_config)\n return profile","def from_xml(cls, xml_data, system, id_generator):\r\n raise NotImplementedError('Modules must implement from_xml to be parsable from xml')","def __init__(self):\r\n super(ProfileParser, self).__init__([self.ProfileEntryHandler()])","def LoadXML(NAME):\r\n # Basics snaged from https://docs.python.org/2/library/xml.etree.elementtree.html\r\n Tree = parse(NAME) # opens and turns the xml file into a tree\r\n Root = Tree.getroot()\r\n return(Root)","def get_profile(self, html_content: str) -> object:\n if not html_content:\n raise Exception(\"HTML content not found\")\n\n soup = BeautifulSoup(html_content, 'html.parser')\n\n self.profile = {\n \"employments\": [],\n \"others\": [],\n \"education\": []\n }\n # get sidebar\n sidebar = soup.select_one(self.tags.get(\"sidebar\"))\n items = sidebar.select(self.tags.get(\"sidebar.items\")) if sidebar else []\n\n # read each item on sidebar\n for item in items:\n\n label = item.select_one(self.tags.get(\"sidebar.items.header\"))\n section_title = label.text.strip()\n\n # Availability Item\n if section_title == 'Availability':\n status, avaibility = smart_split(\n get_value(item.select_one(self.tags.get(\"sidebar.items.availability\"))), '\\n')\n self.profile[\"status\"] = status\n self.profile[\"avaibility\"] = avaibility\n\n # Languages Item\n elif section_title == 'Languages':\n\n languages = [' '.join(get_value(li).split()) for li in\n item.select(self.tags.get(\"sidebar.items.languages.item\"))]\n self.profile[\"languages\"] = [dict(zip([\"name\", \"level\"], lng.split(\":\"))) for lng in languages]\n\n # Education Item\n elif section_title == 'Education':\n lis = item.select(self.tags.get(\"sidebar.items.education.item\"))\n\n for li in lis:\n school = \" \".join(\n get_value(li.select_one(self.tags.get(\"sidebar.items.education.school\"))).split())\n degree_area = get_value(li.select_one(self.tags.get(\"sidebar.items.education.degree_area\")))\n degree, area = smart_split(degree_area)\n attended_date = get_value(li.select_one(self.tags.get(\"sidebar.items.education.attended_date\")))\n\n self.profile[\"education\"].append({\n \"school\": school,\n \"degree\": degree,\n \"area\": area,\n \"attended_date\": attended_date\n })\n\n # Skill panel\n skills = soup.select(self.tags.get(\"skills\"))\n self.profile[\"skills\"] = set(get_value(s) for s in skills) # using set to remove duplications\n\n # Employment/Other Experience panels\n cards = soup.select(self.tags.get(\"profile.panels\"))\n for lis in cards:\n lis = lis.parent.select(\"li\")\n\n for li in lis:\n position, employer = smart_split(\n li.select_one(self.tags.get(\"employments.position_employer\")).get_text(strip=True), \"|\")\n period = ' '.join(li.select_one(self.tags.get(\"employments.period\")).get_text(strip=True).split())\n\n if \"Other\" != position.strip():\n self.profile[\"employments\"].append({\n \"employer\": employer,\n \"job_title\": position,\n \"hire_datetime\": period.split('-')[0].strip(),\n \"termination_datetime\": period.split('-')[1].strip()\n })\n else:\n self.profile[\"others\"].append({\n \"title\": employer,\n \"description\": period.split('-')[0]\n })\n\n title = soup.select_one(self.tags.get(\"title\"))\n hour = get_value(title.parent.parent.select_one(self.tags.get(\"hour_price\")))\n description = get_value(soup.select_one(self.tags.get(\"description\")))\n\n # define the last elements of profile\n self.profile[\"title\"] = get_value(title)\n self.profile[\"price\"] = hour\n self.profile[\"description\"] = description\n self.profile[\"full_name\"] = soup.select_one(self.tags.get(\"full_name\")).get_text(strip=True)\n self.profile[\"first_name\"] = self.profile[\"full_name\"].split()[0]\n self.profile[\"last_name\"] = self.profile[\"full_name\"].split()[-1]\n self.profile[\"address_city\"] = soup.select_one(self.tags.get(\"address_city\")).get_text(strip=True)\n self.profile[\"address_country\"] = soup.select_one(self.tags.get(\"address_country\")).get_text(strip=True)\n self.profile[\"picture_url\"] = soup.select_one(self.tags.get(\"picture\"))[\"src\"]\n\n return self.profile","def __init__(self):\n super(Profile, self).__init__()","def _recurse_load_profile(self, text, profile_path):\n try:\n inherited_profile = Profile()\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\n profile_parser = _ProfileParser(text)\n # Iterate the includes and call recursive to get the profile and variables\n # from parent profiles\n for include in profile_parser.includes:\n # Recursion !!\n profile = self._load_profile(include, cwd)\n inherited_profile.compose_profile(profile)\n\n # Current profile before update with parents (but parent variables already applied)\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\n inherited_profile)\n return inherited_profile\n except ConanException:\n raise\n except Exception as exc:\n raise ConanException(\"Error parsing the profile text file: %s\" % str(exc))","def loader(self, href, parse, encoding=None):\n \n if href[0] != '/':\n href = os.path.join(self.directory, href)\n if not os.path.isfile(href) or not os.access(href, os.R_OK):\n raise RuntimeException(\"Xml: File %s Doesn't Exist or Not Readable (xi)\" % href)\n \n file = open(href)\n if parse == \"xml\":\n data = ElementTree.parse(file).getroot()\n else:\n data = file.read()\n if encoding:\n data = data.decode(encoding)\n file.close()\n return data","def _load_profile_cache(self):\n cache_path = self._prepare_profile_cache_path()\n if cache_path is None:\n return\n\n if self.remote_cache_bytes is not None:\n _LOGGER.info(\n f\"Loading profile cache from provided cache content with length {len(self.remote_cache_bytes)}\",\n )\n with open(cache_path, \"wb\") as f:\n f.write(self.remote_cache_bytes)\n _LOGGER.info(f\"Loading profile cache from: {cache_path}\")\n self._profile_cache = ProfileCacheDB(\n TargetType(self._target_type).name, path=cache_path\n )","def profile_details(self, profile_name):\n url = get_url('profile details', profile=profile_name)\n response = self._get(url)\n if response.status_code == 404:\n return None\n raise_on_error(response)\n return Profile(response.json())","def get_profile(self):\n endpoint = '/profile'\n return self.get_request(endpoint)","def retrieve_profile(self, name):\n\n url = get_url('profile details', profile=name)\n response = self._get(url)\n raise_on_error(response)\n if response.status_code == 404:\n raise QarnotGenericException(response.json()['message'])\n return Profile(response.json())","def load_internal(self, name, profile='default', **kwargs):\n version = kwargs.get('version', '1.0')\n uri = self.conf['uri']\n uri = uri.format(name=name, profile=profile, version=version)\n if uri.startswith('http://') or uri.startswith('https://'):\n r = requests.get(uri)\n if r.status_code == 200:\n return yaml.load(r.content)\n import os\n if not os.path.isabs(uri):\n uri = os.path.join(self.context.workdir, uri)\n with open(uri, 'r') as f:\n content = f.read()\n return yaml.load(content, Loader=yaml.FullLoader)","def user_loader(self, email):\n if not self.um.check_file_exists(\"users.json\"):\n return\n\n if email not in self.users:\n return\n\n user = User()\n user.id = email\n return user","def load(self, xml):\n\n # If the argument is a etree Element, use it as the tree root. This allows nesting of schemas\n if isinstance(xml, etree._Element):\n tree = xml\n\n # If the argument is a string, parse it using lxml.etree.fromstring()\n else:\n tree = etree.fromstring(xml)\n\n # Extract data using the schema's fields\n data = self.dict_type()\n ignore_missing = self.ignore_missing\n\n for name, field in self._fields.items():\n try:\n data[name] = field.load(tree)\n except MissingFieldError as e:\n # Note the use of \"is True\" below, this on purpose\n if field.required is True or not ignore_missing:\n raise e\n\n # Post-processing\n data = self.post_load(data)\n if not isinstance(data, (self.dict_type, dict)):\n raise TypeError(f'post_load() must return a dict or dict-like object, not {type(data)}')\n\n return data","def load_profile(dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"rb\") as f:\n logger = pickle.load(f)\n return logger","def load(filename):\n return XMLReader().from_file(filename)","def new(cls, xml):\n return oxml.parse_xml(xml)","def open_profile_menager():\r\n reload(edc_profile_manager)\r\n edc_profile_manager.ProfileManager()","def get(self, id):\n adm = Administration()\n prof = adm.get_profile_by_id(id)\n return prof","def get(self, id):\n adm = Administration()\n prof = adm.get_profile_by_id(id)\n return prof","def load_xml(filename):\n path = dirname(__file__)\n with open(join(path, 'data', filename)) as file:\n content = file.read()\n return content","def part_from_xml(cls, xml_score_part, xml_tree, song):\n part = cls()\n part.set_xml(xml_score_part, xml_tree)\n part.name = part.get_xml_part_name(xml_score_part)\n part.song = song\n\n return part","def _sloppy_parse_profiles (self, contents):\n profile_start = contents.find('profiles\":')\n profile_list_start = contents.find('profilesList')\n if int(profile_start) > -1 and int(profile_list_start) > -1:\n try:\n try:\n return json.loads('{\"a\":{\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\n except ValueError, e:\n return None\n except TypeError, e:\n return None\n return None","def test_get(self):\r\n profile = self.profile_manager.get('testing')\r\n self.assertIsInstance(profile, Profile)","def get_profile(profile_id):\n profile = Profile.objects.get(id=profile_id)\n return profile","def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\n Dict[str, List[str]]]:\n individuals = [[]]\n sublist = 0\n content = profiles_file.readlines()\n for i in content:\n if i != '\\n':\n individuals[sublist].append(i)\n else:\n sublist += 1\n individuals.append([])\n return sort_profile(individuals)","def __init__(self, xml_text):\n logger.verbose(\"Load Version.xml\")\n self.parse(xml_text)","def use_profile(self, profile_name, workdir=None):\n error_messages = []\n\n # Clear all the current configuration before loading a new profile\n self._w3af_core.plugins.zero_enabled_plugins()\n MiscSettings().set_default_values()\n self._w3af_core.uri_opener.settings.set_default_values()\n\n if profile_name is None:\n # If the profile name is None, I just clear the enabled plugins and\n # return\n return\n\n # This might raise an exception (which we don't want to handle) when\n # the profile does not exist\n profile_inst = profile(profile_name, workdir)\n \n # It exists, work with it!\n\n # Set the target settings of the profile to the core\n self._w3af_core.target.set_options(profile_inst.get_target())\n\n # Set the misc and http settings\n try:\n profile_misc_settings = profile_inst.get_misc_settings()\n except BaseFrameworkException, e:\n msg = ('Setting the framework misc-settings raised an exception'\n ' due to unknown or invalid configuration parameters. %s')\n error_messages.append(msg % e)\n else:\n #\n # IGNORE the following parameters from the profile:\n # - misc_settings.local_ip_address\n #\n if 'local_ip_address' in profile_inst.get_misc_settings():\n local_ip = get_local_ip()\n profile_misc_settings['local_ip_address'].set_value(local_ip)\n\n misc_settings = MiscSettings()\n misc_settings.set_options(profile_misc_settings)\n\n try:\n http_settings = profile_inst.get_http_settings()\n except BaseFrameworkException, e:\n msg = ('Setting the framework http-settings raised an exception'\n ' due to unknown or invalid configuration parameters. %s')\n error_messages.append(msg % e)\n else:\n self._w3af_core.uri_opener.settings.set_options(http_settings)\n\n #\n # Handle plugin options\n #\n error_fmt = ('The profile you are trying to load (%s) seems to be'\n ' outdated, this is a common issue which happens when the'\n ' framework is updated and one of its plugins adds/removes'\n ' one of the configuration parameters referenced by a'\n ' profile, or the plugin is removed all together.\\n\\n'\n\n 'The profile was loaded but some of your settings might'\n ' have been lost. This is the list of issues that were'\n ' found:\\n\\n'\n ' - %s\\n'\n\n '\\nWe recommend you review the specific plugin'\n ' configurations, apply the required changes and save'\n ' the profile in order to update it and avoid this'\n ' message. If this warning does not disappear you can'\n ' manually edit the profile file to fix it.')\n\n core_set_plugins = self._w3af_core.plugins.set_plugins\n\n for plugin_type in self._w3af_core.plugins.get_plugin_types():\n plugin_names = profile_inst.get_enabled_plugins(plugin_type)\n\n # Handle errors that might have been triggered from a possibly\n # invalid profile\n try:\n unknown_plugins = core_set_plugins(plugin_names, plugin_type,\n raise_on_error=False)\n except KeyError:\n msg = ('The profile references the \"%s\" plugin type which is'\n ' unknown to the w3af framework.')\n error_messages.append(msg % plugin_type)\n continue\n \n for unknown_plugin in unknown_plugins:\n msg = ('The profile references the \"%s.%s\" plugin which is'\n ' unknown in the current framework version.')\n error_messages.append(msg % (plugin_type, unknown_plugin))\n\n # Now we set the plugin options, which can also trigger errors with\n # \"outdated\" profiles that users could have in their ~/.w3af/\n # directory.\n for plugin_name in set(plugin_names) - set(unknown_plugins):\n\n try:\n plugin_options = profile_inst.get_plugin_options(\n plugin_type,\n plugin_name)\n self._w3af_core.plugins.set_plugin_options(plugin_type,\n plugin_name,\n plugin_options)\n except BaseFrameworkException, w3e:\n msg = ('Setting the options for plugin \"%s.%s\" raised an'\n ' exception due to unknown or invalid configuration'\n ' parameters. %s')\n error_messages.append(msg % (plugin_type, plugin_name, w3e))\n\n if error_messages:\n msg = error_fmt % (profile_name, '\\n - '.join(error_messages))\n raise BaseFrameworkException(msg)","def _loadData(self, data):\n self._data = data\n self.createdAt = utils.toDatetime(data.attrib.get('createdAt'))\n self.email = data.attrib.get('email')\n self.friend = utils.cast(bool, data.attrib.get('friend'))\n self.friendlyName = data.attrib.get('friendlyName')\n self.home = utils.cast(bool, data.attrib.get('home'))\n self.id = utils.cast(int, data.attrib.get('id'))\n self.server = utils.cast(bool, data.attrib.get('server'))\n self.servers = self.findItems(data, MyPlexServerShare)\n self.thumb = data.attrib.get('thumb')\n self.username = data.attrib.get('username', '')\n for server in self.servers:\n server.accountID = self.id","def __init__(self, profile: AskarProfile):\n self._profile = profile","def load(self, id=None):\n with open(self.location, \"rt\") as f:\n line = f.readline()\n n = int(line.split(\":\")[1].strip())\n\n # Create a new TempProfile instance with dimension `n` and the\n # provided ID.\n inst = self.TempProfile([n], id=id)\n\n f.readline() # skip table header\n for i in range(n):\n time, temp = f.readline().split()\n inst.time[i] = int(time)\n inst.temperature[i] = int(temp)\n return inst","def load(self):\n\n self.refresh_token()\n\n endpoint = app.config['API']['url'] + 'user/get/' + self._id\n response = requests.get(\n endpoint,\n verify = app.config['API']['verify_ssl'],\n headers = {\n 'Authorization': self.token,\n 'API-Key': app.config['API_KEY']\n },\n )\n\n if response.status_code != 200:\n self.logger.error('[%s] %s' % (\n response.status_code, response.reason)\n )\n err = \"Could not retrieve user %s from KDM API @ %s\"\n raise utils.Logout(err % (self._id, app.config['API']['url']))\n\n user_attribs = utils.convert_json_dict(response.json()['user'])\n for attrib in user_attribs.keys():\n if attrib not in ['_id', 'password']:\n setattr(self, attrib, user_attribs[attrib])","def _parse_xml(self, hero_file):\n filepath = os.path.join(self._hero_folder, hero_file)\n\n tree = xml.etree.ElementTree.parse(filepath)\n root = tree.getroot()\n return root","def from_xml_node(cls, xml_node):\n user_id = get_xml_int_value(xml_node, Elements.ID)\n user_name = get_xml_text_value(xml_node, Elements.NAME)\n first_name = get_xml_text_value(xml_node, Elements.FIRST_NAME)\n last_name = get_xml_text_value(xml_node, Elements.LAST_NAME)\n display_name = get_xml_text_value(xml_node, Elements.DISPLAY_NAME)\n user_email = get_xml_text_value(xml_node, Elements.EMAIL)\n out_of_office_from = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_FROM)\n out_of_office_until = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_UNTIL)\n send_email = get_xml_text_value(xml_node, Elements.SEND_EMAIL)\n user_phone = get_xml_text_value(xml_node, Elements.PHONE)\n notes = get_xml_text_value(xml_node, Elements.NOTES)\n ldapDn = get_xml_text_value(xml_node, Elements.LDAPDN)\n user_type = get_xml_text_value(xml_node, Elements.TYPE)\n groups = []\n groups_node = get_xml_node(xml_node, Elements.MEMBER_OF, True)\n if groups_node:\n for group_node in groups_node.iter(tag=Elements.USER):\n groups.append(Group.from_xml_node(group_node))\n roles = []\n roles_node = get_xml_node(xml_node, Elements.ROLES, True)\n if roles_node:\n roles = Roles.from_xml_node(roles_node)\n domains = []\n domains_node = get_xml_node(xml_node, Elements.DOMAINS, True)\n if domains_node:\n domains = Domains.from_xml_node(domains_node)\n\n auth_method = get_xml_text_value(xml_node, Elements.AUTHENTICATION_METHOD)\n try:\n user_type_attrib = xml_node.attrib[Attributes.XSI_NAMESPACE_TYPE]\n except KeyError:\n user_type_attrib = None\n return cls(user_id, user_name, user_email, out_of_office_from, out_of_office_until, send_email, notes, ldapDn,\n first_name, last_name, display_name, groups, roles, domains, auth_method, user_type_attrib,\n user_type, user_phone)","def get(self, name='default'):\r\n if name not in self._cache:\r\n full_path = os.path.join(self._base_folder, name)\r\n self._create_if_needed(full_path)\r\n self._cache[name] = Profile(full_path, name)\r\n return self._cache[name]","def parsexml(self):\n raise NotImplementedError","def loadProfiles():\n with open(userProfilesDir, \"r\") as infile:\n profiles = json.loads(\"\\n\".join(infile.readlines()))\n infile.close()\n return profiles","def _load_data(self, filename):\n if not os.path.isfile(filename):\n return False\n\n with open(filename) as f:\n data = pickle.load(f)\n if data:\n self.profiles = data['profiles']\n self.user_data = data['user_data']\n self.api_data = data['api_data']\n else:\n return False","def from_xml_node(cls, xml_node):\n user_id = get_xml_int_value(xml_node, Elements.ID)\n user_name = get_xml_text_value(xml_node, Elements.NAME)\n user_email = get_xml_text_value(xml_node, Elements.EMAIL)\n out_of_office_from = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_FROM)\n out_of_office_until = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_UNTIL)\n send_email = get_xml_text_value(xml_node, Elements.SEND_EMAIL)\n notes = get_xml_text_value(xml_node, Elements.NOTES)\n user_type = get_xml_text_value(xml_node, Elements.TYPE)\n ldapDn = get_xml_text_value(xml_node, Elements.LDAPDN)\n g_permission_node = get_xml_node(xml_node, Elements.GROUPPERMISSIONS, True)\n if g_permission_node:\n group_permission = Group_Permissions.from_xml_node(g_permission_node)\n else:\n group_permission = None\n\n members_node = get_xml_node(xml_node, Elements.MEMBERS, True)\n if members_node:\n members = Members.from_xml_node(members_node)\n else:\n members = []\n roles_node = get_xml_node(xml_node, Elements.ROLES, True)\n if roles_node:\n roles = Roles.from_xml_node(roles_node)\n else:\n roles = []\n return cls(user_id, user_name, user_email, out_of_office_from, out_of_office_until, send_email, notes, ldapDn,\n group_permission, members, user_type, roles)","def parseXML(self, xml):\n return BeautifulSoup(xml, \"xml\")","def load(self, path):\n if not os.path.isfile(path):\n message = 'Error: The xml was not found, path to search: ' + path\n raise IOError(message)\n\n self._xml = Et.parse(path)\n self._path_to_xml = os.path.abspath(path)","def load(cls, filepath):\n # Load profile from disk\n with open(filepath, \"rb\") as infile:\n data = pickle.load(infile)\n\n # remove profiler class if it exists\n profiler_class = data.pop('profiler_class', None)\n\n # if the user didn't load from the a given profiler class, we need\n # to determine which profiler is being loaded.\n profiler_cls = cls\n if cls is BaseProfiler:\n if profiler_class == 'StructuredProfiler':\n profiler_cls = StructuredProfiler\n elif profiler_class == 'UnstructuredProfiler':\n profiler_cls = UnstructuredProfiler\n elif profiler_class is None: # deprecated case\n profiler_cls = StructuredProfiler\n if '_empty_line_count' in data:\n profiler_cls = UnstructuredProfiler\n else:\n raise ValueError(f'Invalid profiler class {profiler_class} '\n f'failed to load.')\n\n profile_options = profiler_cls._option_class()\n profile_options.data_labeler.is_enabled = False\n profiler = profiler_cls(None, options=profile_options)\n\n for key in data:\n setattr(profiler, key, data[key])\n\n # Restore all data labelers\n profiler._restore_data_labelers()\n return profiler","def _getProfileFromUser(self):\n # Make sure user is authenticated\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n # Get Profile from datastore\n user_id = user.email()\n p_key = ndb.Key(Profile, user_id)\n profile = p_key.get()\n # Create new Profile if not there\n if not profile:\n profile = Profile(\n key = p_key,\n displayName = user.nickname(),\n mainEmail= user.email(),\n teeShirtSize = str(TeeShirtSize.NOT_SPECIFIED),\n )\n profile.put()\n return profile","def loadProfile(fname):\n \n x = np.loadtxt(fname)\n return x[:,1]","def load(self):\r\n self.domain.get_attributes(self.name, item=self)","def test_components_profile(self):\r\n\t\tprofile = Profile.objects.get(bio=\"I'm a female profile with inserted components\")\r\n\t\tself.assertEqual(self.u1.profile, profile)","def parse(self):\n p = feedparser.parse(self.xml)\n self.p = p\n return p","def load_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n if len(profiles) == 0:\r\n #Just in case\r\n profiles[\"Default\"] = Profile()\r\n profiles[\"Default\"].Name = \"Default\"\r\n #Some default templates\r\n profiles[\"Default\"].FileTemplate = \"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\"\r\n profiles[\"Default\"].FolderTemplate = \"{<publisher>}\\{<imprint>}\\{<series>}{ (<startyear>{ <format>})}\"\r\n \r\n if not lastused:\r\n lastused = [profiles.keys()[0]]\r\n \r\n return profiles, lastused","def from_xml(self, xml_data):\n data = BeautifulStoneSoup(xml_data.find('string').contents[0])\n data = data.contents[0]\n self.bikes = int(data.find('ready_bikes').contents[0])\n self.free = int(data.find('empty_locks').contents[0])\n self.name = str(data.find('description').contents[0])\n self.lat = int(float(data.find('latitude').contents[0])*1E6)\n self.lng = int(float(data.find('longitute').contents[0])*1E6)\n return self","def _verify_profile(self, account_id: str, profile_id: str) -> str:\n profile = self.__ingest_profile\n if profile_id and self.__ip.ProfileExists(account_id=account_id, profile_id=profile_id):\n profile = profile_id\n elif self.__ingest_profile=='':\n response = self.__ip.GetDefaultProfile(account_id=account_id)\n if response.status_code in DynamicIngest.success_responses:\n profile = response.json().get('default_profile_id')\n return profile","def load(path):\n\n parser = ConfigParser()\n parser.read(str(path))\n\n def _get(section, option):\n try:\n return parser.get(section, option)\n except (NoSectionError, NoOptionError):\n return None\n\n profiles = {}\n\n for section in parser.sections():\n profiles[section] = Profile(\n domain=_get(section, \"domain\"),\n protocol=_get(section, \"protocol\"),\n client_id=_get(section, \"client_id\"),\n client_secret=_get(section, \"client_secret\"),\n )\n\n return profiles","def _parse_xml_state(self, xml):\n if not xml:\n return None\n LOG.info(xml)\n state = xmltodict.parse(xml)\n self.community = parse_community_cards(state)\n if self.community:\n if len(self.community) > 5:\n raise StateParseException(\"More than 5 community cards: %s\"\n % self.community)\n LOG.info(\"community: %s\", self.community)\n self.players = parse_players(state)\n self.pot, self.max_bet, self.my_bet = self._get_bets()\n return state","def from_xml(path: str):\n sch = Schedule()\n sch.load(path)\n return sch","def FromXml(as_xml):\n try:\n parsed = minidom.parseString(as_xml)\n except:\n #this is just necessary for python2\n parsed = minidom.parseString(as_xml.encode('utf-8'))\n story = Story()\n story.story_id = int(parsed.getElementsByTagName('id')[0].firstChild.data)\n story.url = parsed.getElementsByTagName('url')[0].firstChild.data\n story.owned_by = XmlDocument.GetDataFromTag(parsed, 'owned_by')\n story.created_at = XmlDocument.ParseDatetimeIntoSecs(parsed, 'created_at')\n story.updated_at = XmlDocument.ParseDatetimeIntoSecs(parsed, 'updated_at')\n story.requested_by = XmlDocument.GetDataFromTag(parsed, 'requested_by')\n iteration = XmlDocument.GetDataFromTag(parsed, 'number')\n story.jira_url = XmlDocument.GetDataFromTag(parsed, 'jira_url')\n story.jira_id = XmlDocument.GetDataFromTag(parsed, 'jira_id')\n story.zendesk_url = XmlDocument.GetDataFromTag(parsed, 'zendesk_url')\n story.zendesk_id = XmlDocument.GetDataFromTag(parsed, 'zendesk_id')\n\n if iteration:\n story.iteration_number = int(iteration)\n\n tasks = XmlDocument.GetTagListForTag(parsed, \"task\")\n if tasks is not None:\n story.tasks = []\n for task in tasks:\n storyTaskDictionary = dict(description=None, complete=False, id=None)\n storyTaskDictionary['description'] = XmlNodeList.toText(task.getElementsByTagName(\"description\"))\n storyTaskDictionary['complete'] = XmlNodeList.toBool(task.getElementsByTagName('complete'))\n storyTaskDictionary['id'] = XmlNodeList.toText(task.getElementsByTagName(\"id\"))\n story.tasks.append(Task.FromDictionary(storyTaskDictionary))\n\n story.SetStoryType(\n parsed.getElementsByTagName('story_type')[0].firstChild.data)\n story.SetCurrentState(\n parsed.getElementsByTagName('current_state')[0].firstChild.data)\n story.SetName(XmlDocument.GetDataFromTag(parsed, 'name'))\n story.SetDescription(XmlDocument.GetDataFromTag(parsed, 'description'))\n story.SetDeadline(XmlDocument.ParseDatetimeIntoSecs(parsed, 'deadline'))\n\n estimate = XmlDocument.GetDataFromTag(parsed, 'estimate')\n if estimate is not None:\n story.estimate = estimate\n labels = XmlDocument.GetDataFromTag(parsed, 'labels')\n if labels is not None:\n story.AddLabelsFromString(labels)\n\n story.ClearUpdatedFields()\n return story","def _from_xml(self, xml_procstep):\n try:\n if xml_procstep.tag == 'lineage':\n self.proc_step.clear_widgets(add_another=False)\n xml_procstep = xml_procstep.findall('procstep')\n if xml_procstep:\n for procstep in xml_procstep:\n procdesc_widget = self.proc_step.add_another()\n procdesc_widget._from_xml(procstep)\n else:\n self.proc_step.add_another()\n\n except KeyError:\n pass","def from_xml_node(cls, xml_node):\n users = []\n for user_node in xml_node.findall(Elements.USER):\n user_type = None\n try:\n user_type = user_node.attrib[Attributes.XSI_NAMESPACE_TYPE]\n except (AttributeError, TypeError, KeyError) as error:\n logger.error(\n \"Failed to get the type of the User_List element. Assuming it's User. Error: {}\".format(error))\n if user_type == \"group\":\n user = Group.from_xml_node(user_node)\n else:\n user = User.from_xml_node(user_node)\n users.append(user)\n return cls(users)","def get_my_profile(self):\n\n url = self.api_base_url + \"user/profile\"\n\n try:\n raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url)\n except RequestFailed:\n raise\n\n jsonified_response = json.loads(raw_response.text)\n user_profile = jsonified_response\n\n return user_profile","def getProfile(self):\n # GET /profile\n debugMain('getProfile')\n return self._genericGet('/profile')","def load_xml(self,filename):\n self.initvars()\n source = iter(ET.iterparse(filename, events = ('start','end')))\n self.name = source.next()[1].tag\n for event,elem in source:\n if event == 'end' and elem.tag == 'row':\n row = [None]*self.numcols()\n for name,val in elem.attrib.items():\n try:\n idx = self.getColIndex(name)\n except ColumnNotFoundError:\n idx = len(self.cols)\n row.append(None)\n # Add new column to the table\n self.cols.append(set([name]))\n for oldrow in self.data:\n oldrow.append(None)\n row[idx] = val\n self.data.append(row)\n self.initTypes()","def _loadData(self, data):\n self._data = data\n self.friend = self._initpath == self.key\n self.allowCameraUpload = utils.cast(bool, data.attrib.get('allowCameraUpload'))\n self.allowChannels = utils.cast(bool, data.attrib.get('allowChannels'))\n self.allowSync = utils.cast(bool, data.attrib.get('allowSync'))\n self.email = data.attrib.get('email')\n self.filterAll = data.attrib.get('filterAll')\n self.filterMovies = data.attrib.get('filterMovies')\n self.filterMusic = data.attrib.get('filterMusic')\n self.filterPhotos = data.attrib.get('filterPhotos')\n self.filterTelevision = data.attrib.get('filterTelevision')\n self.home = utils.cast(bool, data.attrib.get('home'))\n self.id = utils.cast(int, data.attrib.get('id'))\n self.protected = utils.cast(bool, data.attrib.get('protected'))\n self.recommendationsPlaylistId = data.attrib.get('recommendationsPlaylistId')\n self.restricted = data.attrib.get('restricted')\n self.thumb = data.attrib.get('thumb')\n self.title = data.attrib.get('title', '')\n self.username = data.attrib.get('username', '')\n self.servers = self.findItems(data, MyPlexServerShare)\n for server in self.servers:\n server.accountID = self.id","def test_user_has_profile_attached(self):\n user = self.users[0]\n self.assertTrue(hasattr(user, 'profile'))\n self.assertIsInstance(user.profile, ImagerProfile)","def ReadWiredNetworkProfile(self, profilename):\n profile = {}\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n for x in config.options(profilename):\n profile[x] = misc.Noneify(config.get(profilename, x))\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\n self.WiredNetwork = profile\n return \"100: Loaded Profile\"\n else:\n self.WiredNetwork = None\n return \"500: Profile Not Found\"","def from_xml(self, doc):\r\n import xml.sax\r\n handler = DomainDumpParser(self)\r\n xml.sax.parse(doc, handler)\r\n return handler","def _loadData(self, data):\n self._data = data\n self.id = utils.cast(int, data.attrib.get('id'))\n self.accountID = utils.cast(int, data.attrib.get('accountID'))\n self.serverId = utils.cast(int, data.attrib.get('serverId'))\n self.machineIdentifier = data.attrib.get('machineIdentifier')\n self.name = data.attrib.get('name')\n self.lastSeenAt = utils.toDatetime(data.attrib.get('lastSeenAt'))\n self.numLibraries = utils.cast(int, data.attrib.get('numLibraries'))\n self.allLibraries = utils.cast(bool, data.attrib.get('allLibraries'))\n self.owned = utils.cast(bool, data.attrib.get('owned'))\n self.pending = utils.cast(bool, data.attrib.get('pending'))","def load(xml):\n if isinstance(xml, XmlReader):\n for n in _process(xml): yield n\n else:\n with XmlReader.Create(xml) as xr:\n for n in _process(xr): yield n","def load_xml_startup_script(name):\n\n for dir in (\"%sshare/gps/support/core/\" % GPS.get_system_dir(),\n \"%sshare/gps/support/ui/\" % GPS.get_system_dir(),\n \"%sshare/gps/library/\" % GPS.get_system_dir(),\n \"%sshare/gps/plug-ins/\" % GPS.get_system_dir()):\n\n try:\n f = file(\"%s%s\" % (dir, name)).read()\n break\n except:\n f = None\n\n GPS.parse_xml(f)\n process_all_events()","def profile(self) -> Profile:\n return self._profile","def profile(self) -> Profile:\n return self._profile","def load_info(self, node):\n self.objtype = None\n self.usable = not xmlutil.getboolattr(node, \"unusable\")\n self.apsize = self.irapsize = DEFAULT_APSIZE\n self.apstd = self.irapstd = None\n self.basedon = self.irbasedon = 0\n self.variability = 0.0\n for child in node:\n tagn = child.tag\n if tagn == \"objtype\":\n self.objtype = xmlutil.gettext(child)\n elif tagn == \"apsize\":\n self.apsize = xmlutil.getfloat(child)\n elif tagn == \"irapsize\":\n self.irapsize = xmlutil.getfloat(child)\n elif tagn == \"apstd\":\n self.apstd = xmlutil.getfloat(child)\n elif tagn == \"irapstd\":\n self.irapstd = xmlutil.getfloat(child)\n elif tagn == \"basedon\":\n self.basedon = xmlutil.getint(child)\n elif tagn == \"irbasedon\":\n self.irbasedon = xmlutil.getint(child)\n elif tagn == \"variability\":\n self.variability = xmlutil.getfloat(child)","def load_xml(pmid, sleep=None):\r\n link = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&retmode=xml&id=%s\" % str(pmid) \r\n page = requests.get(link) \r\n tree = html.fromstring(page.content)\r\n if sleep is not None:\r\n time.sleep(sleep)\r\n return tree","def get_profile(path=\"~\"):\n global profiles\n profile = profiles.get(path,None)\n if not profile:\n profile = InitFileConfig(os.path.join(path,\".myradioprofile\"), {} )\n profiles[path] = profile\n return profile","def get_user_profile(self):\n\t\treturn Job(SDK.PrlSrv_GetUserProfile(self.handle)[0])","def load_xml(pmid, sleep=None):\n link = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&retmode=xml&id=%s\" % str(pmid) + \"&api_key=\" + API_KEY\n print(link)\n page = requests.get(link)\n tree = html.fromstring(page.content)\n if sleep is not None:\n time.sleep(sleep)\n return tree","def read_pardus_profiles(self):\n\n self.lan_config = ConfigParser.ConfigParser()\n self.lan_config.read(self.lan_config_path)\n connection_type = \"802-3-ethernet\"\n for section in self.lan_config.sections():\n lan_settings = {}\n for option in self.lan_config.options(section):\n if option == \"device\":\n #To strip device name from long device string\n lan_settings[option] = self.lan_config.get(section, option).split(\"_\")[-1]\n else:\n lan_settings[option] = self.lan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, lan_settings)\n self.pardus_profiles.append(p)\n\n self.wlan_config = ConfigParser.ConfigParser()\n self.wlan_config.read(self.wlan_config_path)\n connection_type = \"802-11-wireless\"\n for section in self.wlan_config.sections():\n wlan_settings = {}\n for option in self.wlan_config.options(section):\n if option == \"device\":\n wlan_settings[option] = self.wlan_config.get(section, option).split(\"_\")[-1]\n else:\n wlan_settings[option] = self.wlan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\n self.pardus_profiles.append(p)","def loads(xtext):\n\n class XmlLoads(object):\n \"\"\"\n XMLLOADS implements 'YZXml.loads' functionality\n \"\"\"\n\n def __init__(self, xtext):\n self.index = 0\n self.xtext = xtext\n self.length = len(xtext)\n self.stack = []\n\n def loads(self):\n \"\"\"\n LOADS converts xml-string to object-string\n\n Returns\n -------\n - dict\n object-xml contains {'tag': str, 'attribs': dict, 'elements': list}\n \"\"\"\n\n self.ignore_whitespaces()\n\n if self.index >= self.length:\n return\n\n if self.xtext[self.index] == '<':\n self.index += 1\n\n if self.xtext[self.index] == '/':\n self.index += 1\n\n tag = self.read_until('>')\n self.index += 1\n\n elements = []\n while len(self.stack) > 0 and\\\n (isinstance(self.stack[-1], str) or self.stack[-1]['tag'] != tag):\n elements.append(self.stack.pop())\n\n assert len(self.stack) > 0\n\n self.stack[-1]['elements'].extend(reversed(elements))\n\n else:\n self.ignore_whitespaces()\n tag = self.read_until(' >')\n\n attribs = {}\n if self.xtext[self.index] != '>':\n attribs = self.read_attribs()\n\n self.index += 1\n self.stack.append({'tag': tag, 'attribs': attribs, 'elements': []})\n else:\n self.stack.append(self.read_until('<').strip())\n\n self.loads()\n\n\n def ignore_whitespaces(self):\n \"\"\"\n IGNORE_WHITESPACES reads whitespaces and advances self.index\n \"\"\"\n\n whitespaces = [' ', '\\t', '\\n', '\\r']\n while self.index < self.length and self.xtext[self.index] in whitespaces:\n self.index += 1\n\n def read_until(self, chars):\n \"\"\"\n READ_UNTIL reads charaters and advances self.index\n unitl reaches any character in 'cahrs'\n\n Parameters\n ----------\n - chars: str\n stoping characters\n \"\"\"\n\n start_index = self.index\n\n while self.index < self.length and self.xtext[self.index] not in chars:\n self.index += 1\n\n assert self.index < self.length\n\n return self.xtext[start_index:self.index]\n\n def read_attribs(self):\n \"\"\"\n READ_ATTRIBS reads attributes of an elements\n \"\"\"\n\n attribs = {}\n while self.index < self.length:\n self.ignore_whitespaces()\n if self.xtext[self.index] == '>':\n break\n name = self.read_until('=')\n self.index += 1\n self.read_until('\"')\n self.index += 1\n value = self.read_until('\"')\n self.index += 1\n\n attribs[name] = value\n\n return attribs\n\n xmlloads = XmlLoads(xtext)\n xmlloads.loads()\n return xmlloads.stack.pop()","def load(self):\n pass","def load(self):\n pass","def load(self):\n pass","def load(self):\n pass","def inspect(\n name: Optional[str] = typer.Argument(\n None, help=\"Name of profile to inspect; defaults to active profile.\"\n )\n):\n profiles = prefect.settings.load_profiles()\n if name is None:\n current_profile = prefect.context.get_settings_context().profile\n if not current_profile:\n exit_with_error(\"No active profile set - please provide a name to inspect.\")\n name = current_profile.name\n print(f\"No name provided, defaulting to {name!r}\")\n if name not in profiles:\n exit_with_error(f\"Profile {name!r} not found.\")\n\n if not profiles[name].settings:\n # TODO: Consider instructing on how to add settings.\n print(f\"Profile {name!r} is empty.\")\n\n for setting, value in profiles[name].settings.items():\n app.console.print(f\"{setting.name}='{value}'\")","def set_default_profile(self):\n profile = textwrap.dedent(\n \"\"\"\n config:\n boot.autostart: \"true\"\n description: Default LXD profile\n devices:\n eth0:\n name: eth0\n nictype: bridged\n parent: conjureup1\n type: nic\n eth1:\n name: eth1\n nictype: bridged\n parent: conjureup0\n type: nic\n root:\n path: /\n pool: default\n type: disk\n name: default\n \"\"\")\n with NamedTemporaryFile(mode='w', encoding='utf-8',\n delete=False) as tempf:\n utils.spew(tempf.name, profile)\n out = utils.run_script(\n 'cat {} |conjure-up.lxc profile edit default'.format(\n tempf.name))\n if out.returncode != 0:\n raise Exception(\"Problem setting default profile: {}\".format(\n out))"],"string":"[\n \"def load_profiles_from_file(file_path):\\r\\n profiles = {}\\r\\n\\r\\n lastused = \\\"\\\"\\r\\n\\r\\n if File.Exists(file_path):\\r\\n try:\\r\\n with StreamReader(file_path) as xmlfile:\\r\\n xmldoc = XmlDocument()\\r\\n xmldoc.Load(xmlfile)\\r\\n\\r\\n if xmldoc.DocumentElement.Name == \\\"Profiles\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profiles/Profile\\\")\\r\\n #Individual exported profiles are saved with the document element as Profile\\r\\n elif xmldoc.DocumentElement.Name == \\\"Profile\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profile\\\")\\r\\n\\r\\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\\r\\n elif xmldoc.DocumentElement.Name == \\\"Settings\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Settings/Setting\\\")\\r\\n elif xmldoc.DocumentElement.Name == \\\"Setting\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Setting\\\")\\r\\n\\r\\n #No valid root elements\\r\\n else:\\r\\n MessageBox.Show(file_path + \\\" is not a valid Library Organizer profile file.\\\", \\\"Not a valid profile file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n return profiles, lastused\\r\\n\\r\\n if nodes.Count > 0:\\r\\n for node in nodes: \\r\\n profile = Profile()\\r\\n profile.Name = node.Attributes[\\\"Name\\\"].Value\\r\\n result = profile.load_from_xml(node)\\r\\n\\r\\n #Error loading the profile\\r\\n if result == False:\\r\\n MessageBox.Show(\\\"An error occured loading the profile \\\" + profile.Name + \\\". That profile has been skipped.\\\")\\r\\n\\r\\n else:\\r\\n profiles[profile.Name] = profile\\r\\n\\r\\n\\r\\n #Load the last used profile\\r\\n rootnode = xmldoc.DocumentElement\\r\\n if rootnode.HasAttribute(\\\"LastUsed\\\"):\\r\\n lastused = rootnode.Attributes[\\\"LastUsed\\\"].Value.split(\\\",\\\")\\r\\n\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"Something seems to have gone wrong loading the xml file.\\\\n\\\\nThe error was:\\\\n\\\" + str(ex), \\\"Error loading file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n\\r\\n return profiles, lastused\",\n \"def load_profile(path, profile):\\n profiles = load(path)\\n try:\\n return profiles[profile]\\n except KeyError:\\n return Profile(None, None, None, None)\",\n \"def load_xml(self, xml_tree):\\n xml_score_part = next(iter(\\n score_part\\n for score_part in xml_tree.getroot().findall(\\\"part-list/score-part\\\")\\n if self.get_xml_part_name(score_part) == self.name\\n ), None)\\n if xml_score_part is None:\\n raise ValueError(\\\"Part {} not found in the Song.\\\")\\n\\n self.set_xml(xml_score_part, xml_tree)\",\n \"def parse(self):\\n details = self.details()\\n\\n return Profile(\\n book_id=self.book_id,\\n title=self.title(),\\n user_id=self.user_id(),\\n username=self.username(),\\n summary=self.summary(),\\n published=self.published(),\\n updated=self.updated(),\\n **details\\n )\",\n \"def _load_profile(self, profile_name, cwd):\\n\\n profile_path = self.get_profile_path(profile_name, cwd)\\n try:\\n text = load_user_encoded(profile_path)\\n except Exception as e:\\n raise ConanException(f\\\"Cannot load profile:\\\\n{e}\\\")\\n\\n # All profiles will be now rendered with jinja2 as first pass\\n base_path = os.path.dirname(profile_path)\\n file_path = os.path.basename(profile_path)\\n context = {\\\"platform\\\": platform,\\n \\\"os\\\": os,\\n \\\"profile_dir\\\": base_path,\\n \\\"profile_name\\\": file_path,\\n \\\"conan_version\\\": conan_version}\\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\\n text = rtemplate.render(context)\\n\\n try:\\n return self._recurse_load_profile(text, profile_path)\\n except ConanException as exc:\\n raise ConanException(\\\"Error reading '%s' profile: %s\\\" % (profile_name, exc))\",\n \"def load(self, config, profile_key):\\n # a none numeric argument is supposed to be 'alias'\\n if re.search(r\\\"\\\\b[0-9]+\\\\b\\\", profile_key) is None:\\n key = profile_key\\n self.alias = key\\n access_by = \\\"alias\\\"\\n else:\\n key = (int)(profile_key)\\n self.id = key\\n access_by = \\\"id\\\"\\n self.logger.debug(\\\"==> profile will be access by his '%s'\\\" % access_by)\\n\\n # check if the profile exists into config...\\n i = 0\\n profile_found = False\\n while (i < len(config[\\\"profiles\\\"])\\n and profile_found is False):\\n\\n if config[\\\"profiles\\\"][i][access_by] == key:\\n profile_found = True\\n self.logger.debug(\\\"==> profile '%s' found (access by '%s')\\\" %\\n (key, access_by))\\n self.config = config[\\\"profiles\\\"][i]\\n\\n i += 1\\n\\n # ... and if not raise an exception\\n if profile_found is False:\\n raise ProfileLoadError(\\\"profile '%s' not found\\\" % profile_key)\\n\\n # set profile properties\\n self.id = self.config[\\\"id\\\"]\\n self.alias = self.config[\\\"alias\\\"]\\n self.state_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".json\\\")\\n self.lock_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".lock\\\")\\n\\n # set the todo queue limit based on configuration\\n # - default is 1\\n try:\\n todo_limit_queue = self.config[\\\"todo_queue_limit\\\"]\\n self.todo_queue_limit = todo_limit_queue\\n except KeyError:\\n self.todo_queue_limit = 1\",\n \"def from_xml(self, xml):\\n\\n node = parseString(xml).documentElement\\n self.from_node(node)\",\n \"def apply(self):\\r\\n\\r\\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\\r\\n\\r\\n def saving_file(xml):\\r\\n \\\"\\\"\\\" Save user profile in xml format to temp_ dir.\\\"\\\"\\\"\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\\r\\n\\r\\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\\r\\n \\\"/data/sampleProfile.xml\\\")\\r\\n\\r\\n # The below code will parse the sample xml file\\r\\n # and fill important details entered by the user.\\r\\n root_tree = parse_xml.getroot()\\r\\n root_tree[0].text = self.ssid.get()\\r\\n root_tree[1][0][0].text = self.ssid.get()\\r\\n root_tree[3].text = self.connection_mode.get().lower()\\r\\n security = root_tree[4][0]\\r\\n security[0][0].text = self.authentication.get()\\r\\n security[0][1].text = self.encryption.get()\\r\\n if self.authentication.get() != \\\"open\\\":\\r\\n etree.SubElement(security, \\\"sharedKey\\\")\\r\\n etree.SubElement(security[1], \\\"keyType\\\").text = \\\"passPhrase\\\"\\r\\n etree.SubElement(security[1], \\\"protected\\\").text = \\\"false\\\"\\r\\n etree.SubElement(security[1], \\\"keyMaterial\\\").text = self.password.get()\\r\\n\\r\\n # Save the xml file\\r\\n saving_file(root_tree)\\r\\n\\r\\n # Add profile to the system.\\r\\n temp_path = 'netsh wlan add profile filename=\\\"' + self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\"\\r\\n output_ = subprocess.run(temp_path + file_name + '.xml\\\"', shell=True,\\r\\n capture_output=True, text=True)\\r\\n os.remove(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\")\\r\\n\\r\\n # If unable to add profile.\\r\\n if output_.returncode != 0:\\r\\n message = \\\"Sorry, Unable to add profile.\\\\n(You entered wrong details \\\" \\\\\\r\\n \\\"or else you don't have admin rights.)\\\"\\r\\n image_ = \\\"error\\\"\\r\\n\\r\\n else:\\r\\n message = \\\"Profile added successfully (Please Refresh)\\\"\\r\\n image_ = \\\"warning\\\"\\r\\n\\r\\n MessageBox(self.parent, message, image_)\",\n \"def load_frontend_xml(xml_path):\\n frontend_setting = open(xml_path, \\\"r\\\").read()\\n return frontend_setting\",\n \"def parse(xml):\\n from System.IO import StringReader\\n return load(StringReader(xml))\",\n \"def load(self):\\n\\n pacemaker_xml_filename = ('/tmp/pacemaker_%s.xml'\\n % str(uuid.uuid4()))\\n\\n try:\\n if not os.path.exists('/usr/sbin/cibadmin'):\\n return\\n\\n os.system(\\\"/usr/sbin/cibadmin --query > %s\\\"\\n % pacemaker_xml_filename)\\n\\n if not os.path.exists(pacemaker_xml_filename):\\n return\\n\\n self._xmldoc = etree.parse(pacemaker_xml_filename)\\n if self._xmldoc is None:\\n os.remove(pacemaker_xml_filename)\\n return\\n\\n if not etree.iselement(self._xmldoc.getroot()):\\n self._xmldoc = None\\n os.remove(pacemaker_xml_filename)\\n return\\n\\n if len(self._xmldoc.getroot()) == 0:\\n self._xmldoc = None\\n os.remove(pacemaker_xml_filename)\\n return\\n\\n os.remove(pacemaker_xml_filename)\\n\\n except Exception:\\n if os.path.exists(pacemaker_xml_filename):\\n os.remove(pacemaker_xml_filename)\\n\\n LOG.error(\\\"error:\\\", sys.exc_info()[0])\",\n \"def load_xml(self, xml):\\r\\n assert isinstance(xml, ET.Element)\\r\\n\\r\\n self.name = xml.attrib[\\\"name\\\"]\\r\\n self.description = xml.attrib[\\\"description\\\"]\\r\\n\\r\\n self.interfaces = list()\\r\\n interfaces_xmls = xml.findall('interface')\\r\\n\\r\\n for interface_xml in interfaces_xmls:\\r\\n interface = NodeInterfaceXMLFormatter()\\r\\n interface.load_xml(interface_xml)\\r\\n self.interfaces.append(interface)\",\n \"def load_profile_page(self, url='', user=None):\\n if user:\\n url = 'http://www.linkedin.com/in/' + user\\n if 'com/in/' not in url and 'sales/gmail/profile/proxy/' not in url:\\n raise ValueError(\\n \\\"Url must look like... .com/in/NAME or... '.com/sales/gmail/profile/proxy/EMAIL\\\")\\n self.driver.get(url)\\n # Wait for page to load dynamically via javascript\\n try:\\n myElem = WebDriverWait(self.driver, self.timeout).until(AnyEC(\\n EC.presence_of_element_located(\\n (By.CSS_SELECTOR, self.MAIN_SELECTOR)),\\n EC.presence_of_element_located(\\n (By.CSS_SELECTOR, self.ERROR_SELECTOR))\\n ))\\n except TimeoutException as e:\\n raise ValueError(\\n \\\"\\\"\\\"Took too long to load profile. Common problems/solutions:\\n 1. Invalid LI_AT value: ensure that yours is correct (they\\n update frequently)\\n 2. Slow Internet: increase the time out parameter in the Scraper\\n constructor\\n 3. Invalid e-mail address (or user does not allow e-mail scrapes) on scrape_by_email call\\n \\\"\\\"\\\")\\n\\n # Check if we got the 'profile unavailable' page\\n try:\\n self.driver.find_element_by_css_selector(self.MAIN_SELECTOR)\\n except:\\n raise ValueError(\\n 'Profile Unavailable: Profile link does not match any current Linkedin Profiles')\\n # Scroll to the bottom of the page incrementally to load any lazy-loaded content\\n self.scroll_to_bottom()\",\n \"def read_xml(self):\\n pass\",\n \"def getProfileInfo(self):\\n doc = minidom.parse(urllib.urlopen(serverString + \\\"/rest/user/\\\" + self.name))\\n for element in doc.getElementsByTagName(\\\"user\\\")[0].childNodes:\\n if element.nodeType != minidom.Node.ELEMENT_NODE:\\n continue\\n elif element.tagName == \\\"status\\\" and int(element.firstChild.data) != 1:\\n raise ServerError(element.firstChild.data)\\n elif element.tagName == \\\"input\\\":\\n self.name = element.firstChild.data\\n elif element.tagName == \\\"id\\\":\\n self.id = element.firstChild.data\\n elif element.tagName == \\\"image\\\":\\n self.image = element.firstChild.data\\n elif element.tagName == \\\"tagline\\\":\\n if element.firstChild == None:\\n self.tagline = None\\n else:\\n self.tagline = element.firstChild.data\\n elif element.tagName == \\\"creation\\\":\\n self.created = datetime.datetime.strptime(element.firstChild.data[:element.firstChild.data.rfind(\\\".\\\")]+\\\".GMT\\\", \\\"%Y-%m-%d %H:%M:%S.%Z\\\")\",\n \"def parse_xml_results(self, xml):\\n\\n dom = minidom.parseString(xml)\\n \\n statistics_node = dom.getElementsByTagName(\\\"run_statistics\\\")\\n \\n if statistics_node:\\n try:\\n self.run_statistics.load_statistics(statistics_node[0])\\n except ValueError, IndexError:\\n logger.warning('Fail to read run statistics from results.dat')\",\n \"def load_profile(self):\\n self.dut_testpmd.start_testpmd(\\n \\\"Default\\\", \\\"--pkt-filter-mode=perfect --port-topology=chained \\\\\\n --txq=%s --rxq=%s\\\"\\n % (self.PF_QUEUE, self.PF_QUEUE))\\n self.vm0_testpmd.start_testpmd(\\n VM_CORES_MASK, \\\"--port-topology=chained --txq=%s --rxq=%s\\\"\\n % (self.VF_QUEUE, self.VF_QUEUE))\\n self.dut_testpmd.execute_cmd('port stop all')\\n time.sleep(1)\\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\\n self.dut_testpmd.execute_cmd('ddp add 0 /tmp/gtp.pkgo,/tmp/gtp.bak')\\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\\n self.verify(\\\"Profile number is: 1\\\" in out,\\n \\\"Failed to load ddp profile!!!\\\")\\n self.dut_testpmd.execute_cmd('port start all')\\n time.sleep(1)\\n self.dut_testpmd.execute_cmd('set fwd rxonly')\\n self.dut_testpmd.execute_cmd('set verbose 1')\\n self.dut_testpmd.execute_cmd('start')\\n self.vm0_testpmd.execute_cmd('set fwd rxonly')\\n self.vm0_testpmd.execute_cmd('set verbose 1')\\n self.vm0_testpmd.execute_cmd('start')\",\n \"def load_profile_by_name(self, name):\\n # set new profile as active and load data;\\n # search the selector's model for a name that matches the arg\\n self._selector.setCurrentIndex(self.selector_index_by_name(name))\",\n \"def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\\n assert isinstance(profiles, list), \\\"Please provide a list of profiles\\\"\\n cache = ClientCache(self._conan_api.cache_folder)\\n loader = ProfileLoader(cache)\\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\\n profile.conf.validate()\\n cache.new_config.validate()\\n # Apply the new_config to the profiles the global one, so recipes get it too\\n profile.conf.rebase_conf_definition(cache.new_config)\\n return profile\",\n \"def from_xml(cls, xml_data, system, id_generator):\\r\\n raise NotImplementedError('Modules must implement from_xml to be parsable from xml')\",\n \"def __init__(self):\\r\\n super(ProfileParser, self).__init__([self.ProfileEntryHandler()])\",\n \"def LoadXML(NAME):\\r\\n # Basics snaged from https://docs.python.org/2/library/xml.etree.elementtree.html\\r\\n Tree = parse(NAME) # opens and turns the xml file into a tree\\r\\n Root = Tree.getroot()\\r\\n return(Root)\",\n \"def get_profile(self, html_content: str) -> object:\\n if not html_content:\\n raise Exception(\\\"HTML content not found\\\")\\n\\n soup = BeautifulSoup(html_content, 'html.parser')\\n\\n self.profile = {\\n \\\"employments\\\": [],\\n \\\"others\\\": [],\\n \\\"education\\\": []\\n }\\n # get sidebar\\n sidebar = soup.select_one(self.tags.get(\\\"sidebar\\\"))\\n items = sidebar.select(self.tags.get(\\\"sidebar.items\\\")) if sidebar else []\\n\\n # read each item on sidebar\\n for item in items:\\n\\n label = item.select_one(self.tags.get(\\\"sidebar.items.header\\\"))\\n section_title = label.text.strip()\\n\\n # Availability Item\\n if section_title == 'Availability':\\n status, avaibility = smart_split(\\n get_value(item.select_one(self.tags.get(\\\"sidebar.items.availability\\\"))), '\\\\n')\\n self.profile[\\\"status\\\"] = status\\n self.profile[\\\"avaibility\\\"] = avaibility\\n\\n # Languages Item\\n elif section_title == 'Languages':\\n\\n languages = [' '.join(get_value(li).split()) for li in\\n item.select(self.tags.get(\\\"sidebar.items.languages.item\\\"))]\\n self.profile[\\\"languages\\\"] = [dict(zip([\\\"name\\\", \\\"level\\\"], lng.split(\\\":\\\"))) for lng in languages]\\n\\n # Education Item\\n elif section_title == 'Education':\\n lis = item.select(self.tags.get(\\\"sidebar.items.education.item\\\"))\\n\\n for li in lis:\\n school = \\\" \\\".join(\\n get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.school\\\"))).split())\\n degree_area = get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.degree_area\\\")))\\n degree, area = smart_split(degree_area)\\n attended_date = get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.attended_date\\\")))\\n\\n self.profile[\\\"education\\\"].append({\\n \\\"school\\\": school,\\n \\\"degree\\\": degree,\\n \\\"area\\\": area,\\n \\\"attended_date\\\": attended_date\\n })\\n\\n # Skill panel\\n skills = soup.select(self.tags.get(\\\"skills\\\"))\\n self.profile[\\\"skills\\\"] = set(get_value(s) for s in skills) # using set to remove duplications\\n\\n # Employment/Other Experience panels\\n cards = soup.select(self.tags.get(\\\"profile.panels\\\"))\\n for lis in cards:\\n lis = lis.parent.select(\\\"li\\\")\\n\\n for li in lis:\\n position, employer = smart_split(\\n li.select_one(self.tags.get(\\\"employments.position_employer\\\")).get_text(strip=True), \\\"|\\\")\\n period = ' '.join(li.select_one(self.tags.get(\\\"employments.period\\\")).get_text(strip=True).split())\\n\\n if \\\"Other\\\" != position.strip():\\n self.profile[\\\"employments\\\"].append({\\n \\\"employer\\\": employer,\\n \\\"job_title\\\": position,\\n \\\"hire_datetime\\\": period.split('-')[0].strip(),\\n \\\"termination_datetime\\\": period.split('-')[1].strip()\\n })\\n else:\\n self.profile[\\\"others\\\"].append({\\n \\\"title\\\": employer,\\n \\\"description\\\": period.split('-')[0]\\n })\\n\\n title = soup.select_one(self.tags.get(\\\"title\\\"))\\n hour = get_value(title.parent.parent.select_one(self.tags.get(\\\"hour_price\\\")))\\n description = get_value(soup.select_one(self.tags.get(\\\"description\\\")))\\n\\n # define the last elements of profile\\n self.profile[\\\"title\\\"] = get_value(title)\\n self.profile[\\\"price\\\"] = hour\\n self.profile[\\\"description\\\"] = description\\n self.profile[\\\"full_name\\\"] = soup.select_one(self.tags.get(\\\"full_name\\\")).get_text(strip=True)\\n self.profile[\\\"first_name\\\"] = self.profile[\\\"full_name\\\"].split()[0]\\n self.profile[\\\"last_name\\\"] = self.profile[\\\"full_name\\\"].split()[-1]\\n self.profile[\\\"address_city\\\"] = soup.select_one(self.tags.get(\\\"address_city\\\")).get_text(strip=True)\\n self.profile[\\\"address_country\\\"] = soup.select_one(self.tags.get(\\\"address_country\\\")).get_text(strip=True)\\n self.profile[\\\"picture_url\\\"] = soup.select_one(self.tags.get(\\\"picture\\\"))[\\\"src\\\"]\\n\\n return self.profile\",\n \"def __init__(self):\\n super(Profile, self).__init__()\",\n \"def _recurse_load_profile(self, text, profile_path):\\n try:\\n inherited_profile = Profile()\\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\\n profile_parser = _ProfileParser(text)\\n # Iterate the includes and call recursive to get the profile and variables\\n # from parent profiles\\n for include in profile_parser.includes:\\n # Recursion !!\\n profile = self._load_profile(include, cwd)\\n inherited_profile.compose_profile(profile)\\n\\n # Current profile before update with parents (but parent variables already applied)\\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\\n inherited_profile)\\n return inherited_profile\\n except ConanException:\\n raise\\n except Exception as exc:\\n raise ConanException(\\\"Error parsing the profile text file: %s\\\" % str(exc))\",\n \"def loader(self, href, parse, encoding=None):\\n \\n if href[0] != '/':\\n href = os.path.join(self.directory, href)\\n if not os.path.isfile(href) or not os.access(href, os.R_OK):\\n raise RuntimeException(\\\"Xml: File %s Doesn't Exist or Not Readable (xi)\\\" % href)\\n \\n file = open(href)\\n if parse == \\\"xml\\\":\\n data = ElementTree.parse(file).getroot()\\n else:\\n data = file.read()\\n if encoding:\\n data = data.decode(encoding)\\n file.close()\\n return data\",\n \"def _load_profile_cache(self):\\n cache_path = self._prepare_profile_cache_path()\\n if cache_path is None:\\n return\\n\\n if self.remote_cache_bytes is not None:\\n _LOGGER.info(\\n f\\\"Loading profile cache from provided cache content with length {len(self.remote_cache_bytes)}\\\",\\n )\\n with open(cache_path, \\\"wb\\\") as f:\\n f.write(self.remote_cache_bytes)\\n _LOGGER.info(f\\\"Loading profile cache from: {cache_path}\\\")\\n self._profile_cache = ProfileCacheDB(\\n TargetType(self._target_type).name, path=cache_path\\n )\",\n \"def profile_details(self, profile_name):\\n url = get_url('profile details', profile=profile_name)\\n response = self._get(url)\\n if response.status_code == 404:\\n return None\\n raise_on_error(response)\\n return Profile(response.json())\",\n \"def get_profile(self):\\n endpoint = '/profile'\\n return self.get_request(endpoint)\",\n \"def retrieve_profile(self, name):\\n\\n url = get_url('profile details', profile=name)\\n response = self._get(url)\\n raise_on_error(response)\\n if response.status_code == 404:\\n raise QarnotGenericException(response.json()['message'])\\n return Profile(response.json())\",\n \"def load_internal(self, name, profile='default', **kwargs):\\n version = kwargs.get('version', '1.0')\\n uri = self.conf['uri']\\n uri = uri.format(name=name, profile=profile, version=version)\\n if uri.startswith('http://') or uri.startswith('https://'):\\n r = requests.get(uri)\\n if r.status_code == 200:\\n return yaml.load(r.content)\\n import os\\n if not os.path.isabs(uri):\\n uri = os.path.join(self.context.workdir, uri)\\n with open(uri, 'r') as f:\\n content = f.read()\\n return yaml.load(content, Loader=yaml.FullLoader)\",\n \"def user_loader(self, email):\\n if not self.um.check_file_exists(\\\"users.json\\\"):\\n return\\n\\n if email not in self.users:\\n return\\n\\n user = User()\\n user.id = email\\n return user\",\n \"def load(self, xml):\\n\\n # If the argument is a etree Element, use it as the tree root. This allows nesting of schemas\\n if isinstance(xml, etree._Element):\\n tree = xml\\n\\n # If the argument is a string, parse it using lxml.etree.fromstring()\\n else:\\n tree = etree.fromstring(xml)\\n\\n # Extract data using the schema's fields\\n data = self.dict_type()\\n ignore_missing = self.ignore_missing\\n\\n for name, field in self._fields.items():\\n try:\\n data[name] = field.load(tree)\\n except MissingFieldError as e:\\n # Note the use of \\\"is True\\\" below, this on purpose\\n if field.required is True or not ignore_missing:\\n raise e\\n\\n # Post-processing\\n data = self.post_load(data)\\n if not isinstance(data, (self.dict_type, dict)):\\n raise TypeError(f'post_load() must return a dict or dict-like object, not {type(data)}')\\n\\n return data\",\n \"def load_profile(dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"rb\\\") as f:\\n logger = pickle.load(f)\\n return logger\",\n \"def load(filename):\\n return XMLReader().from_file(filename)\",\n \"def new(cls, xml):\\n return oxml.parse_xml(xml)\",\n \"def open_profile_menager():\\r\\n reload(edc_profile_manager)\\r\\n edc_profile_manager.ProfileManager()\",\n \"def get(self, id):\\n adm = Administration()\\n prof = adm.get_profile_by_id(id)\\n return prof\",\n \"def get(self, id):\\n adm = Administration()\\n prof = adm.get_profile_by_id(id)\\n return prof\",\n \"def load_xml(filename):\\n path = dirname(__file__)\\n with open(join(path, 'data', filename)) as file:\\n content = file.read()\\n return content\",\n \"def part_from_xml(cls, xml_score_part, xml_tree, song):\\n part = cls()\\n part.set_xml(xml_score_part, xml_tree)\\n part.name = part.get_xml_part_name(xml_score_part)\\n part.song = song\\n\\n return part\",\n \"def _sloppy_parse_profiles (self, contents):\\n profile_start = contents.find('profiles\\\":')\\n profile_list_start = contents.find('profilesList')\\n if int(profile_start) > -1 and int(profile_list_start) > -1:\\n try:\\n try:\\n return json.loads('{\\\"a\\\":{\\\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\\n except ValueError, e:\\n return None\\n except TypeError, e:\\n return None\\n return None\",\n \"def test_get(self):\\r\\n profile = self.profile_manager.get('testing')\\r\\n self.assertIsInstance(profile, Profile)\",\n \"def get_profile(profile_id):\\n profile = Profile.objects.get(id=profile_id)\\n return profile\",\n \"def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\\n Dict[str, List[str]]]:\\n individuals = [[]]\\n sublist = 0\\n content = profiles_file.readlines()\\n for i in content:\\n if i != '\\\\n':\\n individuals[sublist].append(i)\\n else:\\n sublist += 1\\n individuals.append([])\\n return sort_profile(individuals)\",\n \"def __init__(self, xml_text):\\n logger.verbose(\\\"Load Version.xml\\\")\\n self.parse(xml_text)\",\n \"def use_profile(self, profile_name, workdir=None):\\n error_messages = []\\n\\n # Clear all the current configuration before loading a new profile\\n self._w3af_core.plugins.zero_enabled_plugins()\\n MiscSettings().set_default_values()\\n self._w3af_core.uri_opener.settings.set_default_values()\\n\\n if profile_name is None:\\n # If the profile name is None, I just clear the enabled plugins and\\n # return\\n return\\n\\n # This might raise an exception (which we don't want to handle) when\\n # the profile does not exist\\n profile_inst = profile(profile_name, workdir)\\n \\n # It exists, work with it!\\n\\n # Set the target settings of the profile to the core\\n self._w3af_core.target.set_options(profile_inst.get_target())\\n\\n # Set the misc and http settings\\n try:\\n profile_misc_settings = profile_inst.get_misc_settings()\\n except BaseFrameworkException, e:\\n msg = ('Setting the framework misc-settings raised an exception'\\n ' due to unknown or invalid configuration parameters. %s')\\n error_messages.append(msg % e)\\n else:\\n #\\n # IGNORE the following parameters from the profile:\\n # - misc_settings.local_ip_address\\n #\\n if 'local_ip_address' in profile_inst.get_misc_settings():\\n local_ip = get_local_ip()\\n profile_misc_settings['local_ip_address'].set_value(local_ip)\\n\\n misc_settings = MiscSettings()\\n misc_settings.set_options(profile_misc_settings)\\n\\n try:\\n http_settings = profile_inst.get_http_settings()\\n except BaseFrameworkException, e:\\n msg = ('Setting the framework http-settings raised an exception'\\n ' due to unknown or invalid configuration parameters. %s')\\n error_messages.append(msg % e)\\n else:\\n self._w3af_core.uri_opener.settings.set_options(http_settings)\\n\\n #\\n # Handle plugin options\\n #\\n error_fmt = ('The profile you are trying to load (%s) seems to be'\\n ' outdated, this is a common issue which happens when the'\\n ' framework is updated and one of its plugins adds/removes'\\n ' one of the configuration parameters referenced by a'\\n ' profile, or the plugin is removed all together.\\\\n\\\\n'\\n\\n 'The profile was loaded but some of your settings might'\\n ' have been lost. This is the list of issues that were'\\n ' found:\\\\n\\\\n'\\n ' - %s\\\\n'\\n\\n '\\\\nWe recommend you review the specific plugin'\\n ' configurations, apply the required changes and save'\\n ' the profile in order to update it and avoid this'\\n ' message. If this warning does not disappear you can'\\n ' manually edit the profile file to fix it.')\\n\\n core_set_plugins = self._w3af_core.plugins.set_plugins\\n\\n for plugin_type in self._w3af_core.plugins.get_plugin_types():\\n plugin_names = profile_inst.get_enabled_plugins(plugin_type)\\n\\n # Handle errors that might have been triggered from a possibly\\n # invalid profile\\n try:\\n unknown_plugins = core_set_plugins(plugin_names, plugin_type,\\n raise_on_error=False)\\n except KeyError:\\n msg = ('The profile references the \\\"%s\\\" plugin type which is'\\n ' unknown to the w3af framework.')\\n error_messages.append(msg % plugin_type)\\n continue\\n \\n for unknown_plugin in unknown_plugins:\\n msg = ('The profile references the \\\"%s.%s\\\" plugin which is'\\n ' unknown in the current framework version.')\\n error_messages.append(msg % (plugin_type, unknown_plugin))\\n\\n # Now we set the plugin options, which can also trigger errors with\\n # \\\"outdated\\\" profiles that users could have in their ~/.w3af/\\n # directory.\\n for plugin_name in set(plugin_names) - set(unknown_plugins):\\n\\n try:\\n plugin_options = profile_inst.get_plugin_options(\\n plugin_type,\\n plugin_name)\\n self._w3af_core.plugins.set_plugin_options(plugin_type,\\n plugin_name,\\n plugin_options)\\n except BaseFrameworkException, w3e:\\n msg = ('Setting the options for plugin \\\"%s.%s\\\" raised an'\\n ' exception due to unknown or invalid configuration'\\n ' parameters. %s')\\n error_messages.append(msg % (plugin_type, plugin_name, w3e))\\n\\n if error_messages:\\n msg = error_fmt % (profile_name, '\\\\n - '.join(error_messages))\\n raise BaseFrameworkException(msg)\",\n \"def _loadData(self, data):\\n self._data = data\\n self.createdAt = utils.toDatetime(data.attrib.get('createdAt'))\\n self.email = data.attrib.get('email')\\n self.friend = utils.cast(bool, data.attrib.get('friend'))\\n self.friendlyName = data.attrib.get('friendlyName')\\n self.home = utils.cast(bool, data.attrib.get('home'))\\n self.id = utils.cast(int, data.attrib.get('id'))\\n self.server = utils.cast(bool, data.attrib.get('server'))\\n self.servers = self.findItems(data, MyPlexServerShare)\\n self.thumb = data.attrib.get('thumb')\\n self.username = data.attrib.get('username', '')\\n for server in self.servers:\\n server.accountID = self.id\",\n \"def __init__(self, profile: AskarProfile):\\n self._profile = profile\",\n \"def load(self, id=None):\\n with open(self.location, \\\"rt\\\") as f:\\n line = f.readline()\\n n = int(line.split(\\\":\\\")[1].strip())\\n\\n # Create a new TempProfile instance with dimension `n` and the\\n # provided ID.\\n inst = self.TempProfile([n], id=id)\\n\\n f.readline() # skip table header\\n for i in range(n):\\n time, temp = f.readline().split()\\n inst.time[i] = int(time)\\n inst.temperature[i] = int(temp)\\n return inst\",\n \"def load(self):\\n\\n self.refresh_token()\\n\\n endpoint = app.config['API']['url'] + 'user/get/' + self._id\\n response = requests.get(\\n endpoint,\\n verify = app.config['API']['verify_ssl'],\\n headers = {\\n 'Authorization': self.token,\\n 'API-Key': app.config['API_KEY']\\n },\\n )\\n\\n if response.status_code != 200:\\n self.logger.error('[%s] %s' % (\\n response.status_code, response.reason)\\n )\\n err = \\\"Could not retrieve user %s from KDM API @ %s\\\"\\n raise utils.Logout(err % (self._id, app.config['API']['url']))\\n\\n user_attribs = utils.convert_json_dict(response.json()['user'])\\n for attrib in user_attribs.keys():\\n if attrib not in ['_id', 'password']:\\n setattr(self, attrib, user_attribs[attrib])\",\n \"def _parse_xml(self, hero_file):\\n filepath = os.path.join(self._hero_folder, hero_file)\\n\\n tree = xml.etree.ElementTree.parse(filepath)\\n root = tree.getroot()\\n return root\",\n \"def from_xml_node(cls, xml_node):\\n user_id = get_xml_int_value(xml_node, Elements.ID)\\n user_name = get_xml_text_value(xml_node, Elements.NAME)\\n first_name = get_xml_text_value(xml_node, Elements.FIRST_NAME)\\n last_name = get_xml_text_value(xml_node, Elements.LAST_NAME)\\n display_name = get_xml_text_value(xml_node, Elements.DISPLAY_NAME)\\n user_email = get_xml_text_value(xml_node, Elements.EMAIL)\\n out_of_office_from = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_FROM)\\n out_of_office_until = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_UNTIL)\\n send_email = get_xml_text_value(xml_node, Elements.SEND_EMAIL)\\n user_phone = get_xml_text_value(xml_node, Elements.PHONE)\\n notes = get_xml_text_value(xml_node, Elements.NOTES)\\n ldapDn = get_xml_text_value(xml_node, Elements.LDAPDN)\\n user_type = get_xml_text_value(xml_node, Elements.TYPE)\\n groups = []\\n groups_node = get_xml_node(xml_node, Elements.MEMBER_OF, True)\\n if groups_node:\\n for group_node in groups_node.iter(tag=Elements.USER):\\n groups.append(Group.from_xml_node(group_node))\\n roles = []\\n roles_node = get_xml_node(xml_node, Elements.ROLES, True)\\n if roles_node:\\n roles = Roles.from_xml_node(roles_node)\\n domains = []\\n domains_node = get_xml_node(xml_node, Elements.DOMAINS, True)\\n if domains_node:\\n domains = Domains.from_xml_node(domains_node)\\n\\n auth_method = get_xml_text_value(xml_node, Elements.AUTHENTICATION_METHOD)\\n try:\\n user_type_attrib = xml_node.attrib[Attributes.XSI_NAMESPACE_TYPE]\\n except KeyError:\\n user_type_attrib = None\\n return cls(user_id, user_name, user_email, out_of_office_from, out_of_office_until, send_email, notes, ldapDn,\\n first_name, last_name, display_name, groups, roles, domains, auth_method, user_type_attrib,\\n user_type, user_phone)\",\n \"def get(self, name='default'):\\r\\n if name not in self._cache:\\r\\n full_path = os.path.join(self._base_folder, name)\\r\\n self._create_if_needed(full_path)\\r\\n self._cache[name] = Profile(full_path, name)\\r\\n return self._cache[name]\",\n \"def parsexml(self):\\n raise NotImplementedError\",\n \"def loadProfiles():\\n with open(userProfilesDir, \\\"r\\\") as infile:\\n profiles = json.loads(\\\"\\\\n\\\".join(infile.readlines()))\\n infile.close()\\n return profiles\",\n \"def _load_data(self, filename):\\n if not os.path.isfile(filename):\\n return False\\n\\n with open(filename) as f:\\n data = pickle.load(f)\\n if data:\\n self.profiles = data['profiles']\\n self.user_data = data['user_data']\\n self.api_data = data['api_data']\\n else:\\n return False\",\n \"def from_xml_node(cls, xml_node):\\n user_id = get_xml_int_value(xml_node, Elements.ID)\\n user_name = get_xml_text_value(xml_node, Elements.NAME)\\n user_email = get_xml_text_value(xml_node, Elements.EMAIL)\\n out_of_office_from = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_FROM)\\n out_of_office_until = get_xml_text_value(xml_node, Elements.OUT_OF_OFFICE_UNTIL)\\n send_email = get_xml_text_value(xml_node, Elements.SEND_EMAIL)\\n notes = get_xml_text_value(xml_node, Elements.NOTES)\\n user_type = get_xml_text_value(xml_node, Elements.TYPE)\\n ldapDn = get_xml_text_value(xml_node, Elements.LDAPDN)\\n g_permission_node = get_xml_node(xml_node, Elements.GROUPPERMISSIONS, True)\\n if g_permission_node:\\n group_permission = Group_Permissions.from_xml_node(g_permission_node)\\n else:\\n group_permission = None\\n\\n members_node = get_xml_node(xml_node, Elements.MEMBERS, True)\\n if members_node:\\n members = Members.from_xml_node(members_node)\\n else:\\n members = []\\n roles_node = get_xml_node(xml_node, Elements.ROLES, True)\\n if roles_node:\\n roles = Roles.from_xml_node(roles_node)\\n else:\\n roles = []\\n return cls(user_id, user_name, user_email, out_of_office_from, out_of_office_until, send_email, notes, ldapDn,\\n group_permission, members, user_type, roles)\",\n \"def parseXML(self, xml):\\n return BeautifulSoup(xml, \\\"xml\\\")\",\n \"def load(self, path):\\n if not os.path.isfile(path):\\n message = 'Error: The xml was not found, path to search: ' + path\\n raise IOError(message)\\n\\n self._xml = Et.parse(path)\\n self._path_to_xml = os.path.abspath(path)\",\n \"def load(cls, filepath):\\n # Load profile from disk\\n with open(filepath, \\\"rb\\\") as infile:\\n data = pickle.load(infile)\\n\\n # remove profiler class if it exists\\n profiler_class = data.pop('profiler_class', None)\\n\\n # if the user didn't load from the a given profiler class, we need\\n # to determine which profiler is being loaded.\\n profiler_cls = cls\\n if cls is BaseProfiler:\\n if profiler_class == 'StructuredProfiler':\\n profiler_cls = StructuredProfiler\\n elif profiler_class == 'UnstructuredProfiler':\\n profiler_cls = UnstructuredProfiler\\n elif profiler_class is None: # deprecated case\\n profiler_cls = StructuredProfiler\\n if '_empty_line_count' in data:\\n profiler_cls = UnstructuredProfiler\\n else:\\n raise ValueError(f'Invalid profiler class {profiler_class} '\\n f'failed to load.')\\n\\n profile_options = profiler_cls._option_class()\\n profile_options.data_labeler.is_enabled = False\\n profiler = profiler_cls(None, options=profile_options)\\n\\n for key in data:\\n setattr(profiler, key, data[key])\\n\\n # Restore all data labelers\\n profiler._restore_data_labelers()\\n return profiler\",\n \"def _getProfileFromUser(self):\\n # Make sure user is authenticated\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n # Get Profile from datastore\\n user_id = user.email()\\n p_key = ndb.Key(Profile, user_id)\\n profile = p_key.get()\\n # Create new Profile if not there\\n if not profile:\\n profile = Profile(\\n key = p_key,\\n displayName = user.nickname(),\\n mainEmail= user.email(),\\n teeShirtSize = str(TeeShirtSize.NOT_SPECIFIED),\\n )\\n profile.put()\\n return profile\",\n \"def loadProfile(fname):\\n \\n x = np.loadtxt(fname)\\n return x[:,1]\",\n \"def load(self):\\r\\n self.domain.get_attributes(self.name, item=self)\",\n \"def test_components_profile(self):\\r\\n\\t\\tprofile = Profile.objects.get(bio=\\\"I'm a female profile with inserted components\\\")\\r\\n\\t\\tself.assertEqual(self.u1.profile, profile)\",\n \"def parse(self):\\n p = feedparser.parse(self.xml)\\n self.p = p\\n return p\",\n \"def load_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n if len(profiles) == 0:\\r\\n #Just in case\\r\\n profiles[\\\"Default\\\"] = Profile()\\r\\n profiles[\\\"Default\\\"].Name = \\\"Default\\\"\\r\\n #Some default templates\\r\\n profiles[\\\"Default\\\"].FileTemplate = \\\"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\\\"\\r\\n profiles[\\\"Default\\\"].FolderTemplate = \\\"{<publisher>}\\\\{<imprint>}\\\\{<series>}{ (<startyear>{ <format>})}\\\"\\r\\n \\r\\n if not lastused:\\r\\n lastused = [profiles.keys()[0]]\\r\\n \\r\\n return profiles, lastused\",\n \"def from_xml(self, xml_data):\\n data = BeautifulStoneSoup(xml_data.find('string').contents[0])\\n data = data.contents[0]\\n self.bikes = int(data.find('ready_bikes').contents[0])\\n self.free = int(data.find('empty_locks').contents[0])\\n self.name = str(data.find('description').contents[0])\\n self.lat = int(float(data.find('latitude').contents[0])*1E6)\\n self.lng = int(float(data.find('longitute').contents[0])*1E6)\\n return self\",\n \"def _verify_profile(self, account_id: str, profile_id: str) -> str:\\n profile = self.__ingest_profile\\n if profile_id and self.__ip.ProfileExists(account_id=account_id, profile_id=profile_id):\\n profile = profile_id\\n elif self.__ingest_profile=='':\\n response = self.__ip.GetDefaultProfile(account_id=account_id)\\n if response.status_code in DynamicIngest.success_responses:\\n profile = response.json().get('default_profile_id')\\n return profile\",\n \"def load(path):\\n\\n parser = ConfigParser()\\n parser.read(str(path))\\n\\n def _get(section, option):\\n try:\\n return parser.get(section, option)\\n except (NoSectionError, NoOptionError):\\n return None\\n\\n profiles = {}\\n\\n for section in parser.sections():\\n profiles[section] = Profile(\\n domain=_get(section, \\\"domain\\\"),\\n protocol=_get(section, \\\"protocol\\\"),\\n client_id=_get(section, \\\"client_id\\\"),\\n client_secret=_get(section, \\\"client_secret\\\"),\\n )\\n\\n return profiles\",\n \"def _parse_xml_state(self, xml):\\n if not xml:\\n return None\\n LOG.info(xml)\\n state = xmltodict.parse(xml)\\n self.community = parse_community_cards(state)\\n if self.community:\\n if len(self.community) > 5:\\n raise StateParseException(\\\"More than 5 community cards: %s\\\"\\n % self.community)\\n LOG.info(\\\"community: %s\\\", self.community)\\n self.players = parse_players(state)\\n self.pot, self.max_bet, self.my_bet = self._get_bets()\\n return state\",\n \"def from_xml(path: str):\\n sch = Schedule()\\n sch.load(path)\\n return sch\",\n \"def FromXml(as_xml):\\n try:\\n parsed = minidom.parseString(as_xml)\\n except:\\n #this is just necessary for python2\\n parsed = minidom.parseString(as_xml.encode('utf-8'))\\n story = Story()\\n story.story_id = int(parsed.getElementsByTagName('id')[0].firstChild.data)\\n story.url = parsed.getElementsByTagName('url')[0].firstChild.data\\n story.owned_by = XmlDocument.GetDataFromTag(parsed, 'owned_by')\\n story.created_at = XmlDocument.ParseDatetimeIntoSecs(parsed, 'created_at')\\n story.updated_at = XmlDocument.ParseDatetimeIntoSecs(parsed, 'updated_at')\\n story.requested_by = XmlDocument.GetDataFromTag(parsed, 'requested_by')\\n iteration = XmlDocument.GetDataFromTag(parsed, 'number')\\n story.jira_url = XmlDocument.GetDataFromTag(parsed, 'jira_url')\\n story.jira_id = XmlDocument.GetDataFromTag(parsed, 'jira_id')\\n story.zendesk_url = XmlDocument.GetDataFromTag(parsed, 'zendesk_url')\\n story.zendesk_id = XmlDocument.GetDataFromTag(parsed, 'zendesk_id')\\n\\n if iteration:\\n story.iteration_number = int(iteration)\\n\\n tasks = XmlDocument.GetTagListForTag(parsed, \\\"task\\\")\\n if tasks is not None:\\n story.tasks = []\\n for task in tasks:\\n storyTaskDictionary = dict(description=None, complete=False, id=None)\\n storyTaskDictionary['description'] = XmlNodeList.toText(task.getElementsByTagName(\\\"description\\\"))\\n storyTaskDictionary['complete'] = XmlNodeList.toBool(task.getElementsByTagName('complete'))\\n storyTaskDictionary['id'] = XmlNodeList.toText(task.getElementsByTagName(\\\"id\\\"))\\n story.tasks.append(Task.FromDictionary(storyTaskDictionary))\\n\\n story.SetStoryType(\\n parsed.getElementsByTagName('story_type')[0].firstChild.data)\\n story.SetCurrentState(\\n parsed.getElementsByTagName('current_state')[0].firstChild.data)\\n story.SetName(XmlDocument.GetDataFromTag(parsed, 'name'))\\n story.SetDescription(XmlDocument.GetDataFromTag(parsed, 'description'))\\n story.SetDeadline(XmlDocument.ParseDatetimeIntoSecs(parsed, 'deadline'))\\n\\n estimate = XmlDocument.GetDataFromTag(parsed, 'estimate')\\n if estimate is not None:\\n story.estimate = estimate\\n labels = XmlDocument.GetDataFromTag(parsed, 'labels')\\n if labels is not None:\\n story.AddLabelsFromString(labels)\\n\\n story.ClearUpdatedFields()\\n return story\",\n \"def _from_xml(self, xml_procstep):\\n try:\\n if xml_procstep.tag == 'lineage':\\n self.proc_step.clear_widgets(add_another=False)\\n xml_procstep = xml_procstep.findall('procstep')\\n if xml_procstep:\\n for procstep in xml_procstep:\\n procdesc_widget = self.proc_step.add_another()\\n procdesc_widget._from_xml(procstep)\\n else:\\n self.proc_step.add_another()\\n\\n except KeyError:\\n pass\",\n \"def from_xml_node(cls, xml_node):\\n users = []\\n for user_node in xml_node.findall(Elements.USER):\\n user_type = None\\n try:\\n user_type = user_node.attrib[Attributes.XSI_NAMESPACE_TYPE]\\n except (AttributeError, TypeError, KeyError) as error:\\n logger.error(\\n \\\"Failed to get the type of the User_List element. Assuming it's User. Error: {}\\\".format(error))\\n if user_type == \\\"group\\\":\\n user = Group.from_xml_node(user_node)\\n else:\\n user = User.from_xml_node(user_node)\\n users.append(user)\\n return cls(users)\",\n \"def get_my_profile(self):\\n\\n url = self.api_base_url + \\\"user/profile\\\"\\n\\n try:\\n raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url)\\n except RequestFailed:\\n raise\\n\\n jsonified_response = json.loads(raw_response.text)\\n user_profile = jsonified_response\\n\\n return user_profile\",\n \"def getProfile(self):\\n # GET /profile\\n debugMain('getProfile')\\n return self._genericGet('/profile')\",\n \"def load_xml(self,filename):\\n self.initvars()\\n source = iter(ET.iterparse(filename, events = ('start','end')))\\n self.name = source.next()[1].tag\\n for event,elem in source:\\n if event == 'end' and elem.tag == 'row':\\n row = [None]*self.numcols()\\n for name,val in elem.attrib.items():\\n try:\\n idx = self.getColIndex(name)\\n except ColumnNotFoundError:\\n idx = len(self.cols)\\n row.append(None)\\n # Add new column to the table\\n self.cols.append(set([name]))\\n for oldrow in self.data:\\n oldrow.append(None)\\n row[idx] = val\\n self.data.append(row)\\n self.initTypes()\",\n \"def _loadData(self, data):\\n self._data = data\\n self.friend = self._initpath == self.key\\n self.allowCameraUpload = utils.cast(bool, data.attrib.get('allowCameraUpload'))\\n self.allowChannels = utils.cast(bool, data.attrib.get('allowChannels'))\\n self.allowSync = utils.cast(bool, data.attrib.get('allowSync'))\\n self.email = data.attrib.get('email')\\n self.filterAll = data.attrib.get('filterAll')\\n self.filterMovies = data.attrib.get('filterMovies')\\n self.filterMusic = data.attrib.get('filterMusic')\\n self.filterPhotos = data.attrib.get('filterPhotos')\\n self.filterTelevision = data.attrib.get('filterTelevision')\\n self.home = utils.cast(bool, data.attrib.get('home'))\\n self.id = utils.cast(int, data.attrib.get('id'))\\n self.protected = utils.cast(bool, data.attrib.get('protected'))\\n self.recommendationsPlaylistId = data.attrib.get('recommendationsPlaylistId')\\n self.restricted = data.attrib.get('restricted')\\n self.thumb = data.attrib.get('thumb')\\n self.title = data.attrib.get('title', '')\\n self.username = data.attrib.get('username', '')\\n self.servers = self.findItems(data, MyPlexServerShare)\\n for server in self.servers:\\n server.accountID = self.id\",\n \"def test_user_has_profile_attached(self):\\n user = self.users[0]\\n self.assertTrue(hasattr(user, 'profile'))\\n self.assertIsInstance(user.profile, ImagerProfile)\",\n \"def ReadWiredNetworkProfile(self, profilename):\\n profile = {}\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n for x in config.options(profilename):\\n profile[x] = misc.Noneify(config.get(profilename, x))\\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\\n self.WiredNetwork = profile\\n return \\\"100: Loaded Profile\\\"\\n else:\\n self.WiredNetwork = None\\n return \\\"500: Profile Not Found\\\"\",\n \"def from_xml(self, doc):\\r\\n import xml.sax\\r\\n handler = DomainDumpParser(self)\\r\\n xml.sax.parse(doc, handler)\\r\\n return handler\",\n \"def _loadData(self, data):\\n self._data = data\\n self.id = utils.cast(int, data.attrib.get('id'))\\n self.accountID = utils.cast(int, data.attrib.get('accountID'))\\n self.serverId = utils.cast(int, data.attrib.get('serverId'))\\n self.machineIdentifier = data.attrib.get('machineIdentifier')\\n self.name = data.attrib.get('name')\\n self.lastSeenAt = utils.toDatetime(data.attrib.get('lastSeenAt'))\\n self.numLibraries = utils.cast(int, data.attrib.get('numLibraries'))\\n self.allLibraries = utils.cast(bool, data.attrib.get('allLibraries'))\\n self.owned = utils.cast(bool, data.attrib.get('owned'))\\n self.pending = utils.cast(bool, data.attrib.get('pending'))\",\n \"def load(xml):\\n if isinstance(xml, XmlReader):\\n for n in _process(xml): yield n\\n else:\\n with XmlReader.Create(xml) as xr:\\n for n in _process(xr): yield n\",\n \"def load_xml_startup_script(name):\\n\\n for dir in (\\\"%sshare/gps/support/core/\\\" % GPS.get_system_dir(),\\n \\\"%sshare/gps/support/ui/\\\" % GPS.get_system_dir(),\\n \\\"%sshare/gps/library/\\\" % GPS.get_system_dir(),\\n \\\"%sshare/gps/plug-ins/\\\" % GPS.get_system_dir()):\\n\\n try:\\n f = file(\\\"%s%s\\\" % (dir, name)).read()\\n break\\n except:\\n f = None\\n\\n GPS.parse_xml(f)\\n process_all_events()\",\n \"def profile(self) -> Profile:\\n return self._profile\",\n \"def profile(self) -> Profile:\\n return self._profile\",\n \"def load_info(self, node):\\n self.objtype = None\\n self.usable = not xmlutil.getboolattr(node, \\\"unusable\\\")\\n self.apsize = self.irapsize = DEFAULT_APSIZE\\n self.apstd = self.irapstd = None\\n self.basedon = self.irbasedon = 0\\n self.variability = 0.0\\n for child in node:\\n tagn = child.tag\\n if tagn == \\\"objtype\\\":\\n self.objtype = xmlutil.gettext(child)\\n elif tagn == \\\"apsize\\\":\\n self.apsize = xmlutil.getfloat(child)\\n elif tagn == \\\"irapsize\\\":\\n self.irapsize = xmlutil.getfloat(child)\\n elif tagn == \\\"apstd\\\":\\n self.apstd = xmlutil.getfloat(child)\\n elif tagn == \\\"irapstd\\\":\\n self.irapstd = xmlutil.getfloat(child)\\n elif tagn == \\\"basedon\\\":\\n self.basedon = xmlutil.getint(child)\\n elif tagn == \\\"irbasedon\\\":\\n self.irbasedon = xmlutil.getint(child)\\n elif tagn == \\\"variability\\\":\\n self.variability = xmlutil.getfloat(child)\",\n \"def load_xml(pmid, sleep=None):\\r\\n link = \\\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&retmode=xml&id=%s\\\" % str(pmid) \\r\\n page = requests.get(link) \\r\\n tree = html.fromstring(page.content)\\r\\n if sleep is not None:\\r\\n time.sleep(sleep)\\r\\n return tree\",\n \"def get_profile(path=\\\"~\\\"):\\n global profiles\\n profile = profiles.get(path,None)\\n if not profile:\\n profile = InitFileConfig(os.path.join(path,\\\".myradioprofile\\\"), {} )\\n profiles[path] = profile\\n return profile\",\n \"def get_user_profile(self):\\n\\t\\treturn Job(SDK.PrlSrv_GetUserProfile(self.handle)[0])\",\n \"def load_xml(pmid, sleep=None):\\n link = \\\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&retmode=xml&id=%s\\\" % str(pmid) + \\\"&api_key=\\\" + API_KEY\\n print(link)\\n page = requests.get(link)\\n tree = html.fromstring(page.content)\\n if sleep is not None:\\n time.sleep(sleep)\\n return tree\",\n \"def read_pardus_profiles(self):\\n\\n self.lan_config = ConfigParser.ConfigParser()\\n self.lan_config.read(self.lan_config_path)\\n connection_type = \\\"802-3-ethernet\\\"\\n for section in self.lan_config.sections():\\n lan_settings = {}\\n for option in self.lan_config.options(section):\\n if option == \\\"device\\\":\\n #To strip device name from long device string\\n lan_settings[option] = self.lan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n lan_settings[option] = self.lan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, lan_settings)\\n self.pardus_profiles.append(p)\\n\\n self.wlan_config = ConfigParser.ConfigParser()\\n self.wlan_config.read(self.wlan_config_path)\\n connection_type = \\\"802-11-wireless\\\"\\n for section in self.wlan_config.sections():\\n wlan_settings = {}\\n for option in self.wlan_config.options(section):\\n if option == \\\"device\\\":\\n wlan_settings[option] = self.wlan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n wlan_settings[option] = self.wlan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\\n self.pardus_profiles.append(p)\",\n \"def loads(xtext):\\n\\n class XmlLoads(object):\\n \\\"\\\"\\\"\\n XMLLOADS implements 'YZXml.loads' functionality\\n \\\"\\\"\\\"\\n\\n def __init__(self, xtext):\\n self.index = 0\\n self.xtext = xtext\\n self.length = len(xtext)\\n self.stack = []\\n\\n def loads(self):\\n \\\"\\\"\\\"\\n LOADS converts xml-string to object-string\\n\\n Returns\\n -------\\n - dict\\n object-xml contains {'tag': str, 'attribs': dict, 'elements': list}\\n \\\"\\\"\\\"\\n\\n self.ignore_whitespaces()\\n\\n if self.index >= self.length:\\n return\\n\\n if self.xtext[self.index] == '<':\\n self.index += 1\\n\\n if self.xtext[self.index] == '/':\\n self.index += 1\\n\\n tag = self.read_until('>')\\n self.index += 1\\n\\n elements = []\\n while len(self.stack) > 0 and\\\\\\n (isinstance(self.stack[-1], str) or self.stack[-1]['tag'] != tag):\\n elements.append(self.stack.pop())\\n\\n assert len(self.stack) > 0\\n\\n self.stack[-1]['elements'].extend(reversed(elements))\\n\\n else:\\n self.ignore_whitespaces()\\n tag = self.read_until(' >')\\n\\n attribs = {}\\n if self.xtext[self.index] != '>':\\n attribs = self.read_attribs()\\n\\n self.index += 1\\n self.stack.append({'tag': tag, 'attribs': attribs, 'elements': []})\\n else:\\n self.stack.append(self.read_until('<').strip())\\n\\n self.loads()\\n\\n\\n def ignore_whitespaces(self):\\n \\\"\\\"\\\"\\n IGNORE_WHITESPACES reads whitespaces and advances self.index\\n \\\"\\\"\\\"\\n\\n whitespaces = [' ', '\\\\t', '\\\\n', '\\\\r']\\n while self.index < self.length and self.xtext[self.index] in whitespaces:\\n self.index += 1\\n\\n def read_until(self, chars):\\n \\\"\\\"\\\"\\n READ_UNTIL reads charaters and advances self.index\\n unitl reaches any character in 'cahrs'\\n\\n Parameters\\n ----------\\n - chars: str\\n stoping characters\\n \\\"\\\"\\\"\\n\\n start_index = self.index\\n\\n while self.index < self.length and self.xtext[self.index] not in chars:\\n self.index += 1\\n\\n assert self.index < self.length\\n\\n return self.xtext[start_index:self.index]\\n\\n def read_attribs(self):\\n \\\"\\\"\\\"\\n READ_ATTRIBS reads attributes of an elements\\n \\\"\\\"\\\"\\n\\n attribs = {}\\n while self.index < self.length:\\n self.ignore_whitespaces()\\n if self.xtext[self.index] == '>':\\n break\\n name = self.read_until('=')\\n self.index += 1\\n self.read_until('\\\"')\\n self.index += 1\\n value = self.read_until('\\\"')\\n self.index += 1\\n\\n attribs[name] = value\\n\\n return attribs\\n\\n xmlloads = XmlLoads(xtext)\\n xmlloads.loads()\\n return xmlloads.stack.pop()\",\n \"def load(self):\\n pass\",\n \"def load(self):\\n pass\",\n \"def load(self):\\n pass\",\n \"def load(self):\\n pass\",\n \"def inspect(\\n name: Optional[str] = typer.Argument(\\n None, help=\\\"Name of profile to inspect; defaults to active profile.\\\"\\n )\\n):\\n profiles = prefect.settings.load_profiles()\\n if name is None:\\n current_profile = prefect.context.get_settings_context().profile\\n if not current_profile:\\n exit_with_error(\\\"No active profile set - please provide a name to inspect.\\\")\\n name = current_profile.name\\n print(f\\\"No name provided, defaulting to {name!r}\\\")\\n if name not in profiles:\\n exit_with_error(f\\\"Profile {name!r} not found.\\\")\\n\\n if not profiles[name].settings:\\n # TODO: Consider instructing on how to add settings.\\n print(f\\\"Profile {name!r} is empty.\\\")\\n\\n for setting, value in profiles[name].settings.items():\\n app.console.print(f\\\"{setting.name}='{value}'\\\")\",\n \"def set_default_profile(self):\\n profile = textwrap.dedent(\\n \\\"\\\"\\\"\\n config:\\n boot.autostart: \\\"true\\\"\\n description: Default LXD profile\\n devices:\\n eth0:\\n name: eth0\\n nictype: bridged\\n parent: conjureup1\\n type: nic\\n eth1:\\n name: eth1\\n nictype: bridged\\n parent: conjureup0\\n type: nic\\n root:\\n path: /\\n pool: default\\n type: disk\\n name: default\\n \\\"\\\"\\\")\\n with NamedTemporaryFile(mode='w', encoding='utf-8',\\n delete=False) as tempf:\\n utils.spew(tempf.name, profile)\\n out = utils.run_script(\\n 'cat {} |conjure-up.lxc profile edit default'.format(\\n tempf.name))\\n if out.returncode != 0:\\n raise Exception(\\\"Problem setting default profile: {}\\\".format(\\n out))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60300845","0.5605986","0.55443424","0.5518848","0.5350278","0.5345203","0.53076","0.52905643","0.5275819","0.52674824","0.521211","0.5208215","0.5208047","0.51756513","0.51629275","0.51257974","0.50973666","0.5068245","0.50489193","0.5018405","0.50177634","0.4951285","0.4947764","0.49426383","0.49330294","0.49206278","0.49055412","0.48771116","0.48708406","0.48696178","0.48575997","0.48460478","0.48427466","0.48412192","0.4821604","0.48152724","0.48116645","0.48085704","0.48085704","0.47966042","0.47776672","0.4774709","0.47736958","0.47668973","0.47616395","0.4744385","0.4741202","0.4735502","0.47265267","0.472469","0.47157076","0.4708562","0.47027177","0.4700868","0.46992746","0.46970963","0.46935937","0.46882376","0.4685201","0.4671229","0.4670379","0.46576294","0.4643836","0.4636546","0.46328995","0.4622748","0.46211946","0.46176523","0.46163213","0.46112648","0.46100074","0.46073154","0.46068028","0.46046782","0.4600911","0.458931","0.4583774","0.45773914","0.45743686","0.45676258","0.4567212","0.45582205","0.4557048","0.45519105","0.45505893","0.45462218","0.45462218","0.45460185","0.45459804","0.4544569","0.45412236","0.45367175","0.45330817","0.4532116","0.45306283","0.45306283","0.45306283","0.45306283","0.45296696","0.45244482"],"string":"[\n \"0.60300845\",\n \"0.5605986\",\n \"0.55443424\",\n \"0.5518848\",\n \"0.5350278\",\n \"0.5345203\",\n \"0.53076\",\n \"0.52905643\",\n \"0.5275819\",\n \"0.52674824\",\n \"0.521211\",\n \"0.5208215\",\n \"0.5208047\",\n \"0.51756513\",\n \"0.51629275\",\n \"0.51257974\",\n \"0.50973666\",\n \"0.5068245\",\n \"0.50489193\",\n \"0.5018405\",\n \"0.50177634\",\n \"0.4951285\",\n \"0.4947764\",\n \"0.49426383\",\n \"0.49330294\",\n \"0.49206278\",\n \"0.49055412\",\n \"0.48771116\",\n \"0.48708406\",\n \"0.48696178\",\n \"0.48575997\",\n \"0.48460478\",\n \"0.48427466\",\n \"0.48412192\",\n \"0.4821604\",\n \"0.48152724\",\n \"0.48116645\",\n \"0.48085704\",\n \"0.48085704\",\n \"0.47966042\",\n \"0.47776672\",\n \"0.4774709\",\n \"0.47736958\",\n \"0.47668973\",\n \"0.47616395\",\n \"0.4744385\",\n \"0.4741202\",\n \"0.4735502\",\n \"0.47265267\",\n \"0.472469\",\n \"0.47157076\",\n \"0.4708562\",\n \"0.47027177\",\n \"0.4700868\",\n \"0.46992746\",\n \"0.46970963\",\n \"0.46935937\",\n \"0.46882376\",\n \"0.4685201\",\n \"0.4671229\",\n \"0.4670379\",\n \"0.46576294\",\n \"0.4643836\",\n \"0.4636546\",\n \"0.46328995\",\n \"0.4622748\",\n \"0.46211946\",\n \"0.46176523\",\n \"0.46163213\",\n \"0.46112648\",\n \"0.46100074\",\n \"0.46073154\",\n \"0.46068028\",\n \"0.46046782\",\n \"0.4600911\",\n \"0.458931\",\n \"0.4583774\",\n \"0.45773914\",\n \"0.45743686\",\n \"0.45676258\",\n \"0.4567212\",\n \"0.45582205\",\n \"0.4557048\",\n \"0.45519105\",\n \"0.45505893\",\n \"0.45462218\",\n \"0.45462218\",\n \"0.45460185\",\n \"0.45459804\",\n \"0.4544569\",\n \"0.45412236\",\n \"0.45367175\",\n \"0.45330817\",\n \"0.4532116\",\n \"0.45306283\",\n \"0.45306283\",\n \"0.45306283\",\n \"0.45306283\",\n \"0.45296696\",\n \"0.45244482\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5006978"},"document_rank":{"kind":"string","value":"21"}}},{"rowIdx":11589,"cells":{"query":{"kind":"string","value":"Load profiles from a xml file. If no profiles are found it creates a blank profile. file_path>The absolute path to the profile file Returns a dict of the found profiles and a list of the lastused profile(s)"},"document":{"kind":"string","value":"def load_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n if len(profiles) == 0:\r\n #Just in case\r\n profiles[\"Default\"] = Profile()\r\n profiles[\"Default\"].Name = \"Default\"\r\n #Some default templates\r\n profiles[\"Default\"].FileTemplate = \"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\"\r\n profiles[\"Default\"].FolderTemplate = \"{<publisher>}\\{<imprint>}\\{<series>}{ (<startyear>{ <format>})}\"\r\n \r\n if not lastused:\r\n lastused = [profiles.keys()[0]]\r\n \r\n return profiles, lastused"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused","def import_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n return profiles","def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\n Dict[str, List[str]]]:\n individuals = [[]]\n sublist = 0\n content = profiles_file.readlines()\n for i in content:\n if i != '\\n':\n individuals[sublist].append(i)\n else:\n sublist += 1\n individuals.append([])\n return sort_profile(individuals)","def loadProfiles():\n with open(userProfilesDir, \"r\") as infile:\n profiles = json.loads(\"\\n\".join(infile.readlines()))\n infile.close()\n return profiles","def load(path):\n\n parser = ConfigParser()\n parser.read(str(path))\n\n def _get(section, option):\n try:\n return parser.get(section, option)\n except (NoSectionError, NoOptionError):\n return None\n\n profiles = {}\n\n for section in parser.sections():\n profiles[section] = Profile(\n domain=_get(section, \"domain\"),\n protocol=_get(section, \"protocol\"),\n client_id=_get(section, \"client_id\"),\n client_secret=_get(section, \"client_secret\"),\n )\n\n return profiles","def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\r\n if lastused:\r\n writer.WriteAttributeString(\"LastUsed\", \",\".join(lastused))\r\n for profile in profiles:\r\n profiles[profile].save_to_xml(writer)\r\n writer.WriteEndElement()\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def get_profile(path=\"~\"):\n global profiles\n profile = profiles.get(path,None)\n if not profile:\n profile = InitFileConfig(os.path.join(path,\".myradioprofile\"), {} )\n profiles[path] = profile\n return profile","def _read_profiles(profile_directory):\n # Initialize key variables\n profiles = defaultdict(\n lambda: defaultdict(lambda: defaultdict()))\n\n # Read the yaml files in the profiles directory\n files = os.listdir(profile_directory)\n filenames = ['{}{}{}'.format(\n profile_directory, os.sep, nextfile) for nextfile in files]\n\n for _filename in sorted(filenames):\n # Get rid of excess os.sep separators\n pathitems = _filename.split(os.sep)\n filename = os.sep.join(pathitems)\n\n # Skip obvious\n if os.path.isfile(filename) is False:\n continue\n if filename.lower().endswith('.yaml') is False:\n continue\n\n with open(filename, 'r') as stream:\n try:\n _profiles = yaml.load(stream)['data']\n except yaml.YAMLError as exc:\n print(exc)\n\n # Create dictionary\n for item in _profiles:\n firstname = item['firstname']\n lastname = item['lastname']\n height = item['height']\n weight = item['weight']\n birthdate = item['birthdate']\n profiles[lastname][firstname][birthdate] = {\n 'height': height, 'weight': weight}\n\n return profiles","def populate_profiles_from_directory(self):\n\n self.profiles = []\n\n # Go over all the files and create a profile object\n _profile_files = listdir(self.save_dir)\n\n for profile_filename in _profile_files:\n # Only check for .yaml files\n if path.splitext(profile_filename)[1] == '.yaml':\n # Get the data and create a new profile\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\n _profile = Profile(path.splitext(profile_filename)[0])\n _profile.player_data = _file_data\n self.profiles.append(_profile)","def load_profile(path, profile):\n profiles = load(path)\n try:\n return profiles[profile]\n except KeyError:\n return Profile(None, None, None, None)","def _recurse_load_profile(self, text, profile_path):\n try:\n inherited_profile = Profile()\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\n profile_parser = _ProfileParser(text)\n # Iterate the includes and call recursive to get the profile and variables\n # from parent profiles\n for include in profile_parser.includes:\n # Recursion !!\n profile = self._load_profile(include, cwd)\n inherited_profile.compose_profile(profile)\n\n # Current profile before update with parents (but parent variables already applied)\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\n inherited_profile)\n return inherited_profile\n except ConanException:\n raise\n except Exception as exc:\n raise ConanException(\"Error parsing the profile text file: %s\" % str(exc))","def _sloppy_parse_profiles (self, contents):\n profile_start = contents.find('profiles\":')\n profile_list_start = contents.find('profilesList')\n if int(profile_start) > -1 and int(profile_list_start) > -1:\n try:\n try:\n return json.loads('{\"a\":{\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\n except ValueError, e:\n return None\n except TypeError, e:\n return None\n return None","def get_profiles(self):\n # print(self.uir) #checkpoint\n if os.path.isdir(self.uir+\"/profiles\"):\n profiles=os.listdir(self.uir+\"/profiles\")\n # print(profiles) #checkpoint\n for profile in profiles:\n wsadmin=self.uir+\"/profiles/\"+profile+\"/bin/wsadmin.bat\"\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\n self.profiles.append(self.uir+\"/profiles/\"+profile)\n\n else: print(self.uir+' Instance does not have \"profile\" folder in '+self.uir)\n return","def get_profiles(profile_file_directory):\r\n\t\r\n\tprofile_file_path = profile_file_directory+ \"/profiles.txt\"\r\n\tlist_of_all_allele_numbers_tuple = []\r\n\tdatabase = None\r\n\tlocusList = []\r\n\t\r\n\tfor l in open(profile_file_path):\r\n\t if database is None:\r\n\t\tdatabase = {}\r\n\t\tlocusList = l.split()[1:]\r\n\t\tcontinue\r\n\t t = l.split()\r\n\t st = t[0]\r\n\t v = ' '.join([s for s in t[1:]])\r\n\t if v in database:\r\n\t\tprint >> sys.stderr, 'sequence type ' + str(st) + ' is a duplicate of ' + str(database[v])\r\n\t database[v] = st\r\n\t covert_string_to_tuple_list_of_allele_numbers = tuple(int(x) for x in re.findall(\"[0-9]+\", v)) \r\n\t list_of_all_allele_numbers_tuple.append(covert_string_to_tuple_list_of_allele_numbers)\r\n\t\t\r\n\treturn (database, locusList, list_of_all_allele_numbers_tuple)","def profile(filename: str) -> 'Iterator[None]':\n profiler = Profile()\n profiler.enable()\n\n yield\n\n profiler.disable()\n profiler.create_stats()\n profiler.dump_stats('profiles/{}'.format(filename))","def list_profiles(self) -> dict:\n wsc = self.read_ws_configuration()\n out = OrderedDict()\n for name, json in wsc.profiles.items():\n out[name] = Profile(name, self.ws_data_folder / name, json)\n # Try to find current profile\n try:\n out[self.current_profile_name].is_current = True\n except Exception:\n pass\n return out","def from_file(cls, file_path, ngram_sizes, profile_len):\n profile = cls.from_files((file_path, ), ngram_sizes, profile_len)\n return profile","def profiles(self):\n if not self._profiles:\n self.GetAllProfiles()\n return self._profiles","def profile_files(profile):\n flist = os.listdir(osp.join(profile, 'startup'))\n profile_path = osp.join(osp.abspath('.'), profile)\n return [osp.join(profile_path, 'startup', x) for x in flist]","def GetAllProfiles(self):\n profiles = []\n feed_uri = self._gd_client.GetFeedUri('profiles')\n while feed_uri:\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\n profiles.extend(feed.entry)\n feed_uri = feed.FindNextLink()\n self._profiles = profiles","def load_people(self, file_path):\n pass","def read_profiles(filename):\n profiles = []\n with gzip.open(filename, mode='rt', encoding='utf8') as infile:\n for line in infile:\n profiles.append(Counter(line.split()))\n return profiles","def have_profile_dir(path, maxdepth=3, filename=\"profiles.desc\"):\n\twhile path != \"/\" and maxdepth:\n\t\tif os.path.exists(os.path.join(path, \"profiles\", filename)):\n\t\t\treturn normalize_path(path)\n\t\tpath = normalize_path(path + \"/..\")\n\t\tmaxdepth -= 1","def parse_file(file_path):\n with open(file_path) as f:\n return XmlPropertyListParser().parse(f)","def profiles_path(self) -> Path:\n return self._config.data_path / \"hmm\" / \"profiles.hmm\"","def _load_profile(self, profile_name, cwd):\n\n profile_path = self.get_profile_path(profile_name, cwd)\n try:\n text = load_user_encoded(profile_path)\n except Exception as e:\n raise ConanException(f\"Cannot load profile:\\n{e}\")\n\n # All profiles will be now rendered with jinja2 as first pass\n base_path = os.path.dirname(profile_path)\n file_path = os.path.basename(profile_path)\n context = {\"platform\": platform,\n \"os\": os,\n \"profile_dir\": base_path,\n \"profile_name\": file_path,\n \"conan_version\": conan_version}\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\n text = rtemplate.render(context)\n\n try:\n return self._recurse_load_profile(text, profile_path)\n except ConanException as exc:\n raise ConanException(\"Error reading '%s' profile: %s\" % (profile_name, exc))","def read_pardus_profiles(self):\n\n self.lan_config = ConfigParser.ConfigParser()\n self.lan_config.read(self.lan_config_path)\n connection_type = \"802-3-ethernet\"\n for section in self.lan_config.sections():\n lan_settings = {}\n for option in self.lan_config.options(section):\n if option == \"device\":\n #To strip device name from long device string\n lan_settings[option] = self.lan_config.get(section, option).split(\"_\")[-1]\n else:\n lan_settings[option] = self.lan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, lan_settings)\n self.pardus_profiles.append(p)\n\n self.wlan_config = ConfigParser.ConfigParser()\n self.wlan_config.read(self.wlan_config_path)\n connection_type = \"802-11-wireless\"\n for section in self.wlan_config.sections():\n wlan_settings = {}\n for option in self.wlan_config.options(section):\n if option == \"device\":\n wlan_settings[option] = self.wlan_config.get(section, option).split(\"_\")[-1]\n else:\n wlan_settings[option] = self.wlan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\n self.pardus_profiles.append(p)","def _find_cb_profiles():\n dir_locations = [\".carbonblack\", os.path.join(os.path.expanduser(\"~\"), \".carbonblack\")]\n cred_file = \"credentials.response\"\n profiles = []\n\n for dir in dir_locations:\n cred_file_path = os.path.join(dir, cred_file)\n _MOD_LOGGER.debug(\"Searching CB profiles on '%s'\", cred_file_path)\n if os.path.exists(cred_file_path):\n _MOD_LOGGER.debug(\"File exists, parsing...\")\n config = configparser.ConfigParser(default_section=\"cbbackend\", strict=True)\n config.read(cred_file_path)\n profiles += [sec_name for sec_name in config.keys() if sec_name != \"cbbackend\"]\n\n if profiles:\n _MOD_LOGGER.debug(\"Requested to read 'all' profiles. Found: %s\", \",\".join(profiles))\n\n return profiles","def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\n assert isinstance(profiles, list), \"Please provide a list of profiles\"\n cache = ClientCache(self._conan_api.cache_folder)\n loader = ProfileLoader(cache)\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\n profile.conf.validate()\n cache.new_config.validate()\n # Apply the new_config to the profiles the global one, so recipes get it too\n profile.conf.rebase_conf_definition(cache.new_config)\n return profile","def loadProfile(fname):\n \n x = np.loadtxt(fname)\n return x[:,1]","def parse_profiles(profiles):\n if type(profiles) == list:\n return [\n x\n for x in [ProfileSettingParser.parse(x) for x in profiles]\n if x is not None\n ]\n else:\n return []","def get_profiles(self):\n profiles = [['Profile name', 'GUID']]\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\n for row in r.fetchall():\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\n return profiles","def get_profiles(args):\n # Use profile from cli if provided\n if args.profile and not args.update_config:\n return [args.profile]\n\n # Run config to get or set the config file\n config = configparser.ConfigParser()\n\n if os.path.isfile(SETTINGS_FILE) and not args.update_config:\n # Get profiles from config\n config.read(SETTINGS_FILE)\n else:\n # Get default profiles from user\n try:\n profiles_input = input(\n 'Please enter space separated list of profiles to use: '\n )\n except KeyboardInterrupt:\n # Avoid ugly stacktrace on ctrl-c in input\n sys.exit(1)\n # Setup config\n config.add_section('profiles')\n config.set('profiles', 'default', profiles_input)\n # Write to config\n config_file = open(SETTINGS_FILE, 'w')\n config.write(config_file)\n config_file.close()\n\n return config.get('profiles', 'default').split()","def profiles(self):\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n return list(filter(lambda x: x is not None, executor.map(self.profile_details, self.profiles_names())))","def profiles(self):\n return self._profiles","def profiles(self):\n return self._profiles","def apply(self):\r\n\r\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\r\n\r\n def saving_file(xml):\r\n \"\"\" Save user profile in xml format to temp_ dir.\"\"\"\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))\r\n\r\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\r\n \"/data/sampleProfile.xml\")\r\n\r\n # The below code will parse the sample xml file\r\n # and fill important details entered by the user.\r\n root_tree = parse_xml.getroot()\r\n root_tree[0].text = self.ssid.get()\r\n root_tree[1][0][0].text = self.ssid.get()\r\n root_tree[3].text = self.connection_mode.get().lower()\r\n security = root_tree[4][0]\r\n security[0][0].text = self.authentication.get()\r\n security[0][1].text = self.encryption.get()\r\n if self.authentication.get() != \"open\":\r\n etree.SubElement(security, \"sharedKey\")\r\n etree.SubElement(security[1], \"keyType\").text = \"passPhrase\"\r\n etree.SubElement(security[1], \"protected\").text = \"false\"\r\n etree.SubElement(security[1], \"keyMaterial\").text = self.password.get()\r\n\r\n # Save the xml file\r\n saving_file(root_tree)\r\n\r\n # Add profile to the system.\r\n temp_path = 'netsh wlan add profile filename=\"' + self.app_path + \"\\\\temp_\\\\\"\r\n output_ = subprocess.run(temp_path + file_name + '.xml\"', shell=True,\r\n capture_output=True, text=True)\r\n os.remove(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\")\r\n\r\n # If unable to add profile.\r\n if output_.returncode != 0:\r\n message = \"Sorry, Unable to add profile.\\n(You entered wrong details \" \\\r\n \"or else you don't have admin rights.)\"\r\n image_ = \"error\"\r\n\r\n else:\r\n message = \"Profile added successfully (Please Refresh)\"\r\n image_ = \"warning\"\r\n\r\n MessageBox(self.parent, message, image_)","def parse_profile_list_page(page):\n page_tree = etree.HTML(page)\n profile_list = page_tree.xpath(\n '//table[@class=\"table_header\"]/tr[position() > 4 and position() < (last() - 3)]/td/node()[1]')\n if profile_list:\n profile_list[0] = \"main\"\n return [text.strip() for text in profile_list]\n else:\n raise ProfileException(\"Failed to get profile list\")","def get_list(profiles_folder, logger):\n profile_list = []\n with scandir(profiles_folder) as it:\n for entry in it:\n if entry.is_file():\n filepath = profiles_folder + entry.name\n profile = json_from_file(filepath, logger)\n if profile is not None:\n try:\n profile_list.append({\"filepath\":filepath, \"name\":profile[\"name\"], \"description\":profile[\"description\"]})\n except AttributeError:\n logger.error(\"Missing attributes in \" + filepath)\n logger.error(str(profile))\n return profile_list","def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def profile_directory_path(request, file):\n return directory_path('profile', file)","def get_resolved_profile_catalog(trestle_root: pathlib.Path, profile_path: pathlib.Path) -> cat.Catalog:\n logger.debug(f'get resolved profile catalog for {profile_path} via generated Import.')\n import_ = prof.Import(href=str(profile_path), include_all={})\n import_filter = ProfileResolver.Import(trestle_root, import_)\n logger.debug('launch pipeline')\n result = next(import_filter.process())\n return result","def list_profiles(self, params):\n return self.profiles","def profiles_names(self):\n url = get_url('profiles')\n response = self._get(url)\n raise_on_error(response)\n return response.json()","def dumpProfiles(profiles):\n with open(userProfilesDir, \"w\") as outfile:\n outfile.writelines(json.dumps(profiles, indent=4))\n outfile.close()","def ReadWiredNetworkProfile(self, profilename):\n profile = {}\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n for x in config.options(profilename):\n profile[x] = misc.Noneify(config.get(profilename, x))\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\n self.WiredNetwork = profile\n return \"100: Loaded Profile\"\n else:\n self.WiredNetwork = None\n return \"500: Profile Not Found\"","def available_profiles(cls) -> List[str]:\n return list(cfg.get(\"profiles\"))","def list(self):\n # List is to be extended (directories should not have a trailing slash)\n paths_to_ignore = ['.DS_Store']\n\n profiles = []\n cache = ClientCache(self._conan_api.cache_folder)\n profiles_path = cache.profiles_path\n if os.path.exists(profiles_path):\n for current_directory, _, files in os.walk(profiles_path, followlinks=True):\n files = filter(lambda file: os.path.relpath(\n os.path.join(current_directory, file), profiles_path) not in paths_to_ignore, files)\n\n for filename in files:\n rel_path = os.path.relpath(os.path.join(current_directory, filename),\n profiles_path)\n profiles.append(rel_path)\n\n profiles.sort()\n return profiles","def get_user():\n with open(app.config['DATA_XML'], 'r') as xmlfile:\n root = ElementTree.parse(xmlfile).getroot()\n\n for item in root.iter('server'):\n result = '{}://{}'.format(\n item.find('protocol').text,\n item.find('host').text\n )\n\n data = {\n user.attrib['id']: {\n 'name': user.find('name').text,\n 'avatar': '{}{}'.format(\n result,\n user.find('avatar').text\n )\n }\n for user in root.iter('user')\n }\n return OrderedDict(\n sorted(\n data.items(),\n key=lambda result: itemgetter('name')(itemgetter(1)(result)),\n cmp=locale.strcoll\n )\n )","def loadProfilesFromPlist_(self, plist):\r\n \r\n LogInfo(u\"Loading update profiles with PublicationDate %@\", plist[u\"PublicationDate\"])\r\n self.profiles = dict()\r\n for name, updates in plist[u\"Profiles\"].iteritems():\r\n profile = list()\r\n for update in updates:\r\n profile.append(plist[u\"Updates\"][update])\r\n self.profiles[name] = profile\r\n self.publicationDate = plist[u\"PublicationDate\"]\r\n self.updatePaths = dict()\r\n for name, update in plist[u\"Updates\"].iteritems():\r\n filename, ext = os.path.splitext(os.path.basename(update[u\"url\"]))\r\n self.updatePaths[update[u\"sha1\"]] = u\"%s(%s)%s\" % (filename, update[u\"sha1\"][:7], ext)\r\n self.deprecatedInstallerBuilds = dict()\r\n try:\r\n for replacement, builds in plist[u\"DeprecatedInstallers\"].iteritems():\r\n for build in builds:\r\n self.deprecatedInstallerBuilds[build] = replacement\r\n except KeyError:\r\n LogWarning(u\"No deprecated installers\")\r\n if self.delegate:\r\n self.delegate.profilesUpdated()","def _read_profile_file(cls, profile_file):\n qid2title: Dict[str, str] = {}\n qid2desc: Dict[str, str] = {}\n alias2qids: Dict[str, list] = {}\n type_systems: Dict[str, Dict[str, List[str]]] = {}\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\n\n num_lines = sum(1 for _ in open(profile_file))\n with open(profile_file, \"r\") as in_f:\n for line in tqdm(in_f, total=num_lines, desc=\"Reading profile\"):\n line = ujson.loads(line)\n\n # Check keys and schema\n assert all(\n k in line.keys() for k in REQUIRED_KEYS\n ), f\"A key from {REQUIRED_KEYS} was not in {line}\"\n try:\n # Asserts the types are correct\n ent = EntityObj(\n entity_id=line[\"entity_id\"],\n mentions=line[\"mentions\"],\n title=line.get(\"title\", line[\"entity_id\"]),\n description=line.get(\"description\", \"\"),\n types=line.get(\"types\", {}),\n relations=line.get(\"relations\", []),\n )\n except ValidationError as e:\n print(e.json())\n raise e\n if ent.entity_id in qid2title:\n raise ValueError(f\"{ent.entity_id} is already in our dump\")\n qid2title[ent.entity_id] = ent.title\n qid2desc[ent.entity_id] = ent.description\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\n for men_pair in ent.mentions:\n # Lower case mentions for mention extraction\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\n if new_men not in alias2qids:\n alias2qids[new_men] = []\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\n # Add type systems of type_sys -> QID -> list of type names\n for type_sys in ent.types:\n if type_sys not in type_systems:\n type_systems[type_sys] = {}\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\n # Add kg relations QID -> relation -> list of object QIDs\n for rel_pair in ent.relations:\n if \"relation\" not in rel_pair or \"object\" not in rel_pair:\n raise ValueError(\n \"For each value in relations, it must be a JSON with keys relation and object\"\n )\n if ent.entity_id not in qid2relations:\n qid2relations[ent.entity_id] = {}\n if rel_pair[\"relation\"] not in qid2relations[ent.entity_id]:\n qid2relations[ent.entity_id][rel_pair[\"relation\"]] = []\n qid2relations[ent.entity_id][rel_pair[\"relation\"]].append(\n rel_pair[\"object\"]\n )\n\n # Sort mentions based on score, highest first\n for al in list(alias2qids.keys()):\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\n # Add all qids to the type systems and KG connections with empty values\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\n for qid in qid2title:\n for type_sys in type_systems:\n if qid not in type_systems[type_sys]:\n type_systems[type_sys][qid] = []\n if qid not in qid2relations:\n qid2relations[qid] = {}\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations","def parsePlayerProfile(path, args):\n soup = BeautifulSoup(requests.get(\"{domain}/{pathStr}\".format(domain=args.domain[0], pathStr=path)).text, \"html.parser\")\n\n playerPathName = re.split(\"/|\\.\", path.lower())[-2]\n # set up so a=11, b=12, etc, then joins the values together\n playerID = \"\".join([char if char.isdigit() else str(ord(char) % 86) for char in playerPathName])\n birthday = soup.find(\"span\", id=\"necro-birth\").get(\"data-birth\")\n hasDebuted = soup.find(\"a\", string=\"Debut\")\n debut = isRetired.get(\"href\") if hasDebuted else None\n isRetired = soup.find(\"a\", string=\"Last Game\")\n retired = isRetired.get(\"href\") if isRetired else None\n\n player = parseProfileHeader(soup, playerID, birthday, debut, retired, args)\n\n positionTable = soup.find(\"table\", id=\"standard_fielding\")\n positions = parsePositionInfo(positionTable, args)\n player.addPositions(positions)\n\n hittingTable = soup.find(\"table\", id=\"batting_standard\")\n hittingStats = parseBattingTable(hittingTable, args)\n player.addStats(hittingStats, \"batting\")\n\n pitchingTable = soup.find(\"table\", id=\"pitching_standard\")\n advancedPathTag = soup.find(\"a\", href=re.compile(r\"-pitch.shtml\"), string=\"More Stats\")\n pitchingStats = parsePitchingTables(pitchingTable, advancedPathTag, args)\n player.addStats(pitchingStats, \"pitching\")\n\n return player","def load(cls, filepath):\n # Load profile from disk\n with open(filepath, \"rb\") as infile:\n data = pickle.load(infile)\n\n # remove profiler class if it exists\n profiler_class = data.pop('profiler_class', None)\n\n # if the user didn't load from the a given profiler class, we need\n # to determine which profiler is being loaded.\n profiler_cls = cls\n if cls is BaseProfiler:\n if profiler_class == 'StructuredProfiler':\n profiler_cls = StructuredProfiler\n elif profiler_class == 'UnstructuredProfiler':\n profiler_cls = UnstructuredProfiler\n elif profiler_class is None: # deprecated case\n profiler_cls = StructuredProfiler\n if '_empty_line_count' in data:\n profiler_cls = UnstructuredProfiler\n else:\n raise ValueError(f'Invalid profiler class {profiler_class} '\n f'failed to load.')\n\n profile_options = profiler_cls._option_class()\n profile_options.data_labeler.is_enabled = False\n profiler = profiler_cls(None, options=profile_options)\n\n for key in data:\n setattr(profiler, key, data[key])\n\n # Restore all data labelers\n profiler._restore_data_labelers()\n return profiler","def list_profiles(self):\n return self._get(\"posture\", box=BoxList)","def profiles(self, profiles):\n\n self._profiles = profiles","def call(self, failed=[], deletion=False):\n\n if deletion == False:\n # NOTE we won't it to run only after the delete has completed\n return\n\n log.debug(\"Reading profiles from progran\")\n onos = ProgranHelpers.get_progran_onos_info(self.model_accessor)\n profile_url = \"http://%s:%s/onos/progran/profile/\" % (onos['url'], onos['port'])\n r = requests.get(profile_url, auth=HTTPBasicAuth(onos['username'], onos['password']))\n res = r.json()['ProfileArray']\n\n\n # remove default profiles\n res = [p for p in res if \"Default\" not in p['Name']]\n pnames = [p['Name'] for p in res]\n log.debug(\"Received Profiles: \", profiles=pnames)\n\n field_mapping = {\n 'Name': 'name',\n 'Start': 'start',\n 'End': 'end',\n }\n\n field_transformations = {\n 'Start': ProgranHelpers.date_to_time,\n 'End': ProgranHelpers.date_to_time\n }\n\n handover_mapping = {\n 'A5Hysteresis': 'HysteresisA5',\n 'A3Hysteresis': 'HysteresisA3'\n }\n\n updated_profiles = []\n\n for p in res:\n\n\n # checking for profiles\n try:\n si = ProgranServiceInstance.objects.get(name=p['Name'])\n log.debug(\"Profile %s already exists, updating it\" % p['Name'])\n\n except IndexError:\n si = ProgranServiceInstance()\n\n si.created_by = \"Progran\"\n\n log.debug(\"Profile %s is new, creating it\" % p['Name'])\n\n if not si.is_new:\n # update IMSI association\n xos_imsis_for_profile = [i.subscriber_service_instance.leaf_model for i in si.provided_links.all()]\n progran_imsis_for_profile = p['IMSIRuleArray']\n\n log.debug(\"List of imsis for profile %s in XOS\" % p[\"Name\"], imsis=xos_imsis_for_profile)\n log.debug(\"List of imsis for profile %s in ONOS\" % p[\"Name\"], imsis=progran_imsis_for_profile)\n\n for i in xos_imsis_for_profile:\n if not i.imsi_number in progran_imsis_for_profile:\n log.debug(\"Removing Imsi %s from profile %s\" % (i.imsi_number, p['Name']))\n\n imsi_link = ServiceInstanceLink.objects.get(subscriber_service_instance_id=i.id)\n\n # NOTE: this model has already been removed from the backend, no need to synchronize\n imsi_link.backend_need_delete = False\n imsi_link.no_sync = True\n imsi_link.save() # we need to save it to avoid a synchronization loop\n\n imsi_link.delete()\n else:\n # remove from imsi list coming from progran everything we already know about\n progran_imsis_for_profile.remove(i.imsi_number)\n\n for i in progran_imsis_for_profile:\n log.debug(\"Adding Imsi %s to profile %s\" % (i, p['Name']))\n imsi = MCordSubscriberInstance.objects.get(imsi_number=i)\n imsi_to_profile = ServiceInstanceLink(provider_service_instance=si,\n subscriber_service_instance=imsi)\n imsi_to_profile.save()\n\n # if the model has not been synchronized yet, skip it\n if not si.is_new and si.no_sync is False:\n log.debug(\"Skipping profile %s as not synchronized\" % p['Name'])\n # NOTE add it to the removed profiles to avoid deletion (this is ugly, I know)\n updated_profiles.append(si.name)\n continue\n\n # ugly fix\n if 'AdmControl' in p.keys():\n p['AdmControl'] = str(p['AdmControl'])\n\n si = ProgranHelpers.update_fields(si, p, field_mapping, field_transformations)\n\n # checking for handovers\n handover_dict = p['Handover']\n handover_dict = ProgranHelpers.convert_keys(handover_dict, handover_mapping)\n del p['Handover']\n\n if si.handover_id:\n handover = si.handover\n log.debug(\"handover already exists, updating it\", handover=handover_dict)\n else:\n handover = Handover()\n handover = ProgranHelpers.update_fields(handover, handover_dict)\n log.debug(\"handover is new, creating it\", handover=handover_dict)\n handover.created_by = \"Progran\"\n\n handover = ProgranHelpers.update_fields(handover, handover_dict)\n handover.save()\n\n # Assigning handover to profile\n si.handover = handover\n\n # si.backend_status = \"OK\"\n # si.backend_code = 1\n\n si.no_sync = True\n si.previously_sync = True\n\n if p[\"MMECfg\"]:\n si.mmeip = str(p[\"MMECfg\"][\"IPAddr\"])\n si.mmeport = str(p[\"MMECfg\"][\"Port\"])\n\n si.enacted = time.mktime(datetime.datetime.now().timetuple())\n\n si.save()\n\n updated_profiles.append(si.name)\n\n existing_profiles = [p.name for p in ProgranServiceInstance.objects.all() if not p.is_new]\n deleted_profiles = ProgranHelpers.list_diff(existing_profiles, updated_profiles)\n\n if len(deleted_profiles) > 0:\n for p in deleted_profiles:\n si = ProgranServiceInstance.objects.get(name=p)\n if si.created_by == 'XOS' and si.previously_sync == False:\n # don't delete if the profile has been created by XOS and it hasn't been sync'ed yet\n continue\n # TODO delete also the associated Handover\n log.debug(\"Profiles %s have been removed in progran, removing it from XOS\" % str(p))\n si.delete()","def find_ready_profiles(project, script):\n profiles_ready = []\n for profile in Profile.objects.filter(script=script):\n errors = check_process_ready(project, profile)\n if len(errors) == 0:\n profiles_ready.append(profile)\n return profiles_ready","def get(self, name='default'):\r\n if name not in self._cache:\r\n full_path = os.path.join(self._base_folder, name)\r\n self._create_if_needed(full_path)\r\n self._cache[name] = Profile(full_path, name)\r\n return self._cache[name]","def load_profile(dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"rb\") as f:\n logger = pickle.load(f)\n return logger","def loadPerson(fileid):\n dinf = {}\n drel = {}\n root = etree.Element(\"person\")\n text = None\n # TODO: put this in a global variable, and make a function to populate it from the DTD.\n tags = [\"commonname\", \"ctitle\", \"gname\", \"mname\", \"fname\", \"nname\", \"nameorder\", \"gender\", \"bday\", \"dday\", \"stories\", \"mention\", \"appear1ch\", \"appear1wr\", \"conflict\", \"leadrel\", \"bodytyp\", \"age\", \"skin\", \"eyes\", \"hair\", \"dmarks\", \"dress\", \"attposs\", \"asmell\", \"personality\", \"speech\", \"formocc\", \"currocc\", \"strength\", \"weak\", \"mole\", \"hobby\", \"misc\", \"ethnic\", \"origin\", \"backstory\", \"residence\", \"minchar\", \"talent\", \"abil\", \"sgoal\", \"other\", \"relat\", \"aspects\", \"update\"]\n tags.remove(\"currocc\")\n tags.remove(\"formocc\")\n tags.remove(\"relat\")\n tags.append(\"file\")\n for tag in tags:\n dinf[tag] = [\"\",False]\n dinf['currocc'] = {}\n dinf['currocc']['pos'] = [\"\",False]\n dinf['formocc'] = {}\n dinf['formocc']['pos'] = [\"\",False]\n dinf['aspects'] = {}\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n dinf['formocc']['events'] = events\n if not idExists(fileid):\n status.push(0,\"new person created... '%s'\" % fileid)\n return (dinf,drel)\n fn = os.path.join(config['realmdir'],fileid + \".xml\")\n status.push(0,\"loading person from XML... '%s'\" % fn)\n try:\n with codecs.open(fn,'rU','utf-8') as f:\n tree = etree.parse(f)\n f.close()\n root = tree.getroot()\n except IOError as e:\n print \" Could not open configuration file: %s\" % e\n\n ir = 0\n for i in range(len(root)):\n if root[i].tag is not None:\n if root[i].tag == \"relat\":\n if len(root[i]) > 0:\n node = \"\"\n node = root[i].find(\"file\")\n if node is not None and node.text:\n node = node.text.strip()\n node = common.validateFileid(node)\n drel[node] = {}\n for j in root[i]:\n if j.tag == \"events\":\n if not drel[node].get('events'): drel[node]['events'] = {}\n for k in j:\n stone = str(len(drel[node]['events']))\n drel[node]['events'][stone] = {}\n for m in k:\n if m.tag and m.text:\n drel[node]['events'][stone][m.tag] = [m.text.strip(),False]\n else: # elif j.tag != \"file\":\n if j.tag and j.text:\n drel[node][j.tag] = [j.text.strip(),False]\n if config['debug'] > 3: print drel[node]\n else:\n if config['debug'] > 0:\n print \"Invalid relat tag:\"\n for c in root[i]:\n print c.tag\n else: # no relat length\n if config['debug'] > 0: print \"Empty relat tag.\"\n elif root[i].tag == \"currocc\":\n# print \",\",\n dinf['currocc'] = {}\n try:\n dinf['currocc']['pos'] = [root[i].find(\"pos\").text.strip(),False]\n except AttributeError:\n del dinf['currocc']\n if dinf.get('currocc'):\n events = {}\n if len(root[i]) > 1:\n for j in root[i]:\n if j.tag is not None:\n if j.tag == \"events\":\n for k in j:\n if k.tag == \"mstone\":\n le = str(len(events))\n events[le] = {}\n events[le]['date'] = [\"\",False]\n events[le]['event'] = [\"\",False]\n for m in k:\n if m.tag and m.text:\n events[le][m.tag] = [m.text.strip(),False]\n else:\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n else:\n dinf['currocc'] = {}\n dinf['currocc']['pos'] = [\"\",False]\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n elif root[i].tag == \"formocc\":\n# print \",\",\n dinf['formocc'] = {}\n try:\n dinf['formocc']['pos'] = [root[i].find(\"pos\").text.strip(),False]\n except AttributeError:\n del dinf['formocc']\n if dinf.get('formocc'):\n events = {}\n if len(root[i]) > 1:\n for j in root[i]:\n if j.tag is not None:\n if j.tag == \"events\":\n for k in j:\n if k.tag == \"mstone\":\n le = str(len(events))\n events[le] = {}\n events[le]['date'] = [\"\",False]\n events[le]['event'] = [\"\",False]\n for m in k:\n if m.tag and m.text:\n events[le][m.tag] = [m.text.strip(),False]\n else:\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['formocc']['events'] = events\n else:\n dinf['formocc'] = {}\n dinf['formocc']['pos'] = [\"\",False]\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['formocc']['events'] = events\n elif root[i].tag == \"aspects\":\n if len(root[i]) > 0:\n nodes = root[i]\n for node in nodes:\n k = str(len(dinf['aspects']))\n dinf['aspects'][k] = {}\n if node.tag and node.text:\n dinf['aspects'][k] = [node.text.strip(),False]\n else:\n if config['debug'] > 0:\n print \"Invalid aspects tag:\"\n print node.tag + ': ' + node.text,\n else: # no aspects length\n if config['debug'] > 0: print \"Empty aspects tag.\"\n elif root[i].text is not None:\n# print \".\",\n dinf[root[i].tag] = [root[i].text.strip(), False]\n if config['debug'] > 2: print str(i) + \" \",\n# print str(dinf)\n return (dinf,drel)","def load(cls, filepath):\n return BaseProfiler.load(filepath)","def load(self, config, profile_key):\n # a none numeric argument is supposed to be 'alias'\n if re.search(r\"\\b[0-9]+\\b\", profile_key) is None:\n key = profile_key\n self.alias = key\n access_by = \"alias\"\n else:\n key = (int)(profile_key)\n self.id = key\n access_by = \"id\"\n self.logger.debug(\"==> profile will be access by his '%s'\" % access_by)\n\n # check if the profile exists into config...\n i = 0\n profile_found = False\n while (i < len(config[\"profiles\"])\n and profile_found is False):\n\n if config[\"profiles\"][i][access_by] == key:\n profile_found = True\n self.logger.debug(\"==> profile '%s' found (access by '%s')\" %\n (key, access_by))\n self.config = config[\"profiles\"][i]\n\n i += 1\n\n # ... and if not raise an exception\n if profile_found is False:\n raise ProfileLoadError(\"profile '%s' not found\" % profile_key)\n\n # set profile properties\n self.id = self.config[\"id\"]\n self.alias = self.config[\"alias\"]\n self.state_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".json\")\n self.lock_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".lock\")\n\n # set the todo queue limit based on configuration\n # - default is 1\n try:\n todo_limit_queue = self.config[\"todo_queue_limit\"]\n self.todo_queue_limit = todo_limit_queue\n except KeyError:\n self.todo_queue_limit = 1","def globalProfile():\n return context.profiles","def list_network_profiles(self, **params):\r\n return self.get(self.network_profiles_path, params=params)","def read_plist(path: str) -> dict:\n return _read_plist(path, plistlib.FMT_XML)","def _load_data(self, filename):\n if not os.path.isfile(filename):\n return False\n\n with open(filename) as f:\n data = pickle.load(f)\n if data:\n self.profiles = data['profiles']\n self.user_data = data['user_data']\n self.api_data = data['api_data']\n else:\n return False","def getProfiles(context):\n\n analytics_tool = getToolByName(getSite(), 'portal_analytics')\n # short circuit if user hasn't authorized yet\n if not analytics_tool.is_auth():\n return SimpleVocabulary([])\n\n try:\n profiles = analytics_tool.makeCachedRequest('profiles')\n except error.BadAuthenticationError:\n choices = [('Please authorize with Google in the Google Analytics \\\n control panel.', None)]\n return SimpleVocabulary.fromItems(choices)\n except error.RequestTimedOutError:\n choices = [('The request to Google Analytics timed out. Please try \\\n again later.', None)]\n return SimpleVocabulary.fromItems(choices)\n if profiles:\n unique_choices = {}\n for entry in profiles:\n title = entry.get('name')\n title = crop(title, 40)\n tableId = entry.get('id')\n unique_choices.update({title: tableId})\n choices = unique_choices.items()\n else:\n choices = [('No profiles available', None)]\n return SimpleVocabulary([SimpleTerm(c[1], c[1], c[0]) for c in choices])","def profiles():\n images = get_uploaded_images()\n records = db.session.query(UserProfile).all()\n return render_template('profiles.html', images=images, records =records)","def handle_profile_changing(profiles: [ProfileSettingParser], project, script):\n files_altered = list()\n for profile in profiles:\n try:\n profile_obj = Profile.objects.get(\n pk=profile.profile_id, script=script\n )\n for input_template in profile.input_templates:\n try:\n input_template_obj = InputTemplate.objects.get(\n pk=input_template.input_template_id,\n corresponding_profile=profile_obj,\n )\n for file in input_template.files:\n try:\n file_obj = File.objects.get(\n project=project, pk=file\n )\n file_setting, _ = FileSetting.objects.get_or_create(\n input_template=input_template_obj, file=file_obj\n )\n files_altered.append(file_setting)\n except File.DoesNotExist:\n pass\n except InputTemplate.DoesNotExist:\n pass\n except Profile.DoesNotExist:\n pass\n return files_altered","def from_dir(cls, dir_path, ngram_sizes, profile_len):\n file_paths = []\n for dir_path, unused, file_names in os.walk(dir_path):\n for file_name in file_names:\n file_path = os.path.join(dir_path, file_name)\n file_paths.append(file_path)\n profile = cls.from_files(file_paths, ngram_sizes, profile_len)\n return profile","def read_pfile(pfile_loc):\n with open(pfile_loc, mode='r') as pfile:\n lines = pfile.readlines()\n\n profiles = {}\n nprofs = 0 # counter for total number of profiles so far\n linestart = 0 # counter for which line to start at for each profile\n nlines_tot = len(lines)\n\n while True:\n # Read the header line for each profile first\n lin1 = lines[linestart].split()\n npts_prof = int(lin1[0])\n\n xname = lin1[1]\n yname = lin1[2]\n dyname = ''.join(lin1[3:])[:-1]\n\n # Generate and populate the profile arrays\n x = np.zeros(npts_prof)\n y = np.zeros(npts_prof)\n dy = np.zeros(npts_prof)\n for i in range(npts_prof):\n split_line = lines[linestart + i + 1].split()\n x[i] = float(split_line[0])\n y[i] = float(split_line[1])\n dy[i] = float(split_line[2][:-1])\n\n # profiles[xname + '_' + yname] = x # psinorm\n profiles[xname] = x\n profiles[yname] = y\n profiles[dyname] = dy\n\n nprofs += 1\n linestart += 1 + npts_prof\n\n if linestart >= nlines_tot:\n break\n\n # Check if all psinorms are the same, consolidate if so (they are, don't bother separating)\n\n # condense = True\n # psinorm = None\n # for k in profiles.keys():\n # if k is None or k=='':\n # continue\n #\n # if k[:4] == 'psin':\n # if psinorm is None:\n # psinorm = profiles[k]\n #\n # if max(abs(profiles[k] - psinorm)) > 1e-5:\n # condense = False\n # break\n\n # if condense:\n # profiles = {key: value for key, value in profiles.items()\n # if key[:4] != 'psin' or key is None or key==''}\n # profiles['psinorm'] = psinorm\n\n return profiles","def list_profiles(request, pk=0):\n context = {'items': [], 'resource_type': 'Profile'}\n handle_expired_profiles()\n if pk == 0:\n context['h2'] = \"Configuration Profiles\"\n context['header_1'] = \"Type\"\n context['header_2'] = \"Last Modified\"\n profiles = ConfigurationProfile.objects.all().reverse()\n for profile in profiles:\n assignment_count = profile.pending_install.count()\n install_count = profile.installed.count()\n data = {'filename': str(profile), 'type': \"macOS\", 'meta': profile, 'assignment_count': assignment_count,\n 'install_count': install_count}\n context['items'].append(data)\n else:\n device = get_object_or_404(Laptop, pk=pk)\n context['h2'] = \"Profiles for {}\".format(device.name)\n context['header_1'] = \"Version\"\n context['header_2'] = \"Expires\"\n context['device_view'] = True\n context['device_id'] = pk\n profiles = ConfigurationProfile.objects.filter(pending_install__in=[device])\n profiles |= ConfigurationProfile.objects.filter(installed__in=[device])\n for profile in profiles:\n status = 'Not assigned'\n for entry in profile.installed.all():\n if entry == device:\n status = 'Installed'\n for entry in profile.pending_install.all():\n if entry == device:\n status = 'Assigned'\n record = InstallationRecord.objects.filter(profile=profile, device=device, active=True).first()\n expires_soon = False\n if record is not None and record.expires is not None:\n if timezone.now() < record.expires < timezone.now() + timezone.timedelta(days=30):\n expires_soon = True\n data = {'filename': str(profile), 'downloadable': False, 'install_record': record, 'meta': profile,\n 'status': status, 'expires_soon': expires_soon}\n context['items'].append(data)\n\n return render(request, 'mdm/resource_list.html', context)","def init_profiles_using_namedtuple(no_profiles: int):\n profiles = []\n Profile = namedtuple('Profile', fake.profile().keys())\n for _ in range(no_profiles):\n profiles.append(Profile(**fake.profile()))\n return profiles","def find_prefs():\n conf = SafeConfigParser()\n try:\n conf.read(PROFILES)\n except Exception as err:\n log.error('reading profiles.ini: %s', err)\n return None\n\n for section in conf.sections():\n if conf.has_option(section, 'Name') and \\\n conf.get(section, 'Name') == 'default':\n path = conf.get(section, 'Path')\n if conf.getboolean(section, 'IsRelative'):\n path = os.path.join(CONFDIR, path)\n\n return unicodify(os.path.join(path, 'prefs.js'))\n\n return None","def show_all_profiles(profiles, attribute=None, image=None, height=None, fname=None, **kwargs):\n\n # Filter profiles according to attribute if attribute is set\n if attribute:\n profiles = filter(lambda x: x.description['attribute'] == attribute, profiles)\n # Same for image\n if image:\n profiles = filter(lambda x: x.description['tree']['image_name'] == image, profiles)\n\n for p in profiles:\n show_profiles(p, height, fname, **kwargs)","def get_peak_stats(file_path):\n peak_stats = dict(numberOfPeaks=0,\n peakMean=0,\n peakMeanStd=0,\n peakMedian=0,\n peakMedianStd=0,\n medianTagSingletons=0,\n singletons=0)\n stddevs = []\n peak_singleton_scores = []\n scores = []\n singletons = 0\n i = 0\n with open(file_path) as fh:\n for i, line in enumerate(fh):\n items = line.split('\\t')\n # Gff column 6 is score.\n score = float(items[5])\n scores.append(score)\n # Gff column 9 is a semicolon-separated list.\n attributes = items[8].split(';')\n for attribute in attributes:\n if attribute.startswith('stddev'):\n val = float(attribute.split('=')[1])\n stddevs.append(val)\n if val == 0.0:\n # We have a peakSingleton.\n singletons += 1\n peak_singleton_scores.append(score)\n break\n fh.close()\n if i > 0:\n # The number of lines in the file is the number of peaks.\n peak_stats['numberOfPeaks'] = i + 1\n peak_stats['peakMean'] = numpy.mean(scores)\n peak_stats['peakMeanStd'] = numpy.mean(stddevs)\n peak_stats['peakMedian'] = numpy.median(scores)\n peak_stats['peakMedianStd'] = numpy.median(stddevs)\n peak_stats['medianTagSingletons'] = numpy.median(peak_singleton_scores)\n peak_stats['singletons'] = singletons\n return peak_stats","def get_profile_info(self):\n\n drill_path = str(Path.home())+\"/Documents/ball_e_profiles/drill_profiles/{drill_name}/{drill_name}.csv\".format(\n drill_name=self.drill_name)\n with open(drill_path) as file:\n csv_reader = csv.reader(file, delimiter=',')\n row_count = 0\n info_dict = dict()\n for row in csv_reader:\n if row_count == 0:\n row_count += 1\n else:\n info_dict[row[0]] = [row[1], row[2], row[3]]\n row_count += 1\n\n return info_dict","def read_config(profile):\n p = Path(CONFIG_FILE_PATH).expanduser()\n p.parent.mkdir(parents=True, exist_ok=True)\n config = configparser.ConfigParser()\n config.read(str(p))\n\n if config.has_section(profile):\n return dict(config.items(profile))\n else:\n return {}","def get_profiles(species_id, probes, limit=1000):\n profiles = (\n ExpressionProfile.query.options(undefer(\"profile\"))\n .filter(ExpressionProfile.probe.in_(probes))\n .filter_by(species_id=species_id)\n .options(joinedload(\"sequence\").load_only(\"name\").noload(\"xrefs\"))\n .limit(limit)\n .all()\n )\n\n return profiles","def read_pro(path,var_to_plot= None):\n\n # Which variables are you interested in?\n\n if var_to_plot:\n var_codes = ['0500','0501',pro_code_dict(var_to_plot,inverse=True)]\n else:\n var_codes = ['0500', '0501', '0502', '0503', '0506', '0508',\n '0509', '0511', '0512', '0513', '0515',\n '0516', '0521', '0535', '0540', '0541']\n\n # Set up the dictionary to be returned. Dictionary is organised by variable name.\n\n code_dict = pro_code_dict(return_all=True)\n\n variables = {}\n for var in var_codes:\n variables[code_dict[var]] = []\n\n # Open the .PRO file\n\n with open(path, \"r\") as f:\n\n # Iterate line by line\n\n for line in f.readlines():\n\n # If the variable code (first four chars) matches the variable of interest,\n # append that line to the list of lines\n\n if line[:4] in var_codes:\n variables[code_dict[line[:4]]].append(line)\n\n\n # Now remove the header data\n\n for variable in variables.keys():\n\n variables[variable].pop(0)\n\n snowpro_list = [snowpro_from_snapshot(date_index, variables) for date_index in range(len(variables['Date']))]\n\n return (snowpro_list)","def updateUsersProfilesIfNewer_(self, plist):\r\n \r\n # Load UpdateProfiles from the user's application support directory.\r\n userUpdateProfiles = NSDictionary.dictionaryWithContentsOfFile_(self.userUpdateProfilesPath)\r\n \r\n # If the bundle's plist is newer, update the user's.\r\n if (not userUpdateProfiles) or (userUpdateProfiles[u\"PublicationDate\"].timeIntervalSinceDate_(plist[u\"PublicationDate\"]) < 0):\r\n LogDebug(u\"Saving updated UpdateProfiles.plist\")\r\n self.saveUsersProfiles_(plist)\r\n return plist\r\n else:\r\n return userUpdateProfiles","def read_data(file):\n with rasterio.open(file) as f:\n data = f.read(1)\n profile = f.profile\n return data, profile","def get_all_profiles(store=\"local\"):\n return {\n \"Domain Profile\": get_all_settings(profile=\"domain\", store=store),\n \"Private Profile\": get_all_settings(profile=\"private\", store=store),\n \"Public Profile\": get_all_settings(profile=\"public\", store=store),\n }","def read_profile (filename, index):\n \n col_names = ['m', 'r', 'F', 'P', 'rho', 'T', 'u', 's', \n 'c_P', 'gamma_ad', 'nabla_ad', 'mu', 'n_e', \n 'P_e', 'P_rad', 'nabla_rad', 'nabla', 'v_c',\n 'kappa', 'q_nuc', 'q_PP', \n 'q_CNO', 'q_3a', 'q_nunuc',\n 'q_nu', 'q_grav', 'X', 'X_mol',\n 'X_+', 'Y', 'Y_+', 'Y_++', 'X_C', 'X_N', \n 'X_O', 'Psi']\n\n return read_ez_zip(filename, 'structure_{:05d}.txt'.format(index), col_names)","def show_profiles(profiles, height=None, fname=None, **kwargs):\n _show_profiles(profiles, height, fname, **kwargs)","def get_data(self, n=-1): \n try:\n return Profile(os.path.join(self.name, self.files[n]))\n except IndexError:\n return None","def merge_structural_profile(profile_path, merged_path):\n def list_to_str(lst):\n ''' Given a list, return the string of that list with tab separators\n '''\n return reduce( (lambda s, f: s + '\\t' + str(f)), lst, '')\n\n # external loop profile\n E_path = profile_path+'E_profile.txt'\n fEprofile = open(E_path)\n Eprofiles = fEprofile.readlines()\n\n # hairpin loop profiles\n H_path = profile_path+'H_profile.txt'\n fHprofile = open(H_path)\n Hprofiles = fHprofile.readlines()\n\n # internal loop profiles\n I_path = profile_path+'I_profile.txt'\n fIprofile = open(I_path)\n Iprofiles = fIprofile.readlines()\n\n # multi-loop profiles\n M_path = profile_path+ 'M_profile.txt'\n fMprofile = open(M_path)\n Mprofiles = fMprofile.readlines()\n\n num_seq = int(len(Eprofiles)/2)\n\n # parse into a single file\n fhout = open(merged_path, 'w')\n for i in range(num_seq):\n id = Eprofiles[i*2].split()[0]\n fhout.write(id+'\\n')\n H_prob = Hprofiles[i*2+1].split()\n I_prob = Iprofiles[i*2+1].split()\n M_prob = Mprofiles[i*2+1].split()\n E_prob = Eprofiles[i*2+1].split()\n P_prob = map( (lambda a, b, c, d: 1-float(a)-float(b)-float(c)-float(d)), H_prob, I_prob, M_prob, E_prob)\n fhout.write(list_to_str(P_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(H_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(I_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(M_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(E_prob[:len(P_prob)])+'\\n')\n fhout.close()\n\n return num_seq","def load_training_data(face_profile_directory):\n # delete_empty_profile(face_profile_directory) # delete profile directory without images\n\n # Get a the list of folder names in face_profile as the profile names\n face_profile_names = [d for d in os.listdir(face_profile_directory) if \".\" not in str(d)]\n\n if len(face_profile_names) < 2: \n logging.error(\"\\nFace profile contains too little profiles (At least 2 profiles are needed)\")\n exit()\n # print('cc')\n # print(face_profile_names)\n first_data = str(face_profile_names[0])\n first_data_path = os.path.join(face_profile_directory, first_data)\n read_images_from_single_face_profile(first_data_path, 0)\n \n \n for i in range(1, len(face_profile_names)):\n directory_name = str(face_profile_names[i])\n directory_path = os.path.join(face_profile_directory, directory_name)\n read_images_from_single_face_profile(directory_path, i)","def load_profile(self):\n self.dut_testpmd.start_testpmd(\n \"Default\", \"--pkt-filter-mode=perfect --port-topology=chained \\\n --txq=%s --rxq=%s\"\n % (self.PF_QUEUE, self.PF_QUEUE))\n self.vm0_testpmd.start_testpmd(\n VM_CORES_MASK, \"--port-topology=chained --txq=%s --rxq=%s\"\n % (self.VF_QUEUE, self.VF_QUEUE))\n self.dut_testpmd.execute_cmd('port stop all')\n time.sleep(1)\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\n self.dut_testpmd.execute_cmd('ddp add 0 /tmp/gtp.pkgo,/tmp/gtp.bak')\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\n self.verify(\"Profile number is: 1\" in out,\n \"Failed to load ddp profile!!!\")\n self.dut_testpmd.execute_cmd('port start all')\n time.sleep(1)\n self.dut_testpmd.execute_cmd('set fwd rxonly')\n self.dut_testpmd.execute_cmd('set verbose 1')\n self.dut_testpmd.execute_cmd('start')\n self.vm0_testpmd.execute_cmd('set fwd rxonly')\n self.vm0_testpmd.execute_cmd('set verbose 1')\n self.vm0_testpmd.execute_cmd('start')","def load_users(filename):\n with open(filename, 'rb') as f:\n for line in f:\n yield line.split(':', 1)[0]","def recover_from_xml(path):\n\tlistz = extract_list_from_xml(path)\n\trecover_old_filename(listz)","def _parse_profile_data (self, netflix_page_data):\n profiles = {};\n important_fields = [\n 'profileName',\n 'isActive',\n 'isAccountOwner',\n 'isKids'\n ]\n # values are accessible via dict (sloppy parsing successfull)\n if type(netflix_page_data) == dict:\n for profile_id in netflix_page_data.get('profiles'):\n if self._is_size_key(key=profile_id) == False and type(netflix_page_data['profiles'][profile_id]) == dict and netflix_page_data['profiles'][profile_id].get('avatar', False) != False:\n profile = {'id': profile_id}\n for important_field in important_fields:\n if important_field == 'profileName':\n profile.update({important_field: HTMLParser.HTMLParser().unescape(netflix_page_data['profiles'][profile_id]['summary'][important_field]).encode('utf8')})\n else:\n profile.update({important_field: netflix_page_data['profiles'][profile_id]['summary'][important_field]})\n avatar_base = netflix_page_data['nf'].get(netflix_page_data['profiles'][profile_id]['summary']['avatarName'], False);\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\n profile.update({'avatar': avatar, 'isFirstUse': False})\n profiles.update({profile_id: profile})\n return profiles\n\n # values are stored in lists (returned from JS parser)\n # TODO: get rid of this christmas tree of doom\n for item in netflix_page_data:\n if 'hasViewedRatingWelcomeModal' in dict(item).keys():\n for profile_id in item:\n if self._is_size_key(key=profile_id) == False and type(item[profile_id]) == dict and item[profile_id].get('avatar', False) != False:\n profile = {'id': profile_id}\n for important_field in important_fields:\n profile.update({important_field: item[profile_id]['summary'][important_field]})\n avatar_base = item['nf'].get(item[profile_id]['summary']['avatarName'], False);\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\n profile.update({'avatar': avatar})\n profiles.update({profile_id: profile})\n return profiles","def initialize():\n pinfo = pathinfo()\n\n if not pinfo:\n raise Error('cannot decide profile directory, $SUZUPROFILEDIR, $APPDATA, or $HOME should be configured.')\n\n # dig profdir\n if not os.path.isdir(pinfo['profiledir']):\n os.mkdir(pinfo['profiledir'])\n\n # test dir\n if not os.path.isdir(pinfo['profiledir']):\n raise Error('profile directory {} does not exist, nor cannot create'.format(pinfo['profiledir']))\n\n # test profile data file\n if not os.path.isfile(pinfo['config']):\n with open(pinfo['config'], 'w') as stream:\n json.dump(config.default, stream, indent=2, sort_keys=True)\n\n # test file (do not care on the contents)\n if not os.path.isfile(pinfo['config']):\n raise Error('config file {} does not exist, nor cannot create'.format(pinfo['config']))\n\n return pinfo","def generate_files(input_path, output_path):\n profiles = pd.read_csv(f'{input_path}/Profile/Profile.csv')\n\n # call all predictors\n predicted = {f: pr.all_predictors[f].predict(profiles=profiles, base_folder=input_path)\n for f in pr.features if f in pr.all_predictors}\n\n for i, (index, row) in enumerate(profiles.iterrows()):\n file = open(os.path.join(output_path, f'{row[\"userid\"]}.xml'), 'w')\n gender = pr.get_gender(predicted['gender'][row[1]]) if row[1] in predicted['gender'] else predicted['age_group_alternative'][row[1]]\n open_ = predicted['open'][row[1]] if row[1] in predicted['open'] else 'null'\n conscientious = predicted['conscientious'][row[1]] if row[1] in predicted['conscientious'] else 'null'\n extrovert = predicted['extrovert'][row[1]] if row[1] in predicted['extrovert'] else 'null'\n agreeable = predicted['agreeable'][row[1]] if row[1] in predicted['agreeable'] else 'null'\n neurotic = predicted['neurotic'][row[1]] if row[1] in predicted['neurotic'] else 'null'\n age = predicted['age_group'][row[1]] if row[1] in predicted['age_group'] else 'null'\n\n file.write(f'<user id=\"{row[1]}\" ')\n file.write(f'age_group=\"{age}\" ')\n file.write(f'gender=\"{gender}\" ')\n file.write(f'extrovert=\"{extrovert}\" ')\n file.write(f'neurotic=\"{neurotic}\" ')\n file.write(f'agreeable=\"{agreeable}\" ')\n file.write(f'open=\"{open_}\" ')\n file.write(f'conscientious=\"{conscientious}\" />')\n file.close()","def read_file(filepath: str) -> dict:\n if not filepath.endswith(\".csv\"):\n raise RuntimeError(\"File extension must be .csv\")\n\n people = {}\n with open(filepath) as csv:\n for line in csv:\n email, person = Parser.parse_line(line.rstrip(\"\\n\"))\n if email not in people:\n people[email] = person\n else:\n print(\"Ignoring person with duplicate email {}\".format(email))\n return people","def get_server_profile_attributes(name, attribute=None):\n general_xpaths = {\n \"name\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\n \"description\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\n \"server hardware\": FusionServerProfilesPage.ID_SERVER_HARDWARE_NAME,\n \"associated server\": FusionServerProfilesPage.ID_ASSOCIATED_SERVER,\n \"server hardware type\": FusionServerProfilesPage.ID_PROFILE_HARDWARE,\n \"enclosure group\": FusionServerProfilesPage.ID_SERVER_ENC_GROUP,\n \"affinity\": FusionServerProfilesPage.ID_SERVER_AFFINITY,\n \"server power\": FusionServerProfilesPage.ID_SERVER_POWER_STATUS,\n \"serial number\": FusionServerProfilesPage.ID_SERVER_SERIAL_NUMBER,\n \"uuid\": FusionServerProfilesPage.ID_SERVER_UUID,\n }\n\n # Navigate to Server Profile\n if not select_server_profile(name):\n logger._warn(\"Failed to find Server Profile '%s'\" % name)\n BuiltIn().sleep(4)\n # Get attribute data\n if attribute is not None:\n keys = general_xpaths.keys()\n if attribute.lower() not in keys:\n logger._warn(\"Invalid attribute '%s'.\" % attribute)\n return None\n\n # Get specified attribute\n xpath = general_xpaths[attribute.lower()]\n ui_lib.wait_for_element_visible(xpath, timeout=10)\n value = ui_lib.ignore_staleElementRefException(\"get_text\", xpath)\n\n return value\n else:\n # Get all attributes of Server Profile\n general_data = {}\n for attribute, xpath in general_xpaths.iteritems():\n value = ui_lib.ignore_staleElementRefException(\"get_text\", xpath)\n general_data[attribute] = value\n return general_data\n\n return None","def getProfileInfo(self):\n doc = minidom.parse(urllib.urlopen(serverString + \"/rest/user/\" + self.name))\n for element in doc.getElementsByTagName(\"user\")[0].childNodes:\n if element.nodeType != minidom.Node.ELEMENT_NODE:\n continue\n elif element.tagName == \"status\" and int(element.firstChild.data) != 1:\n raise ServerError(element.firstChild.data)\n elif element.tagName == \"input\":\n self.name = element.firstChild.data\n elif element.tagName == \"id\":\n self.id = element.firstChild.data\n elif element.tagName == \"image\":\n self.image = element.firstChild.data\n elif element.tagName == \"tagline\":\n if element.firstChild == None:\n self.tagline = None\n else:\n self.tagline = element.firstChild.data\n elif element.tagName == \"creation\":\n self.created = datetime.datetime.strptime(element.firstChild.data[:element.firstChild.data.rfind(\".\")]+\".GMT\", \"%Y-%m-%d %H:%M:%S.%Z\")","def getFirefoxProfile(self, profileDirectory=None):\n fp = webdriver.FirefoxProfile(profile_directory=profileDirectory)\n return fp","def _process_profiles(profiles):\n # keep_attributes = str.split(\"user_id public completion_percentage gender region last_login registration age\")\n # p2=profiles[keep_attributes]\n p2 = profiles\n p2['region'] = p2['region'].astype('category')\n p2['public'] = p2['public'].astype('category')\n p2['gender'] = p2['gender'].astype('category')\n p2['last_login'] = pd.to_datetime(p2['last_login'])\n p2['registration'] = pd.to_datetime(p2['registration'])\n p2.loc[p2.age == 0, 'age'] = np.nan\n\n return p2","def addon_profiles(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input['AddonProfilesArgs']]]]:\n return pulumi.get(self, \"addon_profiles\")"],"string":"[\n \"def load_profiles_from_file(file_path):\\r\\n profiles = {}\\r\\n\\r\\n lastused = \\\"\\\"\\r\\n\\r\\n if File.Exists(file_path):\\r\\n try:\\r\\n with StreamReader(file_path) as xmlfile:\\r\\n xmldoc = XmlDocument()\\r\\n xmldoc.Load(xmlfile)\\r\\n\\r\\n if xmldoc.DocumentElement.Name == \\\"Profiles\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profiles/Profile\\\")\\r\\n #Individual exported profiles are saved with the document element as Profile\\r\\n elif xmldoc.DocumentElement.Name == \\\"Profile\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profile\\\")\\r\\n\\r\\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\\r\\n elif xmldoc.DocumentElement.Name == \\\"Settings\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Settings/Setting\\\")\\r\\n elif xmldoc.DocumentElement.Name == \\\"Setting\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Setting\\\")\\r\\n\\r\\n #No valid root elements\\r\\n else:\\r\\n MessageBox.Show(file_path + \\\" is not a valid Library Organizer profile file.\\\", \\\"Not a valid profile file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n return profiles, lastused\\r\\n\\r\\n if nodes.Count > 0:\\r\\n for node in nodes: \\r\\n profile = Profile()\\r\\n profile.Name = node.Attributes[\\\"Name\\\"].Value\\r\\n result = profile.load_from_xml(node)\\r\\n\\r\\n #Error loading the profile\\r\\n if result == False:\\r\\n MessageBox.Show(\\\"An error occured loading the profile \\\" + profile.Name + \\\". That profile has been skipped.\\\")\\r\\n\\r\\n else:\\r\\n profiles[profile.Name] = profile\\r\\n\\r\\n\\r\\n #Load the last used profile\\r\\n rootnode = xmldoc.DocumentElement\\r\\n if rootnode.HasAttribute(\\\"LastUsed\\\"):\\r\\n lastused = rootnode.Attributes[\\\"LastUsed\\\"].Value.split(\\\",\\\")\\r\\n\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"Something seems to have gone wrong loading the xml file.\\\\n\\\\nThe error was:\\\\n\\\" + str(ex), \\\"Error loading file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n\\r\\n return profiles, lastused\",\n \"def import_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n return profiles\",\n \"def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\\n Dict[str, List[str]]]:\\n individuals = [[]]\\n sublist = 0\\n content = profiles_file.readlines()\\n for i in content:\\n if i != '\\\\n':\\n individuals[sublist].append(i)\\n else:\\n sublist += 1\\n individuals.append([])\\n return sort_profile(individuals)\",\n \"def loadProfiles():\\n with open(userProfilesDir, \\\"r\\\") as infile:\\n profiles = json.loads(\\\"\\\\n\\\".join(infile.readlines()))\\n infile.close()\\n return profiles\",\n \"def load(path):\\n\\n parser = ConfigParser()\\n parser.read(str(path))\\n\\n def _get(section, option):\\n try:\\n return parser.get(section, option)\\n except (NoSectionError, NoOptionError):\\n return None\\n\\n profiles = {}\\n\\n for section in parser.sections():\\n profiles[section] = Profile(\\n domain=_get(section, \\\"domain\\\"),\\n protocol=_get(section, \\\"protocol\\\"),\\n client_id=_get(section, \\\"client_id\\\"),\\n client_secret=_get(section, \\\"client_secret\\\"),\\n )\\n\\n return profiles\",\n \"def save_profiles(file_path, profiles, lastused=\\\"\\\"):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n writer.WriteStartElement(\\\"Profiles\\\")\\r\\n if lastused:\\r\\n writer.WriteAttributeString(\\\"LastUsed\\\", \\\",\\\".join(lastused))\\r\\n for profile in profiles:\\r\\n profiles[profile].save_to_xml(writer)\\r\\n writer.WriteEndElement()\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def get_profile(path=\\\"~\\\"):\\n global profiles\\n profile = profiles.get(path,None)\\n if not profile:\\n profile = InitFileConfig(os.path.join(path,\\\".myradioprofile\\\"), {} )\\n profiles[path] = profile\\n return profile\",\n \"def _read_profiles(profile_directory):\\n # Initialize key variables\\n profiles = defaultdict(\\n lambda: defaultdict(lambda: defaultdict()))\\n\\n # Read the yaml files in the profiles directory\\n files = os.listdir(profile_directory)\\n filenames = ['{}{}{}'.format(\\n profile_directory, os.sep, nextfile) for nextfile in files]\\n\\n for _filename in sorted(filenames):\\n # Get rid of excess os.sep separators\\n pathitems = _filename.split(os.sep)\\n filename = os.sep.join(pathitems)\\n\\n # Skip obvious\\n if os.path.isfile(filename) is False:\\n continue\\n if filename.lower().endswith('.yaml') is False:\\n continue\\n\\n with open(filename, 'r') as stream:\\n try:\\n _profiles = yaml.load(stream)['data']\\n except yaml.YAMLError as exc:\\n print(exc)\\n\\n # Create dictionary\\n for item in _profiles:\\n firstname = item['firstname']\\n lastname = item['lastname']\\n height = item['height']\\n weight = item['weight']\\n birthdate = item['birthdate']\\n profiles[lastname][firstname][birthdate] = {\\n 'height': height, 'weight': weight}\\n\\n return profiles\",\n \"def populate_profiles_from_directory(self):\\n\\n self.profiles = []\\n\\n # Go over all the files and create a profile object\\n _profile_files = listdir(self.save_dir)\\n\\n for profile_filename in _profile_files:\\n # Only check for .yaml files\\n if path.splitext(profile_filename)[1] == '.yaml':\\n # Get the data and create a new profile\\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\\n _profile = Profile(path.splitext(profile_filename)[0])\\n _profile.player_data = _file_data\\n self.profiles.append(_profile)\",\n \"def load_profile(path, profile):\\n profiles = load(path)\\n try:\\n return profiles[profile]\\n except KeyError:\\n return Profile(None, None, None, None)\",\n \"def _recurse_load_profile(self, text, profile_path):\\n try:\\n inherited_profile = Profile()\\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\\n profile_parser = _ProfileParser(text)\\n # Iterate the includes and call recursive to get the profile and variables\\n # from parent profiles\\n for include in profile_parser.includes:\\n # Recursion !!\\n profile = self._load_profile(include, cwd)\\n inherited_profile.compose_profile(profile)\\n\\n # Current profile before update with parents (but parent variables already applied)\\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\\n inherited_profile)\\n return inherited_profile\\n except ConanException:\\n raise\\n except Exception as exc:\\n raise ConanException(\\\"Error parsing the profile text file: %s\\\" % str(exc))\",\n \"def _sloppy_parse_profiles (self, contents):\\n profile_start = contents.find('profiles\\\":')\\n profile_list_start = contents.find('profilesList')\\n if int(profile_start) > -1 and int(profile_list_start) > -1:\\n try:\\n try:\\n return json.loads('{\\\"a\\\":{\\\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\\n except ValueError, e:\\n return None\\n except TypeError, e:\\n return None\\n return None\",\n \"def get_profiles(self):\\n # print(self.uir) #checkpoint\\n if os.path.isdir(self.uir+\\\"/profiles\\\"):\\n profiles=os.listdir(self.uir+\\\"/profiles\\\")\\n # print(profiles) #checkpoint\\n for profile in profiles:\\n wsadmin=self.uir+\\\"/profiles/\\\"+profile+\\\"/bin/wsadmin.bat\\\"\\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\\n self.profiles.append(self.uir+\\\"/profiles/\\\"+profile)\\n\\n else: print(self.uir+' Instance does not have \\\"profile\\\" folder in '+self.uir)\\n return\",\n \"def get_profiles(profile_file_directory):\\r\\n\\t\\r\\n\\tprofile_file_path = profile_file_directory+ \\\"/profiles.txt\\\"\\r\\n\\tlist_of_all_allele_numbers_tuple = []\\r\\n\\tdatabase = None\\r\\n\\tlocusList = []\\r\\n\\t\\r\\n\\tfor l in open(profile_file_path):\\r\\n\\t if database is None:\\r\\n\\t\\tdatabase = {}\\r\\n\\t\\tlocusList = l.split()[1:]\\r\\n\\t\\tcontinue\\r\\n\\t t = l.split()\\r\\n\\t st = t[0]\\r\\n\\t v = ' '.join([s for s in t[1:]])\\r\\n\\t if v in database:\\r\\n\\t\\tprint >> sys.stderr, 'sequence type ' + str(st) + ' is a duplicate of ' + str(database[v])\\r\\n\\t database[v] = st\\r\\n\\t covert_string_to_tuple_list_of_allele_numbers = tuple(int(x) for x in re.findall(\\\"[0-9]+\\\", v)) \\r\\n\\t list_of_all_allele_numbers_tuple.append(covert_string_to_tuple_list_of_allele_numbers)\\r\\n\\t\\t\\r\\n\\treturn (database, locusList, list_of_all_allele_numbers_tuple)\",\n \"def profile(filename: str) -> 'Iterator[None]':\\n profiler = Profile()\\n profiler.enable()\\n\\n yield\\n\\n profiler.disable()\\n profiler.create_stats()\\n profiler.dump_stats('profiles/{}'.format(filename))\",\n \"def list_profiles(self) -> dict:\\n wsc = self.read_ws_configuration()\\n out = OrderedDict()\\n for name, json in wsc.profiles.items():\\n out[name] = Profile(name, self.ws_data_folder / name, json)\\n # Try to find current profile\\n try:\\n out[self.current_profile_name].is_current = True\\n except Exception:\\n pass\\n return out\",\n \"def from_file(cls, file_path, ngram_sizes, profile_len):\\n profile = cls.from_files((file_path, ), ngram_sizes, profile_len)\\n return profile\",\n \"def profiles(self):\\n if not self._profiles:\\n self.GetAllProfiles()\\n return self._profiles\",\n \"def profile_files(profile):\\n flist = os.listdir(osp.join(profile, 'startup'))\\n profile_path = osp.join(osp.abspath('.'), profile)\\n return [osp.join(profile_path, 'startup', x) for x in flist]\",\n \"def GetAllProfiles(self):\\n profiles = []\\n feed_uri = self._gd_client.GetFeedUri('profiles')\\n while feed_uri:\\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\\n profiles.extend(feed.entry)\\n feed_uri = feed.FindNextLink()\\n self._profiles = profiles\",\n \"def load_people(self, file_path):\\n pass\",\n \"def read_profiles(filename):\\n profiles = []\\n with gzip.open(filename, mode='rt', encoding='utf8') as infile:\\n for line in infile:\\n profiles.append(Counter(line.split()))\\n return profiles\",\n \"def have_profile_dir(path, maxdepth=3, filename=\\\"profiles.desc\\\"):\\n\\twhile path != \\\"/\\\" and maxdepth:\\n\\t\\tif os.path.exists(os.path.join(path, \\\"profiles\\\", filename)):\\n\\t\\t\\treturn normalize_path(path)\\n\\t\\tpath = normalize_path(path + \\\"/..\\\")\\n\\t\\tmaxdepth -= 1\",\n \"def parse_file(file_path):\\n with open(file_path) as f:\\n return XmlPropertyListParser().parse(f)\",\n \"def profiles_path(self) -> Path:\\n return self._config.data_path / \\\"hmm\\\" / \\\"profiles.hmm\\\"\",\n \"def _load_profile(self, profile_name, cwd):\\n\\n profile_path = self.get_profile_path(profile_name, cwd)\\n try:\\n text = load_user_encoded(profile_path)\\n except Exception as e:\\n raise ConanException(f\\\"Cannot load profile:\\\\n{e}\\\")\\n\\n # All profiles will be now rendered with jinja2 as first pass\\n base_path = os.path.dirname(profile_path)\\n file_path = os.path.basename(profile_path)\\n context = {\\\"platform\\\": platform,\\n \\\"os\\\": os,\\n \\\"profile_dir\\\": base_path,\\n \\\"profile_name\\\": file_path,\\n \\\"conan_version\\\": conan_version}\\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\\n text = rtemplate.render(context)\\n\\n try:\\n return self._recurse_load_profile(text, profile_path)\\n except ConanException as exc:\\n raise ConanException(\\\"Error reading '%s' profile: %s\\\" % (profile_name, exc))\",\n \"def read_pardus_profiles(self):\\n\\n self.lan_config = ConfigParser.ConfigParser()\\n self.lan_config.read(self.lan_config_path)\\n connection_type = \\\"802-3-ethernet\\\"\\n for section in self.lan_config.sections():\\n lan_settings = {}\\n for option in self.lan_config.options(section):\\n if option == \\\"device\\\":\\n #To strip device name from long device string\\n lan_settings[option] = self.lan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n lan_settings[option] = self.lan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, lan_settings)\\n self.pardus_profiles.append(p)\\n\\n self.wlan_config = ConfigParser.ConfigParser()\\n self.wlan_config.read(self.wlan_config_path)\\n connection_type = \\\"802-11-wireless\\\"\\n for section in self.wlan_config.sections():\\n wlan_settings = {}\\n for option in self.wlan_config.options(section):\\n if option == \\\"device\\\":\\n wlan_settings[option] = self.wlan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n wlan_settings[option] = self.wlan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\\n self.pardus_profiles.append(p)\",\n \"def _find_cb_profiles():\\n dir_locations = [\\\".carbonblack\\\", os.path.join(os.path.expanduser(\\\"~\\\"), \\\".carbonblack\\\")]\\n cred_file = \\\"credentials.response\\\"\\n profiles = []\\n\\n for dir in dir_locations:\\n cred_file_path = os.path.join(dir, cred_file)\\n _MOD_LOGGER.debug(\\\"Searching CB profiles on '%s'\\\", cred_file_path)\\n if os.path.exists(cred_file_path):\\n _MOD_LOGGER.debug(\\\"File exists, parsing...\\\")\\n config = configparser.ConfigParser(default_section=\\\"cbbackend\\\", strict=True)\\n config.read(cred_file_path)\\n profiles += [sec_name for sec_name in config.keys() if sec_name != \\\"cbbackend\\\"]\\n\\n if profiles:\\n _MOD_LOGGER.debug(\\\"Requested to read 'all' profiles. Found: %s\\\", \\\",\\\".join(profiles))\\n\\n return profiles\",\n \"def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\\n assert isinstance(profiles, list), \\\"Please provide a list of profiles\\\"\\n cache = ClientCache(self._conan_api.cache_folder)\\n loader = ProfileLoader(cache)\\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\\n profile.conf.validate()\\n cache.new_config.validate()\\n # Apply the new_config to the profiles the global one, so recipes get it too\\n profile.conf.rebase_conf_definition(cache.new_config)\\n return profile\",\n \"def loadProfile(fname):\\n \\n x = np.loadtxt(fname)\\n return x[:,1]\",\n \"def parse_profiles(profiles):\\n if type(profiles) == list:\\n return [\\n x\\n for x in [ProfileSettingParser.parse(x) for x in profiles]\\n if x is not None\\n ]\\n else:\\n return []\",\n \"def get_profiles(self):\\n profiles = [['Profile name', 'GUID']]\\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\\n for row in r.fetchall():\\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\\n return profiles\",\n \"def get_profiles(args):\\n # Use profile from cli if provided\\n if args.profile and not args.update_config:\\n return [args.profile]\\n\\n # Run config to get or set the config file\\n config = configparser.ConfigParser()\\n\\n if os.path.isfile(SETTINGS_FILE) and not args.update_config:\\n # Get profiles from config\\n config.read(SETTINGS_FILE)\\n else:\\n # Get default profiles from user\\n try:\\n profiles_input = input(\\n 'Please enter space separated list of profiles to use: '\\n )\\n except KeyboardInterrupt:\\n # Avoid ugly stacktrace on ctrl-c in input\\n sys.exit(1)\\n # Setup config\\n config.add_section('profiles')\\n config.set('profiles', 'default', profiles_input)\\n # Write to config\\n config_file = open(SETTINGS_FILE, 'w')\\n config.write(config_file)\\n config_file.close()\\n\\n return config.get('profiles', 'default').split()\",\n \"def profiles(self):\\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\\n return list(filter(lambda x: x is not None, executor.map(self.profile_details, self.profiles_names())))\",\n \"def profiles(self):\\n return self._profiles\",\n \"def profiles(self):\\n return self._profiles\",\n \"def apply(self):\\r\\n\\r\\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\\r\\n\\r\\n def saving_file(xml):\\r\\n \\\"\\\"\\\" Save user profile in xml format to temp_ dir.\\\"\\\"\\\"\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\\r\\n\\r\\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\\r\\n \\\"/data/sampleProfile.xml\\\")\\r\\n\\r\\n # The below code will parse the sample xml file\\r\\n # and fill important details entered by the user.\\r\\n root_tree = parse_xml.getroot()\\r\\n root_tree[0].text = self.ssid.get()\\r\\n root_tree[1][0][0].text = self.ssid.get()\\r\\n root_tree[3].text = self.connection_mode.get().lower()\\r\\n security = root_tree[4][0]\\r\\n security[0][0].text = self.authentication.get()\\r\\n security[0][1].text = self.encryption.get()\\r\\n if self.authentication.get() != \\\"open\\\":\\r\\n etree.SubElement(security, \\\"sharedKey\\\")\\r\\n etree.SubElement(security[1], \\\"keyType\\\").text = \\\"passPhrase\\\"\\r\\n etree.SubElement(security[1], \\\"protected\\\").text = \\\"false\\\"\\r\\n etree.SubElement(security[1], \\\"keyMaterial\\\").text = self.password.get()\\r\\n\\r\\n # Save the xml file\\r\\n saving_file(root_tree)\\r\\n\\r\\n # Add profile to the system.\\r\\n temp_path = 'netsh wlan add profile filename=\\\"' + self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\"\\r\\n output_ = subprocess.run(temp_path + file_name + '.xml\\\"', shell=True,\\r\\n capture_output=True, text=True)\\r\\n os.remove(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\")\\r\\n\\r\\n # If unable to add profile.\\r\\n if output_.returncode != 0:\\r\\n message = \\\"Sorry, Unable to add profile.\\\\n(You entered wrong details \\\" \\\\\\r\\n \\\"or else you don't have admin rights.)\\\"\\r\\n image_ = \\\"error\\\"\\r\\n\\r\\n else:\\r\\n message = \\\"Profile added successfully (Please Refresh)\\\"\\r\\n image_ = \\\"warning\\\"\\r\\n\\r\\n MessageBox(self.parent, message, image_)\",\n \"def parse_profile_list_page(page):\\n page_tree = etree.HTML(page)\\n profile_list = page_tree.xpath(\\n '//table[@class=\\\"table_header\\\"]/tr[position() > 4 and position() < (last() - 3)]/td/node()[1]')\\n if profile_list:\\n profile_list[0] = \\\"main\\\"\\n return [text.strip() for text in profile_list]\\n else:\\n raise ProfileException(\\\"Failed to get profile list\\\")\",\n \"def get_list(profiles_folder, logger):\\n profile_list = []\\n with scandir(profiles_folder) as it:\\n for entry in it:\\n if entry.is_file():\\n filepath = profiles_folder + entry.name\\n profile = json_from_file(filepath, logger)\\n if profile is not None:\\n try:\\n profile_list.append({\\\"filepath\\\":filepath, \\\"name\\\":profile[\\\"name\\\"], \\\"description\\\":profile[\\\"description\\\"]})\\n except AttributeError:\\n logger.error(\\\"Missing attributes in \\\" + filepath)\\n logger.error(str(profile))\\n return profile_list\",\n \"def save_profile(file_path, profile):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n profile.save_to_xml(writer)\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def profile_directory_path(request, file):\\n return directory_path('profile', file)\",\n \"def get_resolved_profile_catalog(trestle_root: pathlib.Path, profile_path: pathlib.Path) -> cat.Catalog:\\n logger.debug(f'get resolved profile catalog for {profile_path} via generated Import.')\\n import_ = prof.Import(href=str(profile_path), include_all={})\\n import_filter = ProfileResolver.Import(trestle_root, import_)\\n logger.debug('launch pipeline')\\n result = next(import_filter.process())\\n return result\",\n \"def list_profiles(self, params):\\n return self.profiles\",\n \"def profiles_names(self):\\n url = get_url('profiles')\\n response = self._get(url)\\n raise_on_error(response)\\n return response.json()\",\n \"def dumpProfiles(profiles):\\n with open(userProfilesDir, \\\"w\\\") as outfile:\\n outfile.writelines(json.dumps(profiles, indent=4))\\n outfile.close()\",\n \"def ReadWiredNetworkProfile(self, profilename):\\n profile = {}\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n for x in config.options(profilename):\\n profile[x] = misc.Noneify(config.get(profilename, x))\\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\\n self.WiredNetwork = profile\\n return \\\"100: Loaded Profile\\\"\\n else:\\n self.WiredNetwork = None\\n return \\\"500: Profile Not Found\\\"\",\n \"def available_profiles(cls) -> List[str]:\\n return list(cfg.get(\\\"profiles\\\"))\",\n \"def list(self):\\n # List is to be extended (directories should not have a trailing slash)\\n paths_to_ignore = ['.DS_Store']\\n\\n profiles = []\\n cache = ClientCache(self._conan_api.cache_folder)\\n profiles_path = cache.profiles_path\\n if os.path.exists(profiles_path):\\n for current_directory, _, files in os.walk(profiles_path, followlinks=True):\\n files = filter(lambda file: os.path.relpath(\\n os.path.join(current_directory, file), profiles_path) not in paths_to_ignore, files)\\n\\n for filename in files:\\n rel_path = os.path.relpath(os.path.join(current_directory, filename),\\n profiles_path)\\n profiles.append(rel_path)\\n\\n profiles.sort()\\n return profiles\",\n \"def get_user():\\n with open(app.config['DATA_XML'], 'r') as xmlfile:\\n root = ElementTree.parse(xmlfile).getroot()\\n\\n for item in root.iter('server'):\\n result = '{}://{}'.format(\\n item.find('protocol').text,\\n item.find('host').text\\n )\\n\\n data = {\\n user.attrib['id']: {\\n 'name': user.find('name').text,\\n 'avatar': '{}{}'.format(\\n result,\\n user.find('avatar').text\\n )\\n }\\n for user in root.iter('user')\\n }\\n return OrderedDict(\\n sorted(\\n data.items(),\\n key=lambda result: itemgetter('name')(itemgetter(1)(result)),\\n cmp=locale.strcoll\\n )\\n )\",\n \"def loadProfilesFromPlist_(self, plist):\\r\\n \\r\\n LogInfo(u\\\"Loading update profiles with PublicationDate %@\\\", plist[u\\\"PublicationDate\\\"])\\r\\n self.profiles = dict()\\r\\n for name, updates in plist[u\\\"Profiles\\\"].iteritems():\\r\\n profile = list()\\r\\n for update in updates:\\r\\n profile.append(plist[u\\\"Updates\\\"][update])\\r\\n self.profiles[name] = profile\\r\\n self.publicationDate = plist[u\\\"PublicationDate\\\"]\\r\\n self.updatePaths = dict()\\r\\n for name, update in plist[u\\\"Updates\\\"].iteritems():\\r\\n filename, ext = os.path.splitext(os.path.basename(update[u\\\"url\\\"]))\\r\\n self.updatePaths[update[u\\\"sha1\\\"]] = u\\\"%s(%s)%s\\\" % (filename, update[u\\\"sha1\\\"][:7], ext)\\r\\n self.deprecatedInstallerBuilds = dict()\\r\\n try:\\r\\n for replacement, builds in plist[u\\\"DeprecatedInstallers\\\"].iteritems():\\r\\n for build in builds:\\r\\n self.deprecatedInstallerBuilds[build] = replacement\\r\\n except KeyError:\\r\\n LogWarning(u\\\"No deprecated installers\\\")\\r\\n if self.delegate:\\r\\n self.delegate.profilesUpdated()\",\n \"def _read_profile_file(cls, profile_file):\\n qid2title: Dict[str, str] = {}\\n qid2desc: Dict[str, str] = {}\\n alias2qids: Dict[str, list] = {}\\n type_systems: Dict[str, Dict[str, List[str]]] = {}\\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\\n\\n num_lines = sum(1 for _ in open(profile_file))\\n with open(profile_file, \\\"r\\\") as in_f:\\n for line in tqdm(in_f, total=num_lines, desc=\\\"Reading profile\\\"):\\n line = ujson.loads(line)\\n\\n # Check keys and schema\\n assert all(\\n k in line.keys() for k in REQUIRED_KEYS\\n ), f\\\"A key from {REQUIRED_KEYS} was not in {line}\\\"\\n try:\\n # Asserts the types are correct\\n ent = EntityObj(\\n entity_id=line[\\\"entity_id\\\"],\\n mentions=line[\\\"mentions\\\"],\\n title=line.get(\\\"title\\\", line[\\\"entity_id\\\"]),\\n description=line.get(\\\"description\\\", \\\"\\\"),\\n types=line.get(\\\"types\\\", {}),\\n relations=line.get(\\\"relations\\\", []),\\n )\\n except ValidationError as e:\\n print(e.json())\\n raise e\\n if ent.entity_id in qid2title:\\n raise ValueError(f\\\"{ent.entity_id} is already in our dump\\\")\\n qid2title[ent.entity_id] = ent.title\\n qid2desc[ent.entity_id] = ent.description\\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\\n for men_pair in ent.mentions:\\n # Lower case mentions for mention extraction\\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\\n if new_men not in alias2qids:\\n alias2qids[new_men] = []\\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\\n # Add type systems of type_sys -> QID -> list of type names\\n for type_sys in ent.types:\\n if type_sys not in type_systems:\\n type_systems[type_sys] = {}\\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\\n # Add kg relations QID -> relation -> list of object QIDs\\n for rel_pair in ent.relations:\\n if \\\"relation\\\" not in rel_pair or \\\"object\\\" not in rel_pair:\\n raise ValueError(\\n \\\"For each value in relations, it must be a JSON with keys relation and object\\\"\\n )\\n if ent.entity_id not in qid2relations:\\n qid2relations[ent.entity_id] = {}\\n if rel_pair[\\\"relation\\\"] not in qid2relations[ent.entity_id]:\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]] = []\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]].append(\\n rel_pair[\\\"object\\\"]\\n )\\n\\n # Sort mentions based on score, highest first\\n for al in list(alias2qids.keys()):\\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\\n # Add all qids to the type systems and KG connections with empty values\\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\\n for qid in qid2title:\\n for type_sys in type_systems:\\n if qid not in type_systems[type_sys]:\\n type_systems[type_sys][qid] = []\\n if qid not in qid2relations:\\n qid2relations[qid] = {}\\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations\",\n \"def parsePlayerProfile(path, args):\\n soup = BeautifulSoup(requests.get(\\\"{domain}/{pathStr}\\\".format(domain=args.domain[0], pathStr=path)).text, \\\"html.parser\\\")\\n\\n playerPathName = re.split(\\\"/|\\\\.\\\", path.lower())[-2]\\n # set up so a=11, b=12, etc, then joins the values together\\n playerID = \\\"\\\".join([char if char.isdigit() else str(ord(char) % 86) for char in playerPathName])\\n birthday = soup.find(\\\"span\\\", id=\\\"necro-birth\\\").get(\\\"data-birth\\\")\\n hasDebuted = soup.find(\\\"a\\\", string=\\\"Debut\\\")\\n debut = isRetired.get(\\\"href\\\") if hasDebuted else None\\n isRetired = soup.find(\\\"a\\\", string=\\\"Last Game\\\")\\n retired = isRetired.get(\\\"href\\\") if isRetired else None\\n\\n player = parseProfileHeader(soup, playerID, birthday, debut, retired, args)\\n\\n positionTable = soup.find(\\\"table\\\", id=\\\"standard_fielding\\\")\\n positions = parsePositionInfo(positionTable, args)\\n player.addPositions(positions)\\n\\n hittingTable = soup.find(\\\"table\\\", id=\\\"batting_standard\\\")\\n hittingStats = parseBattingTable(hittingTable, args)\\n player.addStats(hittingStats, \\\"batting\\\")\\n\\n pitchingTable = soup.find(\\\"table\\\", id=\\\"pitching_standard\\\")\\n advancedPathTag = soup.find(\\\"a\\\", href=re.compile(r\\\"-pitch.shtml\\\"), string=\\\"More Stats\\\")\\n pitchingStats = parsePitchingTables(pitchingTable, advancedPathTag, args)\\n player.addStats(pitchingStats, \\\"pitching\\\")\\n\\n return player\",\n \"def load(cls, filepath):\\n # Load profile from disk\\n with open(filepath, \\\"rb\\\") as infile:\\n data = pickle.load(infile)\\n\\n # remove profiler class if it exists\\n profiler_class = data.pop('profiler_class', None)\\n\\n # if the user didn't load from the a given profiler class, we need\\n # to determine which profiler is being loaded.\\n profiler_cls = cls\\n if cls is BaseProfiler:\\n if profiler_class == 'StructuredProfiler':\\n profiler_cls = StructuredProfiler\\n elif profiler_class == 'UnstructuredProfiler':\\n profiler_cls = UnstructuredProfiler\\n elif profiler_class is None: # deprecated case\\n profiler_cls = StructuredProfiler\\n if '_empty_line_count' in data:\\n profiler_cls = UnstructuredProfiler\\n else:\\n raise ValueError(f'Invalid profiler class {profiler_class} '\\n f'failed to load.')\\n\\n profile_options = profiler_cls._option_class()\\n profile_options.data_labeler.is_enabled = False\\n profiler = profiler_cls(None, options=profile_options)\\n\\n for key in data:\\n setattr(profiler, key, data[key])\\n\\n # Restore all data labelers\\n profiler._restore_data_labelers()\\n return profiler\",\n \"def list_profiles(self):\\n return self._get(\\\"posture\\\", box=BoxList)\",\n \"def profiles(self, profiles):\\n\\n self._profiles = profiles\",\n \"def call(self, failed=[], deletion=False):\\n\\n if deletion == False:\\n # NOTE we won't it to run only after the delete has completed\\n return\\n\\n log.debug(\\\"Reading profiles from progran\\\")\\n onos = ProgranHelpers.get_progran_onos_info(self.model_accessor)\\n profile_url = \\\"http://%s:%s/onos/progran/profile/\\\" % (onos['url'], onos['port'])\\n r = requests.get(profile_url, auth=HTTPBasicAuth(onos['username'], onos['password']))\\n res = r.json()['ProfileArray']\\n\\n\\n # remove default profiles\\n res = [p for p in res if \\\"Default\\\" not in p['Name']]\\n pnames = [p['Name'] for p in res]\\n log.debug(\\\"Received Profiles: \\\", profiles=pnames)\\n\\n field_mapping = {\\n 'Name': 'name',\\n 'Start': 'start',\\n 'End': 'end',\\n }\\n\\n field_transformations = {\\n 'Start': ProgranHelpers.date_to_time,\\n 'End': ProgranHelpers.date_to_time\\n }\\n\\n handover_mapping = {\\n 'A5Hysteresis': 'HysteresisA5',\\n 'A3Hysteresis': 'HysteresisA3'\\n }\\n\\n updated_profiles = []\\n\\n for p in res:\\n\\n\\n # checking for profiles\\n try:\\n si = ProgranServiceInstance.objects.get(name=p['Name'])\\n log.debug(\\\"Profile %s already exists, updating it\\\" % p['Name'])\\n\\n except IndexError:\\n si = ProgranServiceInstance()\\n\\n si.created_by = \\\"Progran\\\"\\n\\n log.debug(\\\"Profile %s is new, creating it\\\" % p['Name'])\\n\\n if not si.is_new:\\n # update IMSI association\\n xos_imsis_for_profile = [i.subscriber_service_instance.leaf_model for i in si.provided_links.all()]\\n progran_imsis_for_profile = p['IMSIRuleArray']\\n\\n log.debug(\\\"List of imsis for profile %s in XOS\\\" % p[\\\"Name\\\"], imsis=xos_imsis_for_profile)\\n log.debug(\\\"List of imsis for profile %s in ONOS\\\" % p[\\\"Name\\\"], imsis=progran_imsis_for_profile)\\n\\n for i in xos_imsis_for_profile:\\n if not i.imsi_number in progran_imsis_for_profile:\\n log.debug(\\\"Removing Imsi %s from profile %s\\\" % (i.imsi_number, p['Name']))\\n\\n imsi_link = ServiceInstanceLink.objects.get(subscriber_service_instance_id=i.id)\\n\\n # NOTE: this model has already been removed from the backend, no need to synchronize\\n imsi_link.backend_need_delete = False\\n imsi_link.no_sync = True\\n imsi_link.save() # we need to save it to avoid a synchronization loop\\n\\n imsi_link.delete()\\n else:\\n # remove from imsi list coming from progran everything we already know about\\n progran_imsis_for_profile.remove(i.imsi_number)\\n\\n for i in progran_imsis_for_profile:\\n log.debug(\\\"Adding Imsi %s to profile %s\\\" % (i, p['Name']))\\n imsi = MCordSubscriberInstance.objects.get(imsi_number=i)\\n imsi_to_profile = ServiceInstanceLink(provider_service_instance=si,\\n subscriber_service_instance=imsi)\\n imsi_to_profile.save()\\n\\n # if the model has not been synchronized yet, skip it\\n if not si.is_new and si.no_sync is False:\\n log.debug(\\\"Skipping profile %s as not synchronized\\\" % p['Name'])\\n # NOTE add it to the removed profiles to avoid deletion (this is ugly, I know)\\n updated_profiles.append(si.name)\\n continue\\n\\n # ugly fix\\n if 'AdmControl' in p.keys():\\n p['AdmControl'] = str(p['AdmControl'])\\n\\n si = ProgranHelpers.update_fields(si, p, field_mapping, field_transformations)\\n\\n # checking for handovers\\n handover_dict = p['Handover']\\n handover_dict = ProgranHelpers.convert_keys(handover_dict, handover_mapping)\\n del p['Handover']\\n\\n if si.handover_id:\\n handover = si.handover\\n log.debug(\\\"handover already exists, updating it\\\", handover=handover_dict)\\n else:\\n handover = Handover()\\n handover = ProgranHelpers.update_fields(handover, handover_dict)\\n log.debug(\\\"handover is new, creating it\\\", handover=handover_dict)\\n handover.created_by = \\\"Progran\\\"\\n\\n handover = ProgranHelpers.update_fields(handover, handover_dict)\\n handover.save()\\n\\n # Assigning handover to profile\\n si.handover = handover\\n\\n # si.backend_status = \\\"OK\\\"\\n # si.backend_code = 1\\n\\n si.no_sync = True\\n si.previously_sync = True\\n\\n if p[\\\"MMECfg\\\"]:\\n si.mmeip = str(p[\\\"MMECfg\\\"][\\\"IPAddr\\\"])\\n si.mmeport = str(p[\\\"MMECfg\\\"][\\\"Port\\\"])\\n\\n si.enacted = time.mktime(datetime.datetime.now().timetuple())\\n\\n si.save()\\n\\n updated_profiles.append(si.name)\\n\\n existing_profiles = [p.name for p in ProgranServiceInstance.objects.all() if not p.is_new]\\n deleted_profiles = ProgranHelpers.list_diff(existing_profiles, updated_profiles)\\n\\n if len(deleted_profiles) > 0:\\n for p in deleted_profiles:\\n si = ProgranServiceInstance.objects.get(name=p)\\n if si.created_by == 'XOS' and si.previously_sync == False:\\n # don't delete if the profile has been created by XOS and it hasn't been sync'ed yet\\n continue\\n # TODO delete also the associated Handover\\n log.debug(\\\"Profiles %s have been removed in progran, removing it from XOS\\\" % str(p))\\n si.delete()\",\n \"def find_ready_profiles(project, script):\\n profiles_ready = []\\n for profile in Profile.objects.filter(script=script):\\n errors = check_process_ready(project, profile)\\n if len(errors) == 0:\\n profiles_ready.append(profile)\\n return profiles_ready\",\n \"def get(self, name='default'):\\r\\n if name not in self._cache:\\r\\n full_path = os.path.join(self._base_folder, name)\\r\\n self._create_if_needed(full_path)\\r\\n self._cache[name] = Profile(full_path, name)\\r\\n return self._cache[name]\",\n \"def load_profile(dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"rb\\\") as f:\\n logger = pickle.load(f)\\n return logger\",\n \"def loadPerson(fileid):\\n dinf = {}\\n drel = {}\\n root = etree.Element(\\\"person\\\")\\n text = None\\n # TODO: put this in a global variable, and make a function to populate it from the DTD.\\n tags = [\\\"commonname\\\", \\\"ctitle\\\", \\\"gname\\\", \\\"mname\\\", \\\"fname\\\", \\\"nname\\\", \\\"nameorder\\\", \\\"gender\\\", \\\"bday\\\", \\\"dday\\\", \\\"stories\\\", \\\"mention\\\", \\\"appear1ch\\\", \\\"appear1wr\\\", \\\"conflict\\\", \\\"leadrel\\\", \\\"bodytyp\\\", \\\"age\\\", \\\"skin\\\", \\\"eyes\\\", \\\"hair\\\", \\\"dmarks\\\", \\\"dress\\\", \\\"attposs\\\", \\\"asmell\\\", \\\"personality\\\", \\\"speech\\\", \\\"formocc\\\", \\\"currocc\\\", \\\"strength\\\", \\\"weak\\\", \\\"mole\\\", \\\"hobby\\\", \\\"misc\\\", \\\"ethnic\\\", \\\"origin\\\", \\\"backstory\\\", \\\"residence\\\", \\\"minchar\\\", \\\"talent\\\", \\\"abil\\\", \\\"sgoal\\\", \\\"other\\\", \\\"relat\\\", \\\"aspects\\\", \\\"update\\\"]\\n tags.remove(\\\"currocc\\\")\\n tags.remove(\\\"formocc\\\")\\n tags.remove(\\\"relat\\\")\\n tags.append(\\\"file\\\")\\n for tag in tags:\\n dinf[tag] = [\\\"\\\",False]\\n dinf['currocc'] = {}\\n dinf['currocc']['pos'] = [\\\"\\\",False]\\n dinf['formocc'] = {}\\n dinf['formocc']['pos'] = [\\\"\\\",False]\\n dinf['aspects'] = {}\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n dinf['formocc']['events'] = events\\n if not idExists(fileid):\\n status.push(0,\\\"new person created... '%s'\\\" % fileid)\\n return (dinf,drel)\\n fn = os.path.join(config['realmdir'],fileid + \\\".xml\\\")\\n status.push(0,\\\"loading person from XML... '%s'\\\" % fn)\\n try:\\n with codecs.open(fn,'rU','utf-8') as f:\\n tree = etree.parse(f)\\n f.close()\\n root = tree.getroot()\\n except IOError as e:\\n print \\\" Could not open configuration file: %s\\\" % e\\n\\n ir = 0\\n for i in range(len(root)):\\n if root[i].tag is not None:\\n if root[i].tag == \\\"relat\\\":\\n if len(root[i]) > 0:\\n node = \\\"\\\"\\n node = root[i].find(\\\"file\\\")\\n if node is not None and node.text:\\n node = node.text.strip()\\n node = common.validateFileid(node)\\n drel[node] = {}\\n for j in root[i]:\\n if j.tag == \\\"events\\\":\\n if not drel[node].get('events'): drel[node]['events'] = {}\\n for k in j:\\n stone = str(len(drel[node]['events']))\\n drel[node]['events'][stone] = {}\\n for m in k:\\n if m.tag and m.text:\\n drel[node]['events'][stone][m.tag] = [m.text.strip(),False]\\n else: # elif j.tag != \\\"file\\\":\\n if j.tag and j.text:\\n drel[node][j.tag] = [j.text.strip(),False]\\n if config['debug'] > 3: print drel[node]\\n else:\\n if config['debug'] > 0:\\n print \\\"Invalid relat tag:\\\"\\n for c in root[i]:\\n print c.tag\\n else: # no relat length\\n if config['debug'] > 0: print \\\"Empty relat tag.\\\"\\n elif root[i].tag == \\\"currocc\\\":\\n# print \\\",\\\",\\n dinf['currocc'] = {}\\n try:\\n dinf['currocc']['pos'] = [root[i].find(\\\"pos\\\").text.strip(),False]\\n except AttributeError:\\n del dinf['currocc']\\n if dinf.get('currocc'):\\n events = {}\\n if len(root[i]) > 1:\\n for j in root[i]:\\n if j.tag is not None:\\n if j.tag == \\\"events\\\":\\n for k in j:\\n if k.tag == \\\"mstone\\\":\\n le = str(len(events))\\n events[le] = {}\\n events[le]['date'] = [\\\"\\\",False]\\n events[le]['event'] = [\\\"\\\",False]\\n for m in k:\\n if m.tag and m.text:\\n events[le][m.tag] = [m.text.strip(),False]\\n else:\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n else:\\n dinf['currocc'] = {}\\n dinf['currocc']['pos'] = [\\\"\\\",False]\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n elif root[i].tag == \\\"formocc\\\":\\n# print \\\",\\\",\\n dinf['formocc'] = {}\\n try:\\n dinf['formocc']['pos'] = [root[i].find(\\\"pos\\\").text.strip(),False]\\n except AttributeError:\\n del dinf['formocc']\\n if dinf.get('formocc'):\\n events = {}\\n if len(root[i]) > 1:\\n for j in root[i]:\\n if j.tag is not None:\\n if j.tag == \\\"events\\\":\\n for k in j:\\n if k.tag == \\\"mstone\\\":\\n le = str(len(events))\\n events[le] = {}\\n events[le]['date'] = [\\\"\\\",False]\\n events[le]['event'] = [\\\"\\\",False]\\n for m in k:\\n if m.tag and m.text:\\n events[le][m.tag] = [m.text.strip(),False]\\n else:\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['formocc']['events'] = events\\n else:\\n dinf['formocc'] = {}\\n dinf['formocc']['pos'] = [\\\"\\\",False]\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['formocc']['events'] = events\\n elif root[i].tag == \\\"aspects\\\":\\n if len(root[i]) > 0:\\n nodes = root[i]\\n for node in nodes:\\n k = str(len(dinf['aspects']))\\n dinf['aspects'][k] = {}\\n if node.tag and node.text:\\n dinf['aspects'][k] = [node.text.strip(),False]\\n else:\\n if config['debug'] > 0:\\n print \\\"Invalid aspects tag:\\\"\\n print node.tag + ': ' + node.text,\\n else: # no aspects length\\n if config['debug'] > 0: print \\\"Empty aspects tag.\\\"\\n elif root[i].text is not None:\\n# print \\\".\\\",\\n dinf[root[i].tag] = [root[i].text.strip(), False]\\n if config['debug'] > 2: print str(i) + \\\" \\\",\\n# print str(dinf)\\n return (dinf,drel)\",\n \"def load(cls, filepath):\\n return BaseProfiler.load(filepath)\",\n \"def load(self, config, profile_key):\\n # a none numeric argument is supposed to be 'alias'\\n if re.search(r\\\"\\\\b[0-9]+\\\\b\\\", profile_key) is None:\\n key = profile_key\\n self.alias = key\\n access_by = \\\"alias\\\"\\n else:\\n key = (int)(profile_key)\\n self.id = key\\n access_by = \\\"id\\\"\\n self.logger.debug(\\\"==> profile will be access by his '%s'\\\" % access_by)\\n\\n # check if the profile exists into config...\\n i = 0\\n profile_found = False\\n while (i < len(config[\\\"profiles\\\"])\\n and profile_found is False):\\n\\n if config[\\\"profiles\\\"][i][access_by] == key:\\n profile_found = True\\n self.logger.debug(\\\"==> profile '%s' found (access by '%s')\\\" %\\n (key, access_by))\\n self.config = config[\\\"profiles\\\"][i]\\n\\n i += 1\\n\\n # ... and if not raise an exception\\n if profile_found is False:\\n raise ProfileLoadError(\\\"profile '%s' not found\\\" % profile_key)\\n\\n # set profile properties\\n self.id = self.config[\\\"id\\\"]\\n self.alias = self.config[\\\"alias\\\"]\\n self.state_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".json\\\")\\n self.lock_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".lock\\\")\\n\\n # set the todo queue limit based on configuration\\n # - default is 1\\n try:\\n todo_limit_queue = self.config[\\\"todo_queue_limit\\\"]\\n self.todo_queue_limit = todo_limit_queue\\n except KeyError:\\n self.todo_queue_limit = 1\",\n \"def globalProfile():\\n return context.profiles\",\n \"def list_network_profiles(self, **params):\\r\\n return self.get(self.network_profiles_path, params=params)\",\n \"def read_plist(path: str) -> dict:\\n return _read_plist(path, plistlib.FMT_XML)\",\n \"def _load_data(self, filename):\\n if not os.path.isfile(filename):\\n return False\\n\\n with open(filename) as f:\\n data = pickle.load(f)\\n if data:\\n self.profiles = data['profiles']\\n self.user_data = data['user_data']\\n self.api_data = data['api_data']\\n else:\\n return False\",\n \"def getProfiles(context):\\n\\n analytics_tool = getToolByName(getSite(), 'portal_analytics')\\n # short circuit if user hasn't authorized yet\\n if not analytics_tool.is_auth():\\n return SimpleVocabulary([])\\n\\n try:\\n profiles = analytics_tool.makeCachedRequest('profiles')\\n except error.BadAuthenticationError:\\n choices = [('Please authorize with Google in the Google Analytics \\\\\\n control panel.', None)]\\n return SimpleVocabulary.fromItems(choices)\\n except error.RequestTimedOutError:\\n choices = [('The request to Google Analytics timed out. Please try \\\\\\n again later.', None)]\\n return SimpleVocabulary.fromItems(choices)\\n if profiles:\\n unique_choices = {}\\n for entry in profiles:\\n title = entry.get('name')\\n title = crop(title, 40)\\n tableId = entry.get('id')\\n unique_choices.update({title: tableId})\\n choices = unique_choices.items()\\n else:\\n choices = [('No profiles available', None)]\\n return SimpleVocabulary([SimpleTerm(c[1], c[1], c[0]) for c in choices])\",\n \"def profiles():\\n images = get_uploaded_images()\\n records = db.session.query(UserProfile).all()\\n return render_template('profiles.html', images=images, records =records)\",\n \"def handle_profile_changing(profiles: [ProfileSettingParser], project, script):\\n files_altered = list()\\n for profile in profiles:\\n try:\\n profile_obj = Profile.objects.get(\\n pk=profile.profile_id, script=script\\n )\\n for input_template in profile.input_templates:\\n try:\\n input_template_obj = InputTemplate.objects.get(\\n pk=input_template.input_template_id,\\n corresponding_profile=profile_obj,\\n )\\n for file in input_template.files:\\n try:\\n file_obj = File.objects.get(\\n project=project, pk=file\\n )\\n file_setting, _ = FileSetting.objects.get_or_create(\\n input_template=input_template_obj, file=file_obj\\n )\\n files_altered.append(file_setting)\\n except File.DoesNotExist:\\n pass\\n except InputTemplate.DoesNotExist:\\n pass\\n except Profile.DoesNotExist:\\n pass\\n return files_altered\",\n \"def from_dir(cls, dir_path, ngram_sizes, profile_len):\\n file_paths = []\\n for dir_path, unused, file_names in os.walk(dir_path):\\n for file_name in file_names:\\n file_path = os.path.join(dir_path, file_name)\\n file_paths.append(file_path)\\n profile = cls.from_files(file_paths, ngram_sizes, profile_len)\\n return profile\",\n \"def read_pfile(pfile_loc):\\n with open(pfile_loc, mode='r') as pfile:\\n lines = pfile.readlines()\\n\\n profiles = {}\\n nprofs = 0 # counter for total number of profiles so far\\n linestart = 0 # counter for which line to start at for each profile\\n nlines_tot = len(lines)\\n\\n while True:\\n # Read the header line for each profile first\\n lin1 = lines[linestart].split()\\n npts_prof = int(lin1[0])\\n\\n xname = lin1[1]\\n yname = lin1[2]\\n dyname = ''.join(lin1[3:])[:-1]\\n\\n # Generate and populate the profile arrays\\n x = np.zeros(npts_prof)\\n y = np.zeros(npts_prof)\\n dy = np.zeros(npts_prof)\\n for i in range(npts_prof):\\n split_line = lines[linestart + i + 1].split()\\n x[i] = float(split_line[0])\\n y[i] = float(split_line[1])\\n dy[i] = float(split_line[2][:-1])\\n\\n # profiles[xname + '_' + yname] = x # psinorm\\n profiles[xname] = x\\n profiles[yname] = y\\n profiles[dyname] = dy\\n\\n nprofs += 1\\n linestart += 1 + npts_prof\\n\\n if linestart >= nlines_tot:\\n break\\n\\n # Check if all psinorms are the same, consolidate if so (they are, don't bother separating)\\n\\n # condense = True\\n # psinorm = None\\n # for k in profiles.keys():\\n # if k is None or k=='':\\n # continue\\n #\\n # if k[:4] == 'psin':\\n # if psinorm is None:\\n # psinorm = profiles[k]\\n #\\n # if max(abs(profiles[k] - psinorm)) > 1e-5:\\n # condense = False\\n # break\\n\\n # if condense:\\n # profiles = {key: value for key, value in profiles.items()\\n # if key[:4] != 'psin' or key is None or key==''}\\n # profiles['psinorm'] = psinorm\\n\\n return profiles\",\n \"def list_profiles(request, pk=0):\\n context = {'items': [], 'resource_type': 'Profile'}\\n handle_expired_profiles()\\n if pk == 0:\\n context['h2'] = \\\"Configuration Profiles\\\"\\n context['header_1'] = \\\"Type\\\"\\n context['header_2'] = \\\"Last Modified\\\"\\n profiles = ConfigurationProfile.objects.all().reverse()\\n for profile in profiles:\\n assignment_count = profile.pending_install.count()\\n install_count = profile.installed.count()\\n data = {'filename': str(profile), 'type': \\\"macOS\\\", 'meta': profile, 'assignment_count': assignment_count,\\n 'install_count': install_count}\\n context['items'].append(data)\\n else:\\n device = get_object_or_404(Laptop, pk=pk)\\n context['h2'] = \\\"Profiles for {}\\\".format(device.name)\\n context['header_1'] = \\\"Version\\\"\\n context['header_2'] = \\\"Expires\\\"\\n context['device_view'] = True\\n context['device_id'] = pk\\n profiles = ConfigurationProfile.objects.filter(pending_install__in=[device])\\n profiles |= ConfigurationProfile.objects.filter(installed__in=[device])\\n for profile in profiles:\\n status = 'Not assigned'\\n for entry in profile.installed.all():\\n if entry == device:\\n status = 'Installed'\\n for entry in profile.pending_install.all():\\n if entry == device:\\n status = 'Assigned'\\n record = InstallationRecord.objects.filter(profile=profile, device=device, active=True).first()\\n expires_soon = False\\n if record is not None and record.expires is not None:\\n if timezone.now() < record.expires < timezone.now() + timezone.timedelta(days=30):\\n expires_soon = True\\n data = {'filename': str(profile), 'downloadable': False, 'install_record': record, 'meta': profile,\\n 'status': status, 'expires_soon': expires_soon}\\n context['items'].append(data)\\n\\n return render(request, 'mdm/resource_list.html', context)\",\n \"def init_profiles_using_namedtuple(no_profiles: int):\\n profiles = []\\n Profile = namedtuple('Profile', fake.profile().keys())\\n for _ in range(no_profiles):\\n profiles.append(Profile(**fake.profile()))\\n return profiles\",\n \"def find_prefs():\\n conf = SafeConfigParser()\\n try:\\n conf.read(PROFILES)\\n except Exception as err:\\n log.error('reading profiles.ini: %s', err)\\n return None\\n\\n for section in conf.sections():\\n if conf.has_option(section, 'Name') and \\\\\\n conf.get(section, 'Name') == 'default':\\n path = conf.get(section, 'Path')\\n if conf.getboolean(section, 'IsRelative'):\\n path = os.path.join(CONFDIR, path)\\n\\n return unicodify(os.path.join(path, 'prefs.js'))\\n\\n return None\",\n \"def show_all_profiles(profiles, attribute=None, image=None, height=None, fname=None, **kwargs):\\n\\n # Filter profiles according to attribute if attribute is set\\n if attribute:\\n profiles = filter(lambda x: x.description['attribute'] == attribute, profiles)\\n # Same for image\\n if image:\\n profiles = filter(lambda x: x.description['tree']['image_name'] == image, profiles)\\n\\n for p in profiles:\\n show_profiles(p, height, fname, **kwargs)\",\n \"def get_peak_stats(file_path):\\n peak_stats = dict(numberOfPeaks=0,\\n peakMean=0,\\n peakMeanStd=0,\\n peakMedian=0,\\n peakMedianStd=0,\\n medianTagSingletons=0,\\n singletons=0)\\n stddevs = []\\n peak_singleton_scores = []\\n scores = []\\n singletons = 0\\n i = 0\\n with open(file_path) as fh:\\n for i, line in enumerate(fh):\\n items = line.split('\\\\t')\\n # Gff column 6 is score.\\n score = float(items[5])\\n scores.append(score)\\n # Gff column 9 is a semicolon-separated list.\\n attributes = items[8].split(';')\\n for attribute in attributes:\\n if attribute.startswith('stddev'):\\n val = float(attribute.split('=')[1])\\n stddevs.append(val)\\n if val == 0.0:\\n # We have a peakSingleton.\\n singletons += 1\\n peak_singleton_scores.append(score)\\n break\\n fh.close()\\n if i > 0:\\n # The number of lines in the file is the number of peaks.\\n peak_stats['numberOfPeaks'] = i + 1\\n peak_stats['peakMean'] = numpy.mean(scores)\\n peak_stats['peakMeanStd'] = numpy.mean(stddevs)\\n peak_stats['peakMedian'] = numpy.median(scores)\\n peak_stats['peakMedianStd'] = numpy.median(stddevs)\\n peak_stats['medianTagSingletons'] = numpy.median(peak_singleton_scores)\\n peak_stats['singletons'] = singletons\\n return peak_stats\",\n \"def get_profile_info(self):\\n\\n drill_path = str(Path.home())+\\\"/Documents/ball_e_profiles/drill_profiles/{drill_name}/{drill_name}.csv\\\".format(\\n drill_name=self.drill_name)\\n with open(drill_path) as file:\\n csv_reader = csv.reader(file, delimiter=',')\\n row_count = 0\\n info_dict = dict()\\n for row in csv_reader:\\n if row_count == 0:\\n row_count += 1\\n else:\\n info_dict[row[0]] = [row[1], row[2], row[3]]\\n row_count += 1\\n\\n return info_dict\",\n \"def read_config(profile):\\n p = Path(CONFIG_FILE_PATH).expanduser()\\n p.parent.mkdir(parents=True, exist_ok=True)\\n config = configparser.ConfigParser()\\n config.read(str(p))\\n\\n if config.has_section(profile):\\n return dict(config.items(profile))\\n else:\\n return {}\",\n \"def get_profiles(species_id, probes, limit=1000):\\n profiles = (\\n ExpressionProfile.query.options(undefer(\\\"profile\\\"))\\n .filter(ExpressionProfile.probe.in_(probes))\\n .filter_by(species_id=species_id)\\n .options(joinedload(\\\"sequence\\\").load_only(\\\"name\\\").noload(\\\"xrefs\\\"))\\n .limit(limit)\\n .all()\\n )\\n\\n return profiles\",\n \"def read_pro(path,var_to_plot= None):\\n\\n # Which variables are you interested in?\\n\\n if var_to_plot:\\n var_codes = ['0500','0501',pro_code_dict(var_to_plot,inverse=True)]\\n else:\\n var_codes = ['0500', '0501', '0502', '0503', '0506', '0508',\\n '0509', '0511', '0512', '0513', '0515',\\n '0516', '0521', '0535', '0540', '0541']\\n\\n # Set up the dictionary to be returned. Dictionary is organised by variable name.\\n\\n code_dict = pro_code_dict(return_all=True)\\n\\n variables = {}\\n for var in var_codes:\\n variables[code_dict[var]] = []\\n\\n # Open the .PRO file\\n\\n with open(path, \\\"r\\\") as f:\\n\\n # Iterate line by line\\n\\n for line in f.readlines():\\n\\n # If the variable code (first four chars) matches the variable of interest,\\n # append that line to the list of lines\\n\\n if line[:4] in var_codes:\\n variables[code_dict[line[:4]]].append(line)\\n\\n\\n # Now remove the header data\\n\\n for variable in variables.keys():\\n\\n variables[variable].pop(0)\\n\\n snowpro_list = [snowpro_from_snapshot(date_index, variables) for date_index in range(len(variables['Date']))]\\n\\n return (snowpro_list)\",\n \"def updateUsersProfilesIfNewer_(self, plist):\\r\\n \\r\\n # Load UpdateProfiles from the user's application support directory.\\r\\n userUpdateProfiles = NSDictionary.dictionaryWithContentsOfFile_(self.userUpdateProfilesPath)\\r\\n \\r\\n # If the bundle's plist is newer, update the user's.\\r\\n if (not userUpdateProfiles) or (userUpdateProfiles[u\\\"PublicationDate\\\"].timeIntervalSinceDate_(plist[u\\\"PublicationDate\\\"]) < 0):\\r\\n LogDebug(u\\\"Saving updated UpdateProfiles.plist\\\")\\r\\n self.saveUsersProfiles_(plist)\\r\\n return plist\\r\\n else:\\r\\n return userUpdateProfiles\",\n \"def read_data(file):\\n with rasterio.open(file) as f:\\n data = f.read(1)\\n profile = f.profile\\n return data, profile\",\n \"def get_all_profiles(store=\\\"local\\\"):\\n return {\\n \\\"Domain Profile\\\": get_all_settings(profile=\\\"domain\\\", store=store),\\n \\\"Private Profile\\\": get_all_settings(profile=\\\"private\\\", store=store),\\n \\\"Public Profile\\\": get_all_settings(profile=\\\"public\\\", store=store),\\n }\",\n \"def read_profile (filename, index):\\n \\n col_names = ['m', 'r', 'F', 'P', 'rho', 'T', 'u', 's', \\n 'c_P', 'gamma_ad', 'nabla_ad', 'mu', 'n_e', \\n 'P_e', 'P_rad', 'nabla_rad', 'nabla', 'v_c',\\n 'kappa', 'q_nuc', 'q_PP', \\n 'q_CNO', 'q_3a', 'q_nunuc',\\n 'q_nu', 'q_grav', 'X', 'X_mol',\\n 'X_+', 'Y', 'Y_+', 'Y_++', 'X_C', 'X_N', \\n 'X_O', 'Psi']\\n\\n return read_ez_zip(filename, 'structure_{:05d}.txt'.format(index), col_names)\",\n \"def show_profiles(profiles, height=None, fname=None, **kwargs):\\n _show_profiles(profiles, height, fname, **kwargs)\",\n \"def get_data(self, n=-1): \\n try:\\n return Profile(os.path.join(self.name, self.files[n]))\\n except IndexError:\\n return None\",\n \"def merge_structural_profile(profile_path, merged_path):\\n def list_to_str(lst):\\n ''' Given a list, return the string of that list with tab separators\\n '''\\n return reduce( (lambda s, f: s + '\\\\t' + str(f)), lst, '')\\n\\n # external loop profile\\n E_path = profile_path+'E_profile.txt'\\n fEprofile = open(E_path)\\n Eprofiles = fEprofile.readlines()\\n\\n # hairpin loop profiles\\n H_path = profile_path+'H_profile.txt'\\n fHprofile = open(H_path)\\n Hprofiles = fHprofile.readlines()\\n\\n # internal loop profiles\\n I_path = profile_path+'I_profile.txt'\\n fIprofile = open(I_path)\\n Iprofiles = fIprofile.readlines()\\n\\n # multi-loop profiles\\n M_path = profile_path+ 'M_profile.txt'\\n fMprofile = open(M_path)\\n Mprofiles = fMprofile.readlines()\\n\\n num_seq = int(len(Eprofiles)/2)\\n\\n # parse into a single file\\n fhout = open(merged_path, 'w')\\n for i in range(num_seq):\\n id = Eprofiles[i*2].split()[0]\\n fhout.write(id+'\\\\n')\\n H_prob = Hprofiles[i*2+1].split()\\n I_prob = Iprofiles[i*2+1].split()\\n M_prob = Mprofiles[i*2+1].split()\\n E_prob = Eprofiles[i*2+1].split()\\n P_prob = map( (lambda a, b, c, d: 1-float(a)-float(b)-float(c)-float(d)), H_prob, I_prob, M_prob, E_prob)\\n fhout.write(list_to_str(P_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(H_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(I_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(M_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(E_prob[:len(P_prob)])+'\\\\n')\\n fhout.close()\\n\\n return num_seq\",\n \"def load_training_data(face_profile_directory):\\n # delete_empty_profile(face_profile_directory) # delete profile directory without images\\n\\n # Get a the list of folder names in face_profile as the profile names\\n face_profile_names = [d for d in os.listdir(face_profile_directory) if \\\".\\\" not in str(d)]\\n\\n if len(face_profile_names) < 2: \\n logging.error(\\\"\\\\nFace profile contains too little profiles (At least 2 profiles are needed)\\\")\\n exit()\\n # print('cc')\\n # print(face_profile_names)\\n first_data = str(face_profile_names[0])\\n first_data_path = os.path.join(face_profile_directory, first_data)\\n read_images_from_single_face_profile(first_data_path, 0)\\n \\n \\n for i in range(1, len(face_profile_names)):\\n directory_name = str(face_profile_names[i])\\n directory_path = os.path.join(face_profile_directory, directory_name)\\n read_images_from_single_face_profile(directory_path, i)\",\n \"def load_profile(self):\\n self.dut_testpmd.start_testpmd(\\n \\\"Default\\\", \\\"--pkt-filter-mode=perfect --port-topology=chained \\\\\\n --txq=%s --rxq=%s\\\"\\n % (self.PF_QUEUE, self.PF_QUEUE))\\n self.vm0_testpmd.start_testpmd(\\n VM_CORES_MASK, \\\"--port-topology=chained --txq=%s --rxq=%s\\\"\\n % (self.VF_QUEUE, self.VF_QUEUE))\\n self.dut_testpmd.execute_cmd('port stop all')\\n time.sleep(1)\\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\\n self.dut_testpmd.execute_cmd('ddp add 0 /tmp/gtp.pkgo,/tmp/gtp.bak')\\n out = self.dut_testpmd.execute_cmd('ddp get list 0')\\n self.verify(\\\"Profile number is: 1\\\" in out,\\n \\\"Failed to load ddp profile!!!\\\")\\n self.dut_testpmd.execute_cmd('port start all')\\n time.sleep(1)\\n self.dut_testpmd.execute_cmd('set fwd rxonly')\\n self.dut_testpmd.execute_cmd('set verbose 1')\\n self.dut_testpmd.execute_cmd('start')\\n self.vm0_testpmd.execute_cmd('set fwd rxonly')\\n self.vm0_testpmd.execute_cmd('set verbose 1')\\n self.vm0_testpmd.execute_cmd('start')\",\n \"def load_users(filename):\\n with open(filename, 'rb') as f:\\n for line in f:\\n yield line.split(':', 1)[0]\",\n \"def recover_from_xml(path):\\n\\tlistz = extract_list_from_xml(path)\\n\\trecover_old_filename(listz)\",\n \"def _parse_profile_data (self, netflix_page_data):\\n profiles = {};\\n important_fields = [\\n 'profileName',\\n 'isActive',\\n 'isAccountOwner',\\n 'isKids'\\n ]\\n # values are accessible via dict (sloppy parsing successfull)\\n if type(netflix_page_data) == dict:\\n for profile_id in netflix_page_data.get('profiles'):\\n if self._is_size_key(key=profile_id) == False and type(netflix_page_data['profiles'][profile_id]) == dict and netflix_page_data['profiles'][profile_id].get('avatar', False) != False:\\n profile = {'id': profile_id}\\n for important_field in important_fields:\\n if important_field == 'profileName':\\n profile.update({important_field: HTMLParser.HTMLParser().unescape(netflix_page_data['profiles'][profile_id]['summary'][important_field]).encode('utf8')})\\n else:\\n profile.update({important_field: netflix_page_data['profiles'][profile_id]['summary'][important_field]})\\n avatar_base = netflix_page_data['nf'].get(netflix_page_data['profiles'][profile_id]['summary']['avatarName'], False);\\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\\n profile.update({'avatar': avatar, 'isFirstUse': False})\\n profiles.update({profile_id: profile})\\n return profiles\\n\\n # values are stored in lists (returned from JS parser)\\n # TODO: get rid of this christmas tree of doom\\n for item in netflix_page_data:\\n if 'hasViewedRatingWelcomeModal' in dict(item).keys():\\n for profile_id in item:\\n if self._is_size_key(key=profile_id) == False and type(item[profile_id]) == dict and item[profile_id].get('avatar', False) != False:\\n profile = {'id': profile_id}\\n for important_field in important_fields:\\n profile.update({important_field: item[profile_id]['summary'][important_field]})\\n avatar_base = item['nf'].get(item[profile_id]['summary']['avatarName'], False);\\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\\n profile.update({'avatar': avatar})\\n profiles.update({profile_id: profile})\\n return profiles\",\n \"def initialize():\\n pinfo = pathinfo()\\n\\n if not pinfo:\\n raise Error('cannot decide profile directory, $SUZUPROFILEDIR, $APPDATA, or $HOME should be configured.')\\n\\n # dig profdir\\n if not os.path.isdir(pinfo['profiledir']):\\n os.mkdir(pinfo['profiledir'])\\n\\n # test dir\\n if not os.path.isdir(pinfo['profiledir']):\\n raise Error('profile directory {} does not exist, nor cannot create'.format(pinfo['profiledir']))\\n\\n # test profile data file\\n if not os.path.isfile(pinfo['config']):\\n with open(pinfo['config'], 'w') as stream:\\n json.dump(config.default, stream, indent=2, sort_keys=True)\\n\\n # test file (do not care on the contents)\\n if not os.path.isfile(pinfo['config']):\\n raise Error('config file {} does not exist, nor cannot create'.format(pinfo['config']))\\n\\n return pinfo\",\n \"def generate_files(input_path, output_path):\\n profiles = pd.read_csv(f'{input_path}/Profile/Profile.csv')\\n\\n # call all predictors\\n predicted = {f: pr.all_predictors[f].predict(profiles=profiles, base_folder=input_path)\\n for f in pr.features if f in pr.all_predictors}\\n\\n for i, (index, row) in enumerate(profiles.iterrows()):\\n file = open(os.path.join(output_path, f'{row[\\\"userid\\\"]}.xml'), 'w')\\n gender = pr.get_gender(predicted['gender'][row[1]]) if row[1] in predicted['gender'] else predicted['age_group_alternative'][row[1]]\\n open_ = predicted['open'][row[1]] if row[1] in predicted['open'] else 'null'\\n conscientious = predicted['conscientious'][row[1]] if row[1] in predicted['conscientious'] else 'null'\\n extrovert = predicted['extrovert'][row[1]] if row[1] in predicted['extrovert'] else 'null'\\n agreeable = predicted['agreeable'][row[1]] if row[1] in predicted['agreeable'] else 'null'\\n neurotic = predicted['neurotic'][row[1]] if row[1] in predicted['neurotic'] else 'null'\\n age = predicted['age_group'][row[1]] if row[1] in predicted['age_group'] else 'null'\\n\\n file.write(f'<user id=\\\"{row[1]}\\\" ')\\n file.write(f'age_group=\\\"{age}\\\" ')\\n file.write(f'gender=\\\"{gender}\\\" ')\\n file.write(f'extrovert=\\\"{extrovert}\\\" ')\\n file.write(f'neurotic=\\\"{neurotic}\\\" ')\\n file.write(f'agreeable=\\\"{agreeable}\\\" ')\\n file.write(f'open=\\\"{open_}\\\" ')\\n file.write(f'conscientious=\\\"{conscientious}\\\" />')\\n file.close()\",\n \"def read_file(filepath: str) -> dict:\\n if not filepath.endswith(\\\".csv\\\"):\\n raise RuntimeError(\\\"File extension must be .csv\\\")\\n\\n people = {}\\n with open(filepath) as csv:\\n for line in csv:\\n email, person = Parser.parse_line(line.rstrip(\\\"\\\\n\\\"))\\n if email not in people:\\n people[email] = person\\n else:\\n print(\\\"Ignoring person with duplicate email {}\\\".format(email))\\n return people\",\n \"def get_server_profile_attributes(name, attribute=None):\\n general_xpaths = {\\n \\\"name\\\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\\n \\\"description\\\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\\n \\\"server hardware\\\": FusionServerProfilesPage.ID_SERVER_HARDWARE_NAME,\\n \\\"associated server\\\": FusionServerProfilesPage.ID_ASSOCIATED_SERVER,\\n \\\"server hardware type\\\": FusionServerProfilesPage.ID_PROFILE_HARDWARE,\\n \\\"enclosure group\\\": FusionServerProfilesPage.ID_SERVER_ENC_GROUP,\\n \\\"affinity\\\": FusionServerProfilesPage.ID_SERVER_AFFINITY,\\n \\\"server power\\\": FusionServerProfilesPage.ID_SERVER_POWER_STATUS,\\n \\\"serial number\\\": FusionServerProfilesPage.ID_SERVER_SERIAL_NUMBER,\\n \\\"uuid\\\": FusionServerProfilesPage.ID_SERVER_UUID,\\n }\\n\\n # Navigate to Server Profile\\n if not select_server_profile(name):\\n logger._warn(\\\"Failed to find Server Profile '%s'\\\" % name)\\n BuiltIn().sleep(4)\\n # Get attribute data\\n if attribute is not None:\\n keys = general_xpaths.keys()\\n if attribute.lower() not in keys:\\n logger._warn(\\\"Invalid attribute '%s'.\\\" % attribute)\\n return None\\n\\n # Get specified attribute\\n xpath = general_xpaths[attribute.lower()]\\n ui_lib.wait_for_element_visible(xpath, timeout=10)\\n value = ui_lib.ignore_staleElementRefException(\\\"get_text\\\", xpath)\\n\\n return value\\n else:\\n # Get all attributes of Server Profile\\n general_data = {}\\n for attribute, xpath in general_xpaths.iteritems():\\n value = ui_lib.ignore_staleElementRefException(\\\"get_text\\\", xpath)\\n general_data[attribute] = value\\n return general_data\\n\\n return None\",\n \"def getProfileInfo(self):\\n doc = minidom.parse(urllib.urlopen(serverString + \\\"/rest/user/\\\" + self.name))\\n for element in doc.getElementsByTagName(\\\"user\\\")[0].childNodes:\\n if element.nodeType != minidom.Node.ELEMENT_NODE:\\n continue\\n elif element.tagName == \\\"status\\\" and int(element.firstChild.data) != 1:\\n raise ServerError(element.firstChild.data)\\n elif element.tagName == \\\"input\\\":\\n self.name = element.firstChild.data\\n elif element.tagName == \\\"id\\\":\\n self.id = element.firstChild.data\\n elif element.tagName == \\\"image\\\":\\n self.image = element.firstChild.data\\n elif element.tagName == \\\"tagline\\\":\\n if element.firstChild == None:\\n self.tagline = None\\n else:\\n self.tagline = element.firstChild.data\\n elif element.tagName == \\\"creation\\\":\\n self.created = datetime.datetime.strptime(element.firstChild.data[:element.firstChild.data.rfind(\\\".\\\")]+\\\".GMT\\\", \\\"%Y-%m-%d %H:%M:%S.%Z\\\")\",\n \"def getFirefoxProfile(self, profileDirectory=None):\\n fp = webdriver.FirefoxProfile(profile_directory=profileDirectory)\\n return fp\",\n \"def _process_profiles(profiles):\\n # keep_attributes = str.split(\\\"user_id public completion_percentage gender region last_login registration age\\\")\\n # p2=profiles[keep_attributes]\\n p2 = profiles\\n p2['region'] = p2['region'].astype('category')\\n p2['public'] = p2['public'].astype('category')\\n p2['gender'] = p2['gender'].astype('category')\\n p2['last_login'] = pd.to_datetime(p2['last_login'])\\n p2['registration'] = pd.to_datetime(p2['registration'])\\n p2.loc[p2.age == 0, 'age'] = np.nan\\n\\n return p2\",\n \"def addon_profiles(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input['AddonProfilesArgs']]]]:\\n return pulumi.get(self, \\\"addon_profiles\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81233364","0.77697146","0.67844415","0.6566505","0.636305","0.62747896","0.6168049","0.6128615","0.61190236","0.6096743","0.5969697","0.5946075","0.59331304","0.5897405","0.58071595","0.57577217","0.5669829","0.564856","0.55757815","0.5557105","0.55390906","0.55295134","0.5503458","0.5449346","0.5407596","0.5406341","0.5399971","0.53979677","0.53647906","0.5352732","0.53230983","0.52931124","0.52563125","0.52504396","0.52499455","0.52499455","0.52374244","0.52354825","0.5231485","0.52097905","0.51901996","0.5184926","0.5172769","0.517148","0.5170383","0.51690495","0.51241523","0.5118223","0.5100011","0.50977534","0.5087667","0.50775945","0.50349456","0.5027688","0.5017642","0.5013723","0.50019616","0.50003934","0.49985805","0.49847746","0.49811694","0.49542153","0.49375874","0.49246097","0.49119514","0.49058414","0.49004942","0.4891391","0.48900512","0.48869568","0.4882925","0.48723784","0.48694178","0.48446935","0.48268005","0.48201615","0.48145962","0.48136187","0.48085967","0.47956493","0.47802317","0.4777541","0.47770414","0.47722986","0.47711205","0.4766329","0.47651306","0.47586268","0.47473106","0.47412956","0.47392967","0.47357613","0.47258464","0.47037968","0.4693037","0.46872208","0.46838826","0.46798187","0.46703082","0.46694022"],"string":"[\n \"0.81233364\",\n \"0.77697146\",\n \"0.67844415\",\n \"0.6566505\",\n \"0.636305\",\n \"0.62747896\",\n \"0.6168049\",\n \"0.6128615\",\n \"0.61190236\",\n \"0.6096743\",\n \"0.5969697\",\n \"0.5946075\",\n \"0.59331304\",\n \"0.5897405\",\n \"0.58071595\",\n \"0.57577217\",\n \"0.5669829\",\n \"0.564856\",\n \"0.55757815\",\n \"0.5557105\",\n \"0.55390906\",\n \"0.55295134\",\n \"0.5503458\",\n \"0.5449346\",\n \"0.5407596\",\n \"0.5406341\",\n \"0.5399971\",\n \"0.53979677\",\n \"0.53647906\",\n \"0.5352732\",\n \"0.53230983\",\n \"0.52931124\",\n \"0.52563125\",\n \"0.52504396\",\n \"0.52499455\",\n \"0.52499455\",\n \"0.52374244\",\n \"0.52354825\",\n \"0.5231485\",\n \"0.52097905\",\n \"0.51901996\",\n \"0.5184926\",\n \"0.5172769\",\n \"0.517148\",\n \"0.5170383\",\n \"0.51690495\",\n \"0.51241523\",\n \"0.5118223\",\n \"0.5100011\",\n \"0.50977534\",\n \"0.5087667\",\n \"0.50775945\",\n \"0.50349456\",\n \"0.5027688\",\n \"0.5017642\",\n \"0.5013723\",\n \"0.50019616\",\n \"0.50003934\",\n \"0.49985805\",\n \"0.49847746\",\n \"0.49811694\",\n \"0.49542153\",\n \"0.49375874\",\n \"0.49246097\",\n \"0.49119514\",\n \"0.49058414\",\n \"0.49004942\",\n \"0.4891391\",\n \"0.48900512\",\n \"0.48869568\",\n \"0.4882925\",\n \"0.48723784\",\n \"0.48694178\",\n \"0.48446935\",\n \"0.48268005\",\n \"0.48201615\",\n \"0.48145962\",\n \"0.48136187\",\n \"0.48085967\",\n \"0.47956493\",\n \"0.47802317\",\n \"0.4777541\",\n \"0.47770414\",\n \"0.47722986\",\n \"0.47711205\",\n \"0.4766329\",\n \"0.47651306\",\n \"0.47586268\",\n \"0.47473106\",\n \"0.47412956\",\n \"0.47392967\",\n \"0.47357613\",\n \"0.47258464\",\n \"0.47037968\",\n \"0.4693037\",\n \"0.46872208\",\n \"0.46838826\",\n \"0.46798187\",\n \"0.46703082\",\n \"0.46694022\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7774989"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11590,"cells":{"query":{"kind":"string","value":"Loads profiles from a file. file_path>The absolute path the xml file Returns a dict of the profiles"},"document":{"kind":"string","value":"def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def import_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n return profiles","def load_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n if len(profiles) == 0:\r\n #Just in case\r\n profiles[\"Default\"] = Profile()\r\n profiles[\"Default\"].Name = \"Default\"\r\n #Some default templates\r\n profiles[\"Default\"].FileTemplate = \"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\"\r\n profiles[\"Default\"].FolderTemplate = \"{<publisher>}\\{<imprint>}\\{<series>}{ (<startyear>{ <format>})}\"\r\n \r\n if not lastused:\r\n lastused = [profiles.keys()[0]]\r\n \r\n return profiles, lastused","def load(path):\n\n parser = ConfigParser()\n parser.read(str(path))\n\n def _get(section, option):\n try:\n return parser.get(section, option)\n except (NoSectionError, NoOptionError):\n return None\n\n profiles = {}\n\n for section in parser.sections():\n profiles[section] = Profile(\n domain=_get(section, \"domain\"),\n protocol=_get(section, \"protocol\"),\n client_id=_get(section, \"client_id\"),\n client_secret=_get(section, \"client_secret\"),\n )\n\n return profiles","def loadProfiles():\n with open(userProfilesDir, \"r\") as infile:\n profiles = json.loads(\"\\n\".join(infile.readlines()))\n infile.close()\n return profiles","def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\n Dict[str, List[str]]]:\n individuals = [[]]\n sublist = 0\n content = profiles_file.readlines()\n for i in content:\n if i != '\\n':\n individuals[sublist].append(i)\n else:\n sublist += 1\n individuals.append([])\n return sort_profile(individuals)","def load_people(self, file_path):\n pass","def load_profile(path, profile):\n profiles = load(path)\n try:\n return profiles[profile]\n except KeyError:\n return Profile(None, None, None, None)","def get_profile(path=\"~\"):\n global profiles\n profile = profiles.get(path,None)\n if not profile:\n profile = InitFileConfig(os.path.join(path,\".myradioprofile\"), {} )\n profiles[path] = profile\n return profile","def _read_profiles(profile_directory):\n # Initialize key variables\n profiles = defaultdict(\n lambda: defaultdict(lambda: defaultdict()))\n\n # Read the yaml files in the profiles directory\n files = os.listdir(profile_directory)\n filenames = ['{}{}{}'.format(\n profile_directory, os.sep, nextfile) for nextfile in files]\n\n for _filename in sorted(filenames):\n # Get rid of excess os.sep separators\n pathitems = _filename.split(os.sep)\n filename = os.sep.join(pathitems)\n\n # Skip obvious\n if os.path.isfile(filename) is False:\n continue\n if filename.lower().endswith('.yaml') is False:\n continue\n\n with open(filename, 'r') as stream:\n try:\n _profiles = yaml.load(stream)['data']\n except yaml.YAMLError as exc:\n print(exc)\n\n # Create dictionary\n for item in _profiles:\n firstname = item['firstname']\n lastname = item['lastname']\n height = item['height']\n weight = item['weight']\n birthdate = item['birthdate']\n profiles[lastname][firstname][birthdate] = {\n 'height': height, 'weight': weight}\n\n return profiles","def parse_file(file_path):\n with open(file_path) as f:\n return XmlPropertyListParser().parse(f)","def from_file(cls, file_path, ngram_sizes, profile_len):\n profile = cls.from_files((file_path, ), ngram_sizes, profile_len)\n return profile","def _recurse_load_profile(self, text, profile_path):\n try:\n inherited_profile = Profile()\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\n profile_parser = _ProfileParser(text)\n # Iterate the includes and call recursive to get the profile and variables\n # from parent profiles\n for include in profile_parser.includes:\n # Recursion !!\n profile = self._load_profile(include, cwd)\n inherited_profile.compose_profile(profile)\n\n # Current profile before update with parents (but parent variables already applied)\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\n inherited_profile)\n return inherited_profile\n except ConanException:\n raise\n except Exception as exc:\n raise ConanException(\"Error parsing the profile text file: %s\" % str(exc))","def loadProfile(fname):\n \n x = np.loadtxt(fname)\n return x[:,1]","def _load_profile(self, profile_name, cwd):\n\n profile_path = self.get_profile_path(profile_name, cwd)\n try:\n text = load_user_encoded(profile_path)\n except Exception as e:\n raise ConanException(f\"Cannot load profile:\\n{e}\")\n\n # All profiles will be now rendered with jinja2 as first pass\n base_path = os.path.dirname(profile_path)\n file_path = os.path.basename(profile_path)\n context = {\"platform\": platform,\n \"os\": os,\n \"profile_dir\": base_path,\n \"profile_name\": file_path,\n \"conan_version\": conan_version}\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\n text = rtemplate.render(context)\n\n try:\n return self._recurse_load_profile(text, profile_path)\n except ConanException as exc:\n raise ConanException(\"Error reading '%s' profile: %s\" % (profile_name, exc))","def _sloppy_parse_profiles (self, contents):\n profile_start = contents.find('profiles\":')\n profile_list_start = contents.find('profilesList')\n if int(profile_start) > -1 and int(profile_list_start) > -1:\n try:\n try:\n return json.loads('{\"a\":{\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\n except ValueError, e:\n return None\n except TypeError, e:\n return None\n return None","def _load_data(self, filename):\n if not os.path.isfile(filename):\n return False\n\n with open(filename) as f:\n data = pickle.load(f)\n if data:\n self.profiles = data['profiles']\n self.user_data = data['user_data']\n self.api_data = data['api_data']\n else:\n return False","def populate_profiles_from_directory(self):\n\n self.profiles = []\n\n # Go over all the files and create a profile object\n _profile_files = listdir(self.save_dir)\n\n for profile_filename in _profile_files:\n # Only check for .yaml files\n if path.splitext(profile_filename)[1] == '.yaml':\n # Get the data and create a new profile\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\n _profile = Profile(path.splitext(profile_filename)[0])\n _profile.player_data = _file_data\n self.profiles.append(_profile)","def read_plist(path: str) -> dict:\n return _read_plist(path, plistlib.FMT_XML)","def _load_file(self, file_path: str) -> dict:\n raise NotImplementedError()","def load(cls, filepath):\n return BaseProfiler.load(filepath)","def read_profiles(filename):\n profiles = []\n with gzip.open(filename, mode='rt', encoding='utf8') as infile:\n for line in infile:\n profiles.append(Counter(line.split()))\n return profiles","def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\r\n if lastused:\r\n writer.WriteAttributeString(\"LastUsed\", \",\".join(lastused))\r\n for profile in profiles:\r\n profiles[profile].save_to_xml(writer)\r\n writer.WriteEndElement()\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def profiles_path(self) -> Path:\n return self._config.data_path / \"hmm\" / \"profiles.hmm\"","def get_resolved_profile_catalog(trestle_root: pathlib.Path, profile_path: pathlib.Path) -> cat.Catalog:\n logger.debug(f'get resolved profile catalog for {profile_path} via generated Import.')\n import_ = prof.Import(href=str(profile_path), include_all={})\n import_filter = ProfileResolver.Import(trestle_root, import_)\n logger.debug('launch pipeline')\n result = next(import_filter.process())\n return result","def profile_directory_path(request, file):\n return directory_path('profile', file)","def get_profiles(profile_file_directory):\r\n\t\r\n\tprofile_file_path = profile_file_directory+ \"/profiles.txt\"\r\n\tlist_of_all_allele_numbers_tuple = []\r\n\tdatabase = None\r\n\tlocusList = []\r\n\t\r\n\tfor l in open(profile_file_path):\r\n\t if database is None:\r\n\t\tdatabase = {}\r\n\t\tlocusList = l.split()[1:]\r\n\t\tcontinue\r\n\t t = l.split()\r\n\t st = t[0]\r\n\t v = ' '.join([s for s in t[1:]])\r\n\t if v in database:\r\n\t\tprint >> sys.stderr, 'sequence type ' + str(st) + ' is a duplicate of ' + str(database[v])\r\n\t database[v] = st\r\n\t covert_string_to_tuple_list_of_allele_numbers = tuple(int(x) for x in re.findall(\"[0-9]+\", v)) \r\n\t list_of_all_allele_numbers_tuple.append(covert_string_to_tuple_list_of_allele_numbers)\r\n\t\t\r\n\treturn (database, locusList, list_of_all_allele_numbers_tuple)","def list_profiles(self) -> dict:\n wsc = self.read_ws_configuration()\n out = OrderedDict()\n for name, json in wsc.profiles.items():\n out[name] = Profile(name, self.ws_data_folder / name, json)\n # Try to find current profile\n try:\n out[self.current_profile_name].is_current = True\n except Exception:\n pass\n return out","def _read_profile_file(cls, profile_file):\n qid2title: Dict[str, str] = {}\n qid2desc: Dict[str, str] = {}\n alias2qids: Dict[str, list] = {}\n type_systems: Dict[str, Dict[str, List[str]]] = {}\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\n\n num_lines = sum(1 for _ in open(profile_file))\n with open(profile_file, \"r\") as in_f:\n for line in tqdm(in_f, total=num_lines, desc=\"Reading profile\"):\n line = ujson.loads(line)\n\n # Check keys and schema\n assert all(\n k in line.keys() for k in REQUIRED_KEYS\n ), f\"A key from {REQUIRED_KEYS} was not in {line}\"\n try:\n # Asserts the types are correct\n ent = EntityObj(\n entity_id=line[\"entity_id\"],\n mentions=line[\"mentions\"],\n title=line.get(\"title\", line[\"entity_id\"]),\n description=line.get(\"description\", \"\"),\n types=line.get(\"types\", {}),\n relations=line.get(\"relations\", []),\n )\n except ValidationError as e:\n print(e.json())\n raise e\n if ent.entity_id in qid2title:\n raise ValueError(f\"{ent.entity_id} is already in our dump\")\n qid2title[ent.entity_id] = ent.title\n qid2desc[ent.entity_id] = ent.description\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\n for men_pair in ent.mentions:\n # Lower case mentions for mention extraction\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\n if new_men not in alias2qids:\n alias2qids[new_men] = []\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\n # Add type systems of type_sys -> QID -> list of type names\n for type_sys in ent.types:\n if type_sys not in type_systems:\n type_systems[type_sys] = {}\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\n # Add kg relations QID -> relation -> list of object QIDs\n for rel_pair in ent.relations:\n if \"relation\" not in rel_pair or \"object\" not in rel_pair:\n raise ValueError(\n \"For each value in relations, it must be a JSON with keys relation and object\"\n )\n if ent.entity_id not in qid2relations:\n qid2relations[ent.entity_id] = {}\n if rel_pair[\"relation\"] not in qid2relations[ent.entity_id]:\n qid2relations[ent.entity_id][rel_pair[\"relation\"]] = []\n qid2relations[ent.entity_id][rel_pair[\"relation\"]].append(\n rel_pair[\"object\"]\n )\n\n # Sort mentions based on score, highest first\n for al in list(alias2qids.keys()):\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\n # Add all qids to the type systems and KG connections with empty values\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\n for qid in qid2title:\n for type_sys in type_systems:\n if qid not in type_systems[type_sys]:\n type_systems[type_sys][qid] = []\n if qid not in qid2relations:\n qid2relations[qid] = {}\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations","def get_profiles(self):\n # print(self.uir) #checkpoint\n if os.path.isdir(self.uir+\"/profiles\"):\n profiles=os.listdir(self.uir+\"/profiles\")\n # print(profiles) #checkpoint\n for profile in profiles:\n wsadmin=self.uir+\"/profiles/\"+profile+\"/bin/wsadmin.bat\"\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\n self.profiles.append(self.uir+\"/profiles/\"+profile)\n\n else: print(self.uir+' Instance does not have \"profile\" folder in '+self.uir)\n return","def profile(filename: str) -> 'Iterator[None]':\n profiler = Profile()\n profiler.enable()\n\n yield\n\n profiler.disable()\n profiler.create_stats()\n profiler.dump_stats('profiles/{}'.format(filename))","def load_vsp_pairs(path: str) -> dict:\n vsp_pairs = dict()\n with io.open(file=path, mode=\"r\", encoding=\"utf-8\") as src_file:\n for line in src_file:\n content = line.split(':', 1)\n vsp_pairs[tuple(content[0].split(','))] = content[1]\n return vsp_pairs","def loadRatingScoreMappingFromFile(file):\n\treturn \\\n\tcompose(\n\t\tdict\n\t , partial(map, lambda line: ((line[0], line[1]), line[2]))\n\t , partial(takewhile, lambda line: len(line) > 2 and line[0] != '')\n\t , lambda t: t[1]\n\t , lambda lines: (pop(lines), lines)\n\t , fileToLines\n \t , partial(join, getDataDirectory())\n\t)(file)","def read_pardus_profiles(self):\n\n self.lan_config = ConfigParser.ConfigParser()\n self.lan_config.read(self.lan_config_path)\n connection_type = \"802-3-ethernet\"\n for section in self.lan_config.sections():\n lan_settings = {}\n for option in self.lan_config.options(section):\n if option == \"device\":\n #To strip device name from long device string\n lan_settings[option] = self.lan_config.get(section, option).split(\"_\")[-1]\n else:\n lan_settings[option] = self.lan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, lan_settings)\n self.pardus_profiles.append(p)\n\n self.wlan_config = ConfigParser.ConfigParser()\n self.wlan_config.read(self.wlan_config_path)\n connection_type = \"802-11-wireless\"\n for section in self.wlan_config.sections():\n wlan_settings = {}\n for option in self.wlan_config.options(section):\n if option == \"device\":\n wlan_settings[option] = self.wlan_config.get(section, option).split(\"_\")[-1]\n else:\n wlan_settings[option] = self.wlan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\n self.pardus_profiles.append(p)","def load(self):\n settings_path = os.path.join(self.file_path, \"__file_data.json\")\n if os.path.exists( settings_path ):\n self.fileList = simplejson.loads( open( settings_path, 'r' ).read() )\n\n settings_path = os.path.join(self.file_path, \"__user_data.json\")\n if os.path.exists( settings_path ):\n self.userList = simplejson.loads( open( settings_path, 'r' ).read() )","def profiles(self):\n if not self._profiles:\n self.GetAllProfiles()\n return self._profiles","def load(self, config, profile_key):\n # a none numeric argument is supposed to be 'alias'\n if re.search(r\"\\b[0-9]+\\b\", profile_key) is None:\n key = profile_key\n self.alias = key\n access_by = \"alias\"\n else:\n key = (int)(profile_key)\n self.id = key\n access_by = \"id\"\n self.logger.debug(\"==> profile will be access by his '%s'\" % access_by)\n\n # check if the profile exists into config...\n i = 0\n profile_found = False\n while (i < len(config[\"profiles\"])\n and profile_found is False):\n\n if config[\"profiles\"][i][access_by] == key:\n profile_found = True\n self.logger.debug(\"==> profile '%s' found (access by '%s')\" %\n (key, access_by))\n self.config = config[\"profiles\"][i]\n\n i += 1\n\n # ... and if not raise an exception\n if profile_found is False:\n raise ProfileLoadError(\"profile '%s' not found\" % profile_key)\n\n # set profile properties\n self.id = self.config[\"id\"]\n self.alias = self.config[\"alias\"]\n self.state_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".json\")\n self.lock_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".lock\")\n\n # set the todo queue limit based on configuration\n # - default is 1\n try:\n todo_limit_queue = self.config[\"todo_queue_limit\"]\n self.todo_queue_limit = todo_limit_queue\n except KeyError:\n self.todo_queue_limit = 1","def load_file(self, file_path):\n with open(file_path, \"r\") as mappings_file:\n for raw_line in mappings_file:\n line = raw_line.split()\n # Add new record to the records dictionary.\n new_record = Record(line[0], line[1], line[2], line[3])\n self.add_record(new_record)","def read_conf_file(self, file_path):\n ambari_props = {}\n if os.path.exists(file_path):\n with open(file_path, \"r\") as f:\n lines = f.readlines()\n if lines:\n Logger.debug(\"Reading file {0}, has {1} lines.\".format(file_path, len(lines)))\n for l in lines:\n l = l.strip()\n if l.startswith(\"#\"):\n continue\n parts = l.split(\"=\")\n if len(parts) >= 2:\n prop = parts[0]\n value = \"\".join(parts[1:])\n ambari_props[prop] = value\n return ambari_props","def read_pro(path,var_to_plot= None):\n\n # Which variables are you interested in?\n\n if var_to_plot:\n var_codes = ['0500','0501',pro_code_dict(var_to_plot,inverse=True)]\n else:\n var_codes = ['0500', '0501', '0502', '0503', '0506', '0508',\n '0509', '0511', '0512', '0513', '0515',\n '0516', '0521', '0535', '0540', '0541']\n\n # Set up the dictionary to be returned. Dictionary is organised by variable name.\n\n code_dict = pro_code_dict(return_all=True)\n\n variables = {}\n for var in var_codes:\n variables[code_dict[var]] = []\n\n # Open the .PRO file\n\n with open(path, \"r\") as f:\n\n # Iterate line by line\n\n for line in f.readlines():\n\n # If the variable code (first four chars) matches the variable of interest,\n # append that line to the list of lines\n\n if line[:4] in var_codes:\n variables[code_dict[line[:4]]].append(line)\n\n\n # Now remove the header data\n\n for variable in variables.keys():\n\n variables[variable].pop(0)\n\n snowpro_list = [snowpro_from_snapshot(date_index, variables) for date_index in range(len(variables['Date']))]\n\n return (snowpro_list)","def loadPerson(fileid):\n dinf = {}\n drel = {}\n root = etree.Element(\"person\")\n text = None\n # TODO: put this in a global variable, and make a function to populate it from the DTD.\n tags = [\"commonname\", \"ctitle\", \"gname\", \"mname\", \"fname\", \"nname\", \"nameorder\", \"gender\", \"bday\", \"dday\", \"stories\", \"mention\", \"appear1ch\", \"appear1wr\", \"conflict\", \"leadrel\", \"bodytyp\", \"age\", \"skin\", \"eyes\", \"hair\", \"dmarks\", \"dress\", \"attposs\", \"asmell\", \"personality\", \"speech\", \"formocc\", \"currocc\", \"strength\", \"weak\", \"mole\", \"hobby\", \"misc\", \"ethnic\", \"origin\", \"backstory\", \"residence\", \"minchar\", \"talent\", \"abil\", \"sgoal\", \"other\", \"relat\", \"aspects\", \"update\"]\n tags.remove(\"currocc\")\n tags.remove(\"formocc\")\n tags.remove(\"relat\")\n tags.append(\"file\")\n for tag in tags:\n dinf[tag] = [\"\",False]\n dinf['currocc'] = {}\n dinf['currocc']['pos'] = [\"\",False]\n dinf['formocc'] = {}\n dinf['formocc']['pos'] = [\"\",False]\n dinf['aspects'] = {}\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n dinf['formocc']['events'] = events\n if not idExists(fileid):\n status.push(0,\"new person created... '%s'\" % fileid)\n return (dinf,drel)\n fn = os.path.join(config['realmdir'],fileid + \".xml\")\n status.push(0,\"loading person from XML... '%s'\" % fn)\n try:\n with codecs.open(fn,'rU','utf-8') as f:\n tree = etree.parse(f)\n f.close()\n root = tree.getroot()\n except IOError as e:\n print \" Could not open configuration file: %s\" % e\n\n ir = 0\n for i in range(len(root)):\n if root[i].tag is not None:\n if root[i].tag == \"relat\":\n if len(root[i]) > 0:\n node = \"\"\n node = root[i].find(\"file\")\n if node is not None and node.text:\n node = node.text.strip()\n node = common.validateFileid(node)\n drel[node] = {}\n for j in root[i]:\n if j.tag == \"events\":\n if not drel[node].get('events'): drel[node]['events'] = {}\n for k in j:\n stone = str(len(drel[node]['events']))\n drel[node]['events'][stone] = {}\n for m in k:\n if m.tag and m.text:\n drel[node]['events'][stone][m.tag] = [m.text.strip(),False]\n else: # elif j.tag != \"file\":\n if j.tag and j.text:\n drel[node][j.tag] = [j.text.strip(),False]\n if config['debug'] > 3: print drel[node]\n else:\n if config['debug'] > 0:\n print \"Invalid relat tag:\"\n for c in root[i]:\n print c.tag\n else: # no relat length\n if config['debug'] > 0: print \"Empty relat tag.\"\n elif root[i].tag == \"currocc\":\n# print \",\",\n dinf['currocc'] = {}\n try:\n dinf['currocc']['pos'] = [root[i].find(\"pos\").text.strip(),False]\n except AttributeError:\n del dinf['currocc']\n if dinf.get('currocc'):\n events = {}\n if len(root[i]) > 1:\n for j in root[i]:\n if j.tag is not None:\n if j.tag == \"events\":\n for k in j:\n if k.tag == \"mstone\":\n le = str(len(events))\n events[le] = {}\n events[le]['date'] = [\"\",False]\n events[le]['event'] = [\"\",False]\n for m in k:\n if m.tag and m.text:\n events[le][m.tag] = [m.text.strip(),False]\n else:\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n else:\n dinf['currocc'] = {}\n dinf['currocc']['pos'] = [\"\",False]\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n elif root[i].tag == \"formocc\":\n# print \",\",\n dinf['formocc'] = {}\n try:\n dinf['formocc']['pos'] = [root[i].find(\"pos\").text.strip(),False]\n except AttributeError:\n del dinf['formocc']\n if dinf.get('formocc'):\n events = {}\n if len(root[i]) > 1:\n for j in root[i]:\n if j.tag is not None:\n if j.tag == \"events\":\n for k in j:\n if k.tag == \"mstone\":\n le = str(len(events))\n events[le] = {}\n events[le]['date'] = [\"\",False]\n events[le]['event'] = [\"\",False]\n for m in k:\n if m.tag and m.text:\n events[le][m.tag] = [m.text.strip(),False]\n else:\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['formocc']['events'] = events\n else:\n dinf['formocc'] = {}\n dinf['formocc']['pos'] = [\"\",False]\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['formocc']['events'] = events\n elif root[i].tag == \"aspects\":\n if len(root[i]) > 0:\n nodes = root[i]\n for node in nodes:\n k = str(len(dinf['aspects']))\n dinf['aspects'][k] = {}\n if node.tag and node.text:\n dinf['aspects'][k] = [node.text.strip(),False]\n else:\n if config['debug'] > 0:\n print \"Invalid aspects tag:\"\n print node.tag + ': ' + node.text,\n else: # no aspects length\n if config['debug'] > 0: print \"Empty aspects tag.\"\n elif root[i].text is not None:\n# print \".\",\n dinf[root[i].tag] = [root[i].text.strip(), False]\n if config['debug'] > 2: print str(i) + \" \",\n# print str(dinf)\n return (dinf,drel)","def load(self, file=\"setup\", path=\"settings\"):\n\n # check if filename already contains file extension, if not, add it\n if file[-5:] != '.json':\n file += '.json'\n # load mappings from file\n with open(os.path.join(path, file), 'r') as file:\n self.data = json.load(file)","def ppt_file_to_dict(self, file_path):\n try:\n file = open(file_path, \"rb\")\n\n except IOError as e:\n print(e)\n return\n\n pres = Presentation(file)\n file.close()\n\n for i in range(len(pres.slides)):\n self.process_slide(pres.slides[i], i + 1, file_path)","def load(cls, filepath):\n # Load profile from disk\n with open(filepath, \"rb\") as infile:\n data = pickle.load(infile)\n\n # remove profiler class if it exists\n profiler_class = data.pop('profiler_class', None)\n\n # if the user didn't load from the a given profiler class, we need\n # to determine which profiler is being loaded.\n profiler_cls = cls\n if cls is BaseProfiler:\n if profiler_class == 'StructuredProfiler':\n profiler_cls = StructuredProfiler\n elif profiler_class == 'UnstructuredProfiler':\n profiler_cls = UnstructuredProfiler\n elif profiler_class is None: # deprecated case\n profiler_cls = StructuredProfiler\n if '_empty_line_count' in data:\n profiler_cls = UnstructuredProfiler\n else:\n raise ValueError(f'Invalid profiler class {profiler_class} '\n f'failed to load.')\n\n profile_options = profiler_cls._option_class()\n profile_options.data_labeler.is_enabled = False\n profiler = profiler_cls(None, options=profile_options)\n\n for key in data:\n setattr(profiler, key, data[key])\n\n # Restore all data labelers\n profiler._restore_data_labelers()\n return profiler","def loads(file_path: Path) -> tuple[dict[str, Any], list[tuple[int, str]]]:\n\n content = read_file_with_any_encoding(file_path=file_path)\n\n if not all(key in content for key in (\"resource\", \"provider\")):\n return {}, []\n\n file_lines = [(idx + 1, line) for idx, line in enumerate(content.splitlines(keepends=True))]\n\n template: \"dict[str, Any] | list[dict[str, Any]]\" = loader.loads(content=content)\n if not template:\n template = {}\n if isinstance(template, list):\n template = template[0]\n\n if template:\n template = prepare_definition(template)\n\n return template, file_lines","def load_mappings(filepath):\n return pickle.load(open(filepath, \"rb\"))","def load(file):\n\n return RegistrationModel.load(file)","def load_profile(dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"rb\") as f:\n logger = pickle.load(f)\n return logger","def get_settings_from_file(path, default_settings=DEFAULT_CONFIG):\r\n\r\n name, ext = os.path.splitext(os.path.basename(path))\r\n module = load_source(name, path)\r\n return get_settings_from_module(module, default_settings=default_settings)","def from_json(cls, file_path):\n profile = cls()\n with open(file_path, 'r') as fd:\n profile._ngrams = json.load(fd)\n return profile","def read_file(filepath: str) -> dict:\n if not filepath.endswith(\".csv\"):\n raise RuntimeError(\"File extension must be .csv\")\n\n people = {}\n with open(filepath) as csv:\n for line in csv:\n email, person = Parser.parse_line(line.rstrip(\"\\n\"))\n if email not in people:\n people[email] = person\n else:\n print(\"Ignoring person with duplicate email {}\".format(email))\n return people","def load(self, path: str) -> None:\n with open(path, \"r\", encoding=\"utf-8\") as file:\n all_pairs = json.load(file)\n for pair in all_pairs:\n added_pair = StudentPair.from_json(pair)\n self.add_pair(added_pair, True)","def ReadWiredNetworkProfile(self, profilename):\n profile = {}\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n for x in config.options(profilename):\n profile[x] = misc.Noneify(config.get(profilename, x))\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\n self.WiredNetwork = profile\n return \"100: Loaded Profile\"\n else:\n self.WiredNetwork = None\n return \"500: Profile Not Found\"","def load_persistent_settings(path):\n settings = {}\n if path.exists():\n with open(path, \"r\") as f:\n persistent = yaml.load(f)\n if persistent:\n settings.update(persistent)\n return settings","def load_frontend_xml(xml_path):\n frontend_setting = open(xml_path, \"r\").read()\n return frontend_setting","def loadProfilesFromPlist_(self, plist):\r\n \r\n LogInfo(u\"Loading update profiles with PublicationDate %@\", plist[u\"PublicationDate\"])\r\n self.profiles = dict()\r\n for name, updates in plist[u\"Profiles\"].iteritems():\r\n profile = list()\r\n for update in updates:\r\n profile.append(plist[u\"Updates\"][update])\r\n self.profiles[name] = profile\r\n self.publicationDate = plist[u\"PublicationDate\"]\r\n self.updatePaths = dict()\r\n for name, update in plist[u\"Updates\"].iteritems():\r\n filename, ext = os.path.splitext(os.path.basename(update[u\"url\"]))\r\n self.updatePaths[update[u\"sha1\"]] = u\"%s(%s)%s\" % (filename, update[u\"sha1\"][:7], ext)\r\n self.deprecatedInstallerBuilds = dict()\r\n try:\r\n for replacement, builds in plist[u\"DeprecatedInstallers\"].iteritems():\r\n for build in builds:\r\n self.deprecatedInstallerBuilds[build] = replacement\r\n except KeyError:\r\n LogWarning(u\"No deprecated installers\")\r\n if self.delegate:\r\n self.delegate.profilesUpdated()","def parse_info_from_file(path):\n try:\n filename = os.path.split(path)[1]\n filename = os.path.splitext(filename)[0]\n age, gender, race, _ = filename.split('_')\n\n return int(age), dataset_dict['gender_id'][int(gender)], dataset_dict['race_id'][int(race)]\n except Exception as ex:\n return None, None, None","def GetAllProfiles(self):\n profiles = []\n feed_uri = self._gd_client.GetFeedUri('profiles')\n while feed_uri:\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\n profiles.extend(feed.entry)\n feed_uri = feed.FindNextLink()\n self._profiles = profiles","def Load(self, filename):\n logging.info(\"Reading users file at %s\", filename)\n try:\n try:\n contents = utils.ReadFile(filename)\n except EnvironmentError as err:\n self._users = None\n if err.errno == errno.ENOENT:\n logging.warning(\"No users file at %s\", filename)\n else:\n logging.warning(\"Error while reading %s: %s\", filename, err)\n return False\n\n users = http.auth.ParsePasswordFile(contents)\n\n except Exception as err: # pylint: disable=W0703\n # We don't care about the type of exception\n logging.error(\"Error while parsing %s: %s\", filename, err)\n return False\n\n self._users = users\n\n return True","def read_data(file):\n with rasterio.open(file) as f:\n data = f.read(1)\n profile = f.profile\n return data, profile","def fromFile(filename: unicode) -> ghidra.framework.ApplicationProperties:\n ...","def dumpProfiles(profiles):\n with open(userProfilesDir, \"w\") as outfile:\n outfile.writelines(json.dumps(profiles, indent=4))\n outfile.close()","def have_profile_dir(path, maxdepth=3, filename=\"profiles.desc\"):\n\twhile path != \"/\" and maxdepth:\n\t\tif os.path.exists(os.path.join(path, \"profiles\", filename)):\n\t\t\treturn normalize_path(path)\n\t\tpath = normalize_path(path + \"/..\")\n\t\tmaxdepth -= 1","def loadState(self, file):\n if isinstance(file, str):\n with open(file, 'r') as f:\n xml = f.read()\n else:\n xml = file.read()\n self.context.setState(mm.XmlSerializer.deserialize(xml))","def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\n assert isinstance(profiles, list), \"Please provide a list of profiles\"\n cache = ClientCache(self._conan_api.cache_folder)\n loader = ProfileLoader(cache)\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\n profile.conf.validate()\n cache.new_config.validate()\n # Apply the new_config to the profiles the global one, so recipes get it too\n profile.conf.rebase_conf_definition(cache.new_config)\n return profile","def ReadUserMappings(self, file_object):\n # TODO: replace by:\n # config_parser = configparser. ConfigParser(interpolation=None)\n config_parser = configparser.RawConfigParser()\n config_parser.readfp(file_object)\n\n user_mappings = {}\n for option_name in config_parser.options(u'user_mappings'):\n user_mapping = self._GetConfigValue(\n config_parser, u'user_mappings', option_name)\n\n option_name = option_name.lower()\n user_mappings[option_name] = user_mapping.lower()\n\n return user_mappings","def get_user():\n with open(app.config['DATA_XML'], 'r') as xmlfile:\n root = ElementTree.parse(xmlfile).getroot()\n\n for item in root.iter('server'):\n result = '{}://{}'.format(\n item.find('protocol').text,\n item.find('host').text\n )\n\n data = {\n user.attrib['id']: {\n 'name': user.find('name').text,\n 'avatar': '{}{}'.format(\n result,\n user.find('avatar').text\n )\n }\n for user in root.iter('user')\n }\n return OrderedDict(\n sorted(\n data.items(),\n key=lambda result: itemgetter('name')(itemgetter(1)(result)),\n cmp=locale.strcoll\n )\n )","def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\n if not os.path.isfile(path):\n raise ValueError(f'File {path} does not exist')\n\n with open(path, 'rb') as file:\n return plistlib.load(file, fmt=format, dict_type=dict)","def profile_files(profile):\n flist = os.listdir(osp.join(profile, 'startup'))\n profile_path = osp.join(osp.abspath('.'), profile)\n return [osp.join(profile_path, 'startup', x) for x in flist]","def read_users(self, users_path):\n with open(users_path, mode='rb') as f:\n users = pickle.load(f)\n return users","def load_users(filename):\n with open(filename, 'rb') as f:\n for line in f:\n yield line.split(':', 1)[0]","def read_file(file_path):\n\n output_dict = dict()\n try:\n if os.path.exists(file_path):\n with open(file_path) as fd:\n output = fd.readlines()\n for idx in range(len(output)):\n key_info = output[idx].split('=')[0].strip()\n value_info = output[idx].split('=')[1].strip()\n output_dict[key_info] = value_info\n return output_dict\n except Exception as e:\n SysTools.logger.warning(\"Read file:%s failed, reason:%s\" % (file_path, str(e)))","def loadCountryGroupMappingFromFile(file):\n\treturn \\\n\tcompose(\n\t\tdict\n\t , partial(map, lambda line: (line[0], line[2].strip()))\n\t , partial(takewhile, lambda line: len(line) > 2 and line[0] != '')\n\t , lambda t: t[1]\n\t , lambda lines: (pop(lines), lines)\n\t , fileToLines\n \t , partial(join, getDataDirectory())\n\t)(file)","def loadMenuPreferences(path):\n with open(path, 'r') as f:\n return json.load(f)","def get_list(profiles_folder, logger):\n profile_list = []\n with scandir(profiles_folder) as it:\n for entry in it:\n if entry.is_file():\n filepath = profiles_folder + entry.name\n profile = json_from_file(filepath, logger)\n if profile is not None:\n try:\n profile_list.append({\"filepath\":filepath, \"name\":profile[\"name\"], \"description\":profile[\"description\"]})\n except AttributeError:\n logger.error(\"Missing attributes in \" + filepath)\n logger.error(str(profile))\n return profile_list","def read_profile (filename, index):\n \n col_names = ['m', 'r', 'F', 'P', 'rho', 'T', 'u', 's', \n 'c_P', 'gamma_ad', 'nabla_ad', 'mu', 'n_e', \n 'P_e', 'P_rad', 'nabla_rad', 'nabla', 'v_c',\n 'kappa', 'q_nuc', 'q_PP', \n 'q_CNO', 'q_3a', 'q_nunuc',\n 'q_nu', 'q_grav', 'X', 'X_mol',\n 'X_+', 'Y', 'Y_+', 'Y_++', 'X_C', 'X_N', \n 'X_O', 'Psi']\n\n return read_ez_zip(filename, 'structure_{:05d}.txt'.format(index), col_names)","def parse_xml(file: Path):\n check_file(file) # Check the existency of the file\n\n doc = ET.parse(file)\n data = parse_xml_tree(doc.getroot())\n return data","def profiles():\n images = get_uploaded_images()\n records = db.session.query(UserProfile).all()\n return render_template('profiles.html', images=images, records =records)","async def load(self, file: IO) -> dict:","def read_file(fileName):\r\n\r\n xmlFile = open(fileName, 'r', encoding='utf-8')\r\n\r\n xmlContent = xmlFile.read()\r\n dictContent = xmltodict.parse(xmlContent)\r\n\r\n return dictContent","def load_configuration(self, file_path):\n file_path = self._get_existing_path(file_path)\n\n self._bp_session.test_name = self._test_configuration_file_flow.load_configuration(file_path)\n test_model = ElementTree.parse(file_path).getroot().find(\"testmodel\")\n network_name = test_model.get(\"network\")\n interfaces = []\n for interface in test_model.findall(\"interface\"):\n interfaces.append(int(interface.get(\"number\")))\n self._port_reservation_helper.reserve_ports(network_name, interfaces, self._bp_session)","def load(self, file):\n self.namespace['workflow'].configfile(file)\n self.updateNamespace()","def load_file(self, filepath):\n filepath = self._yaml_extension(filepath)\n data = self._load_data_yaml(filepath)\n return data","def load_settings(path):\n if os.path.exists(path):\n comments = lambda s: s and not s.startswith(\"#\")\n settings = filter(comments, open(path, 'r'))\n return dict((k.strip(), v.strip()) for k, _, v in\n [s.partition('=') for s in settings])\n # Handle nonexistent or empty settings file\n return {}","def load_settings(path):\n if os.path.exists(path):\n comments = lambda s: s and not s.startswith(\"#\")\n settings = filter(comments, open(path, 'r'))\n return dict((k.strip(), v.strip()) for k, _, v in\n [s.partition('=') for s in settings])\n # Handle nonexistent or empty settings file\n return {}","def profiles(self):\n return self._profiles","def profiles(self):\n return self._profiles","def load(file):\n _config.load(file)","def load_from_file(self, filepath):\n config_file = open(filepath, \"r\")\n config_dict = {}\n\n # load everything from the file\n line = \"none\"\n while line:\n line = config_file.readline()\n\n # check for lines of only whitespace\n if len(line.strip()) == 0:\n continue\n\n # process this line\n line_list = line.split(\"=\")\n key = line_list[0].strip()\n value = eval(line_list[1].strip())\n\n config_dict[key] = value\n\n # load from the dictionary into the variables\n self.map_width = config_dict[\"map_width\"]\n self.map_height = config_dict[\"map_height\"]\n self.map_resolution = config_dict[\"map_resolution\"]\n self.slam_origin = config_dict[\"slam_origin\"]\n self.slam_rotation = config_dict[\"slam_rotation\"]\n self.slam_uncertainty_cutoff = config_dict[\"slam_uncertainty_cutoff\"]","def profiles(self, profiles):\n\n self._profiles = profiles","def profiles_names(self):\n url = get_url('profiles')\n response = self._get(url)\n raise_on_error(response)\n return response.json()","def get_profile_info(self):\n\n drill_path = str(Path.home())+\"/Documents/ball_e_profiles/drill_profiles/{drill_name}/{drill_name}.csv\".format(\n drill_name=self.drill_name)\n with open(drill_path) as file:\n csv_reader = csv.reader(file, delimiter=',')\n row_count = 0\n info_dict = dict()\n for row in csv_reader:\n if row_count == 0:\n row_count += 1\n else:\n info_dict[row[0]] = [row[1], row[2], row[3]]\n row_count += 1\n\n return info_dict","def read_topics_from_xml(file, topics_dict):\n\n # Read XML file\n xml = read_file(file)\n\n # Pass to HTML\n html = BeautifulSoup(xml, \"html.parser\")\n\n # Get topics\n topics = html.findAll('topic')\n for topic in topics:\n # Get topic number + query and description\n topic_number = int(topic['number'])\n query = [topic.query.getText().strip(),\n topic.description.getText().strip()]\n topics_dict[topic_number]['query'] = query\n\n # Get subtopics\n subtopics = topic.findAll('subtopic')\n topics_dict[topic_number]['subtopics'] = [x.getText().strip() for x in subtopics]\n\n return topics_dict","def load_training_data(file_path):\n return load_data(file_path)","def read_config(profile):\n p = Path(CONFIG_FILE_PATH).expanduser()\n p.parent.mkdir(parents=True, exist_ok=True)\n config = configparser.ConfigParser()\n config.read(str(p))\n\n if config.has_section(profile):\n return dict(config.items(profile))\n else:\n return {}","def load_accounts(path):\n with open(path, 'r') as f:\n acc = [x.strip() for x in f.readlines()]\n return acc","def load_users():\n if path.exists(STORAGE_PATH) is False:\n return {}\n\n with open(STORAGE_PATH, \"rb\") as fp:\n return pickle.load(fp)","def read_ini_file (path):\n # TODO write a code to read data from ini-file to dictionary\n\n\n pass","def get_profiles(self):\n profiles = [['Profile name', 'GUID']]\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\n for row in r.fetchall():\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\n return profiles","def process_file(self, data, filename):\n\n if data:\n data = self.update_province_info(data)\n self.get_province_info(data, filename)"],"string":"[\n \"def import_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n return profiles\",\n \"def load_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n if len(profiles) == 0:\\r\\n #Just in case\\r\\n profiles[\\\"Default\\\"] = Profile()\\r\\n profiles[\\\"Default\\\"].Name = \\\"Default\\\"\\r\\n #Some default templates\\r\\n profiles[\\\"Default\\\"].FileTemplate = \\\"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\\\"\\r\\n profiles[\\\"Default\\\"].FolderTemplate = \\\"{<publisher>}\\\\{<imprint>}\\\\{<series>}{ (<startyear>{ <format>})}\\\"\\r\\n \\r\\n if not lastused:\\r\\n lastused = [profiles.keys()[0]]\\r\\n \\r\\n return profiles, lastused\",\n \"def load(path):\\n\\n parser = ConfigParser()\\n parser.read(str(path))\\n\\n def _get(section, option):\\n try:\\n return parser.get(section, option)\\n except (NoSectionError, NoOptionError):\\n return None\\n\\n profiles = {}\\n\\n for section in parser.sections():\\n profiles[section] = Profile(\\n domain=_get(section, \\\"domain\\\"),\\n protocol=_get(section, \\\"protocol\\\"),\\n client_id=_get(section, \\\"client_id\\\"),\\n client_secret=_get(section, \\\"client_secret\\\"),\\n )\\n\\n return profiles\",\n \"def loadProfiles():\\n with open(userProfilesDir, \\\"r\\\") as infile:\\n profiles = json.loads(\\\"\\\\n\\\".join(infile.readlines()))\\n infile.close()\\n return profiles\",\n \"def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\\n Dict[str, List[str]]]:\\n individuals = [[]]\\n sublist = 0\\n content = profiles_file.readlines()\\n for i in content:\\n if i != '\\\\n':\\n individuals[sublist].append(i)\\n else:\\n sublist += 1\\n individuals.append([])\\n return sort_profile(individuals)\",\n \"def load_people(self, file_path):\\n pass\",\n \"def load_profile(path, profile):\\n profiles = load(path)\\n try:\\n return profiles[profile]\\n except KeyError:\\n return Profile(None, None, None, None)\",\n \"def get_profile(path=\\\"~\\\"):\\n global profiles\\n profile = profiles.get(path,None)\\n if not profile:\\n profile = InitFileConfig(os.path.join(path,\\\".myradioprofile\\\"), {} )\\n profiles[path] = profile\\n return profile\",\n \"def _read_profiles(profile_directory):\\n # Initialize key variables\\n profiles = defaultdict(\\n lambda: defaultdict(lambda: defaultdict()))\\n\\n # Read the yaml files in the profiles directory\\n files = os.listdir(profile_directory)\\n filenames = ['{}{}{}'.format(\\n profile_directory, os.sep, nextfile) for nextfile in files]\\n\\n for _filename in sorted(filenames):\\n # Get rid of excess os.sep separators\\n pathitems = _filename.split(os.sep)\\n filename = os.sep.join(pathitems)\\n\\n # Skip obvious\\n if os.path.isfile(filename) is False:\\n continue\\n if filename.lower().endswith('.yaml') is False:\\n continue\\n\\n with open(filename, 'r') as stream:\\n try:\\n _profiles = yaml.load(stream)['data']\\n except yaml.YAMLError as exc:\\n print(exc)\\n\\n # Create dictionary\\n for item in _profiles:\\n firstname = item['firstname']\\n lastname = item['lastname']\\n height = item['height']\\n weight = item['weight']\\n birthdate = item['birthdate']\\n profiles[lastname][firstname][birthdate] = {\\n 'height': height, 'weight': weight}\\n\\n return profiles\",\n \"def parse_file(file_path):\\n with open(file_path) as f:\\n return XmlPropertyListParser().parse(f)\",\n \"def from_file(cls, file_path, ngram_sizes, profile_len):\\n profile = cls.from_files((file_path, ), ngram_sizes, profile_len)\\n return profile\",\n \"def _recurse_load_profile(self, text, profile_path):\\n try:\\n inherited_profile = Profile()\\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\\n profile_parser = _ProfileParser(text)\\n # Iterate the includes and call recursive to get the profile and variables\\n # from parent profiles\\n for include in profile_parser.includes:\\n # Recursion !!\\n profile = self._load_profile(include, cwd)\\n inherited_profile.compose_profile(profile)\\n\\n # Current profile before update with parents (but parent variables already applied)\\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\\n inherited_profile)\\n return inherited_profile\\n except ConanException:\\n raise\\n except Exception as exc:\\n raise ConanException(\\\"Error parsing the profile text file: %s\\\" % str(exc))\",\n \"def loadProfile(fname):\\n \\n x = np.loadtxt(fname)\\n return x[:,1]\",\n \"def _load_profile(self, profile_name, cwd):\\n\\n profile_path = self.get_profile_path(profile_name, cwd)\\n try:\\n text = load_user_encoded(profile_path)\\n except Exception as e:\\n raise ConanException(f\\\"Cannot load profile:\\\\n{e}\\\")\\n\\n # All profiles will be now rendered with jinja2 as first pass\\n base_path = os.path.dirname(profile_path)\\n file_path = os.path.basename(profile_path)\\n context = {\\\"platform\\\": platform,\\n \\\"os\\\": os,\\n \\\"profile_dir\\\": base_path,\\n \\\"profile_name\\\": file_path,\\n \\\"conan_version\\\": conan_version}\\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\\n text = rtemplate.render(context)\\n\\n try:\\n return self._recurse_load_profile(text, profile_path)\\n except ConanException as exc:\\n raise ConanException(\\\"Error reading '%s' profile: %s\\\" % (profile_name, exc))\",\n \"def _sloppy_parse_profiles (self, contents):\\n profile_start = contents.find('profiles\\\":')\\n profile_list_start = contents.find('profilesList')\\n if int(profile_start) > -1 and int(profile_list_start) > -1:\\n try:\\n try:\\n return json.loads('{\\\"a\\\":{\\\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\\n except ValueError, e:\\n return None\\n except TypeError, e:\\n return None\\n return None\",\n \"def _load_data(self, filename):\\n if not os.path.isfile(filename):\\n return False\\n\\n with open(filename) as f:\\n data = pickle.load(f)\\n if data:\\n self.profiles = data['profiles']\\n self.user_data = data['user_data']\\n self.api_data = data['api_data']\\n else:\\n return False\",\n \"def populate_profiles_from_directory(self):\\n\\n self.profiles = []\\n\\n # Go over all the files and create a profile object\\n _profile_files = listdir(self.save_dir)\\n\\n for profile_filename in _profile_files:\\n # Only check for .yaml files\\n if path.splitext(profile_filename)[1] == '.yaml':\\n # Get the data and create a new profile\\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\\n _profile = Profile(path.splitext(profile_filename)[0])\\n _profile.player_data = _file_data\\n self.profiles.append(_profile)\",\n \"def read_plist(path: str) -> dict:\\n return _read_plist(path, plistlib.FMT_XML)\",\n \"def _load_file(self, file_path: str) -> dict:\\n raise NotImplementedError()\",\n \"def load(cls, filepath):\\n return BaseProfiler.load(filepath)\",\n \"def read_profiles(filename):\\n profiles = []\\n with gzip.open(filename, mode='rt', encoding='utf8') as infile:\\n for line in infile:\\n profiles.append(Counter(line.split()))\\n return profiles\",\n \"def save_profiles(file_path, profiles, lastused=\\\"\\\"):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n writer.WriteStartElement(\\\"Profiles\\\")\\r\\n if lastused:\\r\\n writer.WriteAttributeString(\\\"LastUsed\\\", \\\",\\\".join(lastused))\\r\\n for profile in profiles:\\r\\n profiles[profile].save_to_xml(writer)\\r\\n writer.WriteEndElement()\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def profiles_path(self) -> Path:\\n return self._config.data_path / \\\"hmm\\\" / \\\"profiles.hmm\\\"\",\n \"def get_resolved_profile_catalog(trestle_root: pathlib.Path, profile_path: pathlib.Path) -> cat.Catalog:\\n logger.debug(f'get resolved profile catalog for {profile_path} via generated Import.')\\n import_ = prof.Import(href=str(profile_path), include_all={})\\n import_filter = ProfileResolver.Import(trestle_root, import_)\\n logger.debug('launch pipeline')\\n result = next(import_filter.process())\\n return result\",\n \"def profile_directory_path(request, file):\\n return directory_path('profile', file)\",\n \"def get_profiles(profile_file_directory):\\r\\n\\t\\r\\n\\tprofile_file_path = profile_file_directory+ \\\"/profiles.txt\\\"\\r\\n\\tlist_of_all_allele_numbers_tuple = []\\r\\n\\tdatabase = None\\r\\n\\tlocusList = []\\r\\n\\t\\r\\n\\tfor l in open(profile_file_path):\\r\\n\\t if database is None:\\r\\n\\t\\tdatabase = {}\\r\\n\\t\\tlocusList = l.split()[1:]\\r\\n\\t\\tcontinue\\r\\n\\t t = l.split()\\r\\n\\t st = t[0]\\r\\n\\t v = ' '.join([s for s in t[1:]])\\r\\n\\t if v in database:\\r\\n\\t\\tprint >> sys.stderr, 'sequence type ' + str(st) + ' is a duplicate of ' + str(database[v])\\r\\n\\t database[v] = st\\r\\n\\t covert_string_to_tuple_list_of_allele_numbers = tuple(int(x) for x in re.findall(\\\"[0-9]+\\\", v)) \\r\\n\\t list_of_all_allele_numbers_tuple.append(covert_string_to_tuple_list_of_allele_numbers)\\r\\n\\t\\t\\r\\n\\treturn (database, locusList, list_of_all_allele_numbers_tuple)\",\n \"def list_profiles(self) -> dict:\\n wsc = self.read_ws_configuration()\\n out = OrderedDict()\\n for name, json in wsc.profiles.items():\\n out[name] = Profile(name, self.ws_data_folder / name, json)\\n # Try to find current profile\\n try:\\n out[self.current_profile_name].is_current = True\\n except Exception:\\n pass\\n return out\",\n \"def _read_profile_file(cls, profile_file):\\n qid2title: Dict[str, str] = {}\\n qid2desc: Dict[str, str] = {}\\n alias2qids: Dict[str, list] = {}\\n type_systems: Dict[str, Dict[str, List[str]]] = {}\\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\\n\\n num_lines = sum(1 for _ in open(profile_file))\\n with open(profile_file, \\\"r\\\") as in_f:\\n for line in tqdm(in_f, total=num_lines, desc=\\\"Reading profile\\\"):\\n line = ujson.loads(line)\\n\\n # Check keys and schema\\n assert all(\\n k in line.keys() for k in REQUIRED_KEYS\\n ), f\\\"A key from {REQUIRED_KEYS} was not in {line}\\\"\\n try:\\n # Asserts the types are correct\\n ent = EntityObj(\\n entity_id=line[\\\"entity_id\\\"],\\n mentions=line[\\\"mentions\\\"],\\n title=line.get(\\\"title\\\", line[\\\"entity_id\\\"]),\\n description=line.get(\\\"description\\\", \\\"\\\"),\\n types=line.get(\\\"types\\\", {}),\\n relations=line.get(\\\"relations\\\", []),\\n )\\n except ValidationError as e:\\n print(e.json())\\n raise e\\n if ent.entity_id in qid2title:\\n raise ValueError(f\\\"{ent.entity_id} is already in our dump\\\")\\n qid2title[ent.entity_id] = ent.title\\n qid2desc[ent.entity_id] = ent.description\\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\\n for men_pair in ent.mentions:\\n # Lower case mentions for mention extraction\\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\\n if new_men not in alias2qids:\\n alias2qids[new_men] = []\\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\\n # Add type systems of type_sys -> QID -> list of type names\\n for type_sys in ent.types:\\n if type_sys not in type_systems:\\n type_systems[type_sys] = {}\\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\\n # Add kg relations QID -> relation -> list of object QIDs\\n for rel_pair in ent.relations:\\n if \\\"relation\\\" not in rel_pair or \\\"object\\\" not in rel_pair:\\n raise ValueError(\\n \\\"For each value in relations, it must be a JSON with keys relation and object\\\"\\n )\\n if ent.entity_id not in qid2relations:\\n qid2relations[ent.entity_id] = {}\\n if rel_pair[\\\"relation\\\"] not in qid2relations[ent.entity_id]:\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]] = []\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]].append(\\n rel_pair[\\\"object\\\"]\\n )\\n\\n # Sort mentions based on score, highest first\\n for al in list(alias2qids.keys()):\\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\\n # Add all qids to the type systems and KG connections with empty values\\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\\n for qid in qid2title:\\n for type_sys in type_systems:\\n if qid not in type_systems[type_sys]:\\n type_systems[type_sys][qid] = []\\n if qid not in qid2relations:\\n qid2relations[qid] = {}\\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations\",\n \"def get_profiles(self):\\n # print(self.uir) #checkpoint\\n if os.path.isdir(self.uir+\\\"/profiles\\\"):\\n profiles=os.listdir(self.uir+\\\"/profiles\\\")\\n # print(profiles) #checkpoint\\n for profile in profiles:\\n wsadmin=self.uir+\\\"/profiles/\\\"+profile+\\\"/bin/wsadmin.bat\\\"\\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\\n self.profiles.append(self.uir+\\\"/profiles/\\\"+profile)\\n\\n else: print(self.uir+' Instance does not have \\\"profile\\\" folder in '+self.uir)\\n return\",\n \"def profile(filename: str) -> 'Iterator[None]':\\n profiler = Profile()\\n profiler.enable()\\n\\n yield\\n\\n profiler.disable()\\n profiler.create_stats()\\n profiler.dump_stats('profiles/{}'.format(filename))\",\n \"def load_vsp_pairs(path: str) -> dict:\\n vsp_pairs = dict()\\n with io.open(file=path, mode=\\\"r\\\", encoding=\\\"utf-8\\\") as src_file:\\n for line in src_file:\\n content = line.split(':', 1)\\n vsp_pairs[tuple(content[0].split(','))] = content[1]\\n return vsp_pairs\",\n \"def loadRatingScoreMappingFromFile(file):\\n\\treturn \\\\\\n\\tcompose(\\n\\t\\tdict\\n\\t , partial(map, lambda line: ((line[0], line[1]), line[2]))\\n\\t , partial(takewhile, lambda line: len(line) > 2 and line[0] != '')\\n\\t , lambda t: t[1]\\n\\t , lambda lines: (pop(lines), lines)\\n\\t , fileToLines\\n \\t , partial(join, getDataDirectory())\\n\\t)(file)\",\n \"def read_pardus_profiles(self):\\n\\n self.lan_config = ConfigParser.ConfigParser()\\n self.lan_config.read(self.lan_config_path)\\n connection_type = \\\"802-3-ethernet\\\"\\n for section in self.lan_config.sections():\\n lan_settings = {}\\n for option in self.lan_config.options(section):\\n if option == \\\"device\\\":\\n #To strip device name from long device string\\n lan_settings[option] = self.lan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n lan_settings[option] = self.lan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, lan_settings)\\n self.pardus_profiles.append(p)\\n\\n self.wlan_config = ConfigParser.ConfigParser()\\n self.wlan_config.read(self.wlan_config_path)\\n connection_type = \\\"802-11-wireless\\\"\\n for section in self.wlan_config.sections():\\n wlan_settings = {}\\n for option in self.wlan_config.options(section):\\n if option == \\\"device\\\":\\n wlan_settings[option] = self.wlan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n wlan_settings[option] = self.wlan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\\n self.pardus_profiles.append(p)\",\n \"def load(self):\\n settings_path = os.path.join(self.file_path, \\\"__file_data.json\\\")\\n if os.path.exists( settings_path ):\\n self.fileList = simplejson.loads( open( settings_path, 'r' ).read() )\\n\\n settings_path = os.path.join(self.file_path, \\\"__user_data.json\\\")\\n if os.path.exists( settings_path ):\\n self.userList = simplejson.loads( open( settings_path, 'r' ).read() )\",\n \"def profiles(self):\\n if not self._profiles:\\n self.GetAllProfiles()\\n return self._profiles\",\n \"def load(self, config, profile_key):\\n # a none numeric argument is supposed to be 'alias'\\n if re.search(r\\\"\\\\b[0-9]+\\\\b\\\", profile_key) is None:\\n key = profile_key\\n self.alias = key\\n access_by = \\\"alias\\\"\\n else:\\n key = (int)(profile_key)\\n self.id = key\\n access_by = \\\"id\\\"\\n self.logger.debug(\\\"==> profile will be access by his '%s'\\\" % access_by)\\n\\n # check if the profile exists into config...\\n i = 0\\n profile_found = False\\n while (i < len(config[\\\"profiles\\\"])\\n and profile_found is False):\\n\\n if config[\\\"profiles\\\"][i][access_by] == key:\\n profile_found = True\\n self.logger.debug(\\\"==> profile '%s' found (access by '%s')\\\" %\\n (key, access_by))\\n self.config = config[\\\"profiles\\\"][i]\\n\\n i += 1\\n\\n # ... and if not raise an exception\\n if profile_found is False:\\n raise ProfileLoadError(\\\"profile '%s' not found\\\" % profile_key)\\n\\n # set profile properties\\n self.id = self.config[\\\"id\\\"]\\n self.alias = self.config[\\\"alias\\\"]\\n self.state_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".json\\\")\\n self.lock_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".lock\\\")\\n\\n # set the todo queue limit based on configuration\\n # - default is 1\\n try:\\n todo_limit_queue = self.config[\\\"todo_queue_limit\\\"]\\n self.todo_queue_limit = todo_limit_queue\\n except KeyError:\\n self.todo_queue_limit = 1\",\n \"def load_file(self, file_path):\\n with open(file_path, \\\"r\\\") as mappings_file:\\n for raw_line in mappings_file:\\n line = raw_line.split()\\n # Add new record to the records dictionary.\\n new_record = Record(line[0], line[1], line[2], line[3])\\n self.add_record(new_record)\",\n \"def read_conf_file(self, file_path):\\n ambari_props = {}\\n if os.path.exists(file_path):\\n with open(file_path, \\\"r\\\") as f:\\n lines = f.readlines()\\n if lines:\\n Logger.debug(\\\"Reading file {0}, has {1} lines.\\\".format(file_path, len(lines)))\\n for l in lines:\\n l = l.strip()\\n if l.startswith(\\\"#\\\"):\\n continue\\n parts = l.split(\\\"=\\\")\\n if len(parts) >= 2:\\n prop = parts[0]\\n value = \\\"\\\".join(parts[1:])\\n ambari_props[prop] = value\\n return ambari_props\",\n \"def read_pro(path,var_to_plot= None):\\n\\n # Which variables are you interested in?\\n\\n if var_to_plot:\\n var_codes = ['0500','0501',pro_code_dict(var_to_plot,inverse=True)]\\n else:\\n var_codes = ['0500', '0501', '0502', '0503', '0506', '0508',\\n '0509', '0511', '0512', '0513', '0515',\\n '0516', '0521', '0535', '0540', '0541']\\n\\n # Set up the dictionary to be returned. Dictionary is organised by variable name.\\n\\n code_dict = pro_code_dict(return_all=True)\\n\\n variables = {}\\n for var in var_codes:\\n variables[code_dict[var]] = []\\n\\n # Open the .PRO file\\n\\n with open(path, \\\"r\\\") as f:\\n\\n # Iterate line by line\\n\\n for line in f.readlines():\\n\\n # If the variable code (first four chars) matches the variable of interest,\\n # append that line to the list of lines\\n\\n if line[:4] in var_codes:\\n variables[code_dict[line[:4]]].append(line)\\n\\n\\n # Now remove the header data\\n\\n for variable in variables.keys():\\n\\n variables[variable].pop(0)\\n\\n snowpro_list = [snowpro_from_snapshot(date_index, variables) for date_index in range(len(variables['Date']))]\\n\\n return (snowpro_list)\",\n \"def loadPerson(fileid):\\n dinf = {}\\n drel = {}\\n root = etree.Element(\\\"person\\\")\\n text = None\\n # TODO: put this in a global variable, and make a function to populate it from the DTD.\\n tags = [\\\"commonname\\\", \\\"ctitle\\\", \\\"gname\\\", \\\"mname\\\", \\\"fname\\\", \\\"nname\\\", \\\"nameorder\\\", \\\"gender\\\", \\\"bday\\\", \\\"dday\\\", \\\"stories\\\", \\\"mention\\\", \\\"appear1ch\\\", \\\"appear1wr\\\", \\\"conflict\\\", \\\"leadrel\\\", \\\"bodytyp\\\", \\\"age\\\", \\\"skin\\\", \\\"eyes\\\", \\\"hair\\\", \\\"dmarks\\\", \\\"dress\\\", \\\"attposs\\\", \\\"asmell\\\", \\\"personality\\\", \\\"speech\\\", \\\"formocc\\\", \\\"currocc\\\", \\\"strength\\\", \\\"weak\\\", \\\"mole\\\", \\\"hobby\\\", \\\"misc\\\", \\\"ethnic\\\", \\\"origin\\\", \\\"backstory\\\", \\\"residence\\\", \\\"minchar\\\", \\\"talent\\\", \\\"abil\\\", \\\"sgoal\\\", \\\"other\\\", \\\"relat\\\", \\\"aspects\\\", \\\"update\\\"]\\n tags.remove(\\\"currocc\\\")\\n tags.remove(\\\"formocc\\\")\\n tags.remove(\\\"relat\\\")\\n tags.append(\\\"file\\\")\\n for tag in tags:\\n dinf[tag] = [\\\"\\\",False]\\n dinf['currocc'] = {}\\n dinf['currocc']['pos'] = [\\\"\\\",False]\\n dinf['formocc'] = {}\\n dinf['formocc']['pos'] = [\\\"\\\",False]\\n dinf['aspects'] = {}\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n dinf['formocc']['events'] = events\\n if not idExists(fileid):\\n status.push(0,\\\"new person created... '%s'\\\" % fileid)\\n return (dinf,drel)\\n fn = os.path.join(config['realmdir'],fileid + \\\".xml\\\")\\n status.push(0,\\\"loading person from XML... '%s'\\\" % fn)\\n try:\\n with codecs.open(fn,'rU','utf-8') as f:\\n tree = etree.parse(f)\\n f.close()\\n root = tree.getroot()\\n except IOError as e:\\n print \\\" Could not open configuration file: %s\\\" % e\\n\\n ir = 0\\n for i in range(len(root)):\\n if root[i].tag is not None:\\n if root[i].tag == \\\"relat\\\":\\n if len(root[i]) > 0:\\n node = \\\"\\\"\\n node = root[i].find(\\\"file\\\")\\n if node is not None and node.text:\\n node = node.text.strip()\\n node = common.validateFileid(node)\\n drel[node] = {}\\n for j in root[i]:\\n if j.tag == \\\"events\\\":\\n if not drel[node].get('events'): drel[node]['events'] = {}\\n for k in j:\\n stone = str(len(drel[node]['events']))\\n drel[node]['events'][stone] = {}\\n for m in k:\\n if m.tag and m.text:\\n drel[node]['events'][stone][m.tag] = [m.text.strip(),False]\\n else: # elif j.tag != \\\"file\\\":\\n if j.tag and j.text:\\n drel[node][j.tag] = [j.text.strip(),False]\\n if config['debug'] > 3: print drel[node]\\n else:\\n if config['debug'] > 0:\\n print \\\"Invalid relat tag:\\\"\\n for c in root[i]:\\n print c.tag\\n else: # no relat length\\n if config['debug'] > 0: print \\\"Empty relat tag.\\\"\\n elif root[i].tag == \\\"currocc\\\":\\n# print \\\",\\\",\\n dinf['currocc'] = {}\\n try:\\n dinf['currocc']['pos'] = [root[i].find(\\\"pos\\\").text.strip(),False]\\n except AttributeError:\\n del dinf['currocc']\\n if dinf.get('currocc'):\\n events = {}\\n if len(root[i]) > 1:\\n for j in root[i]:\\n if j.tag is not None:\\n if j.tag == \\\"events\\\":\\n for k in j:\\n if k.tag == \\\"mstone\\\":\\n le = str(len(events))\\n events[le] = {}\\n events[le]['date'] = [\\\"\\\",False]\\n events[le]['event'] = [\\\"\\\",False]\\n for m in k:\\n if m.tag and m.text:\\n events[le][m.tag] = [m.text.strip(),False]\\n else:\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n else:\\n dinf['currocc'] = {}\\n dinf['currocc']['pos'] = [\\\"\\\",False]\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n elif root[i].tag == \\\"formocc\\\":\\n# print \\\",\\\",\\n dinf['formocc'] = {}\\n try:\\n dinf['formocc']['pos'] = [root[i].find(\\\"pos\\\").text.strip(),False]\\n except AttributeError:\\n del dinf['formocc']\\n if dinf.get('formocc'):\\n events = {}\\n if len(root[i]) > 1:\\n for j in root[i]:\\n if j.tag is not None:\\n if j.tag == \\\"events\\\":\\n for k in j:\\n if k.tag == \\\"mstone\\\":\\n le = str(len(events))\\n events[le] = {}\\n events[le]['date'] = [\\\"\\\",False]\\n events[le]['event'] = [\\\"\\\",False]\\n for m in k:\\n if m.tag and m.text:\\n events[le][m.tag] = [m.text.strip(),False]\\n else:\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['formocc']['events'] = events\\n else:\\n dinf['formocc'] = {}\\n dinf['formocc']['pos'] = [\\\"\\\",False]\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['formocc']['events'] = events\\n elif root[i].tag == \\\"aspects\\\":\\n if len(root[i]) > 0:\\n nodes = root[i]\\n for node in nodes:\\n k = str(len(dinf['aspects']))\\n dinf['aspects'][k] = {}\\n if node.tag and node.text:\\n dinf['aspects'][k] = [node.text.strip(),False]\\n else:\\n if config['debug'] > 0:\\n print \\\"Invalid aspects tag:\\\"\\n print node.tag + ': ' + node.text,\\n else: # no aspects length\\n if config['debug'] > 0: print \\\"Empty aspects tag.\\\"\\n elif root[i].text is not None:\\n# print \\\".\\\",\\n dinf[root[i].tag] = [root[i].text.strip(), False]\\n if config['debug'] > 2: print str(i) + \\\" \\\",\\n# print str(dinf)\\n return (dinf,drel)\",\n \"def load(self, file=\\\"setup\\\", path=\\\"settings\\\"):\\n\\n # check if filename already contains file extension, if not, add it\\n if file[-5:] != '.json':\\n file += '.json'\\n # load mappings from file\\n with open(os.path.join(path, file), 'r') as file:\\n self.data = json.load(file)\",\n \"def ppt_file_to_dict(self, file_path):\\n try:\\n file = open(file_path, \\\"rb\\\")\\n\\n except IOError as e:\\n print(e)\\n return\\n\\n pres = Presentation(file)\\n file.close()\\n\\n for i in range(len(pres.slides)):\\n self.process_slide(pres.slides[i], i + 1, file_path)\",\n \"def load(cls, filepath):\\n # Load profile from disk\\n with open(filepath, \\\"rb\\\") as infile:\\n data = pickle.load(infile)\\n\\n # remove profiler class if it exists\\n profiler_class = data.pop('profiler_class', None)\\n\\n # if the user didn't load from the a given profiler class, we need\\n # to determine which profiler is being loaded.\\n profiler_cls = cls\\n if cls is BaseProfiler:\\n if profiler_class == 'StructuredProfiler':\\n profiler_cls = StructuredProfiler\\n elif profiler_class == 'UnstructuredProfiler':\\n profiler_cls = UnstructuredProfiler\\n elif profiler_class is None: # deprecated case\\n profiler_cls = StructuredProfiler\\n if '_empty_line_count' in data:\\n profiler_cls = UnstructuredProfiler\\n else:\\n raise ValueError(f'Invalid profiler class {profiler_class} '\\n f'failed to load.')\\n\\n profile_options = profiler_cls._option_class()\\n profile_options.data_labeler.is_enabled = False\\n profiler = profiler_cls(None, options=profile_options)\\n\\n for key in data:\\n setattr(profiler, key, data[key])\\n\\n # Restore all data labelers\\n profiler._restore_data_labelers()\\n return profiler\",\n \"def loads(file_path: Path) -> tuple[dict[str, Any], list[tuple[int, str]]]:\\n\\n content = read_file_with_any_encoding(file_path=file_path)\\n\\n if not all(key in content for key in (\\\"resource\\\", \\\"provider\\\")):\\n return {}, []\\n\\n file_lines = [(idx + 1, line) for idx, line in enumerate(content.splitlines(keepends=True))]\\n\\n template: \\\"dict[str, Any] | list[dict[str, Any]]\\\" = loader.loads(content=content)\\n if not template:\\n template = {}\\n if isinstance(template, list):\\n template = template[0]\\n\\n if template:\\n template = prepare_definition(template)\\n\\n return template, file_lines\",\n \"def load_mappings(filepath):\\n return pickle.load(open(filepath, \\\"rb\\\"))\",\n \"def load(file):\\n\\n return RegistrationModel.load(file)\",\n \"def load_profile(dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"rb\\\") as f:\\n logger = pickle.load(f)\\n return logger\",\n \"def get_settings_from_file(path, default_settings=DEFAULT_CONFIG):\\r\\n\\r\\n name, ext = os.path.splitext(os.path.basename(path))\\r\\n module = load_source(name, path)\\r\\n return get_settings_from_module(module, default_settings=default_settings)\",\n \"def from_json(cls, file_path):\\n profile = cls()\\n with open(file_path, 'r') as fd:\\n profile._ngrams = json.load(fd)\\n return profile\",\n \"def read_file(filepath: str) -> dict:\\n if not filepath.endswith(\\\".csv\\\"):\\n raise RuntimeError(\\\"File extension must be .csv\\\")\\n\\n people = {}\\n with open(filepath) as csv:\\n for line in csv:\\n email, person = Parser.parse_line(line.rstrip(\\\"\\\\n\\\"))\\n if email not in people:\\n people[email] = person\\n else:\\n print(\\\"Ignoring person with duplicate email {}\\\".format(email))\\n return people\",\n \"def load(self, path: str) -> None:\\n with open(path, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n all_pairs = json.load(file)\\n for pair in all_pairs:\\n added_pair = StudentPair.from_json(pair)\\n self.add_pair(added_pair, True)\",\n \"def ReadWiredNetworkProfile(self, profilename):\\n profile = {}\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n for x in config.options(profilename):\\n profile[x] = misc.Noneify(config.get(profilename, x))\\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\\n self.WiredNetwork = profile\\n return \\\"100: Loaded Profile\\\"\\n else:\\n self.WiredNetwork = None\\n return \\\"500: Profile Not Found\\\"\",\n \"def load_persistent_settings(path):\\n settings = {}\\n if path.exists():\\n with open(path, \\\"r\\\") as f:\\n persistent = yaml.load(f)\\n if persistent:\\n settings.update(persistent)\\n return settings\",\n \"def load_frontend_xml(xml_path):\\n frontend_setting = open(xml_path, \\\"r\\\").read()\\n return frontend_setting\",\n \"def loadProfilesFromPlist_(self, plist):\\r\\n \\r\\n LogInfo(u\\\"Loading update profiles with PublicationDate %@\\\", plist[u\\\"PublicationDate\\\"])\\r\\n self.profiles = dict()\\r\\n for name, updates in plist[u\\\"Profiles\\\"].iteritems():\\r\\n profile = list()\\r\\n for update in updates:\\r\\n profile.append(plist[u\\\"Updates\\\"][update])\\r\\n self.profiles[name] = profile\\r\\n self.publicationDate = plist[u\\\"PublicationDate\\\"]\\r\\n self.updatePaths = dict()\\r\\n for name, update in plist[u\\\"Updates\\\"].iteritems():\\r\\n filename, ext = os.path.splitext(os.path.basename(update[u\\\"url\\\"]))\\r\\n self.updatePaths[update[u\\\"sha1\\\"]] = u\\\"%s(%s)%s\\\" % (filename, update[u\\\"sha1\\\"][:7], ext)\\r\\n self.deprecatedInstallerBuilds = dict()\\r\\n try:\\r\\n for replacement, builds in plist[u\\\"DeprecatedInstallers\\\"].iteritems():\\r\\n for build in builds:\\r\\n self.deprecatedInstallerBuilds[build] = replacement\\r\\n except KeyError:\\r\\n LogWarning(u\\\"No deprecated installers\\\")\\r\\n if self.delegate:\\r\\n self.delegate.profilesUpdated()\",\n \"def parse_info_from_file(path):\\n try:\\n filename = os.path.split(path)[1]\\n filename = os.path.splitext(filename)[0]\\n age, gender, race, _ = filename.split('_')\\n\\n return int(age), dataset_dict['gender_id'][int(gender)], dataset_dict['race_id'][int(race)]\\n except Exception as ex:\\n return None, None, None\",\n \"def GetAllProfiles(self):\\n profiles = []\\n feed_uri = self._gd_client.GetFeedUri('profiles')\\n while feed_uri:\\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\\n profiles.extend(feed.entry)\\n feed_uri = feed.FindNextLink()\\n self._profiles = profiles\",\n \"def Load(self, filename):\\n logging.info(\\\"Reading users file at %s\\\", filename)\\n try:\\n try:\\n contents = utils.ReadFile(filename)\\n except EnvironmentError as err:\\n self._users = None\\n if err.errno == errno.ENOENT:\\n logging.warning(\\\"No users file at %s\\\", filename)\\n else:\\n logging.warning(\\\"Error while reading %s: %s\\\", filename, err)\\n return False\\n\\n users = http.auth.ParsePasswordFile(contents)\\n\\n except Exception as err: # pylint: disable=W0703\\n # We don't care about the type of exception\\n logging.error(\\\"Error while parsing %s: %s\\\", filename, err)\\n return False\\n\\n self._users = users\\n\\n return True\",\n \"def read_data(file):\\n with rasterio.open(file) as f:\\n data = f.read(1)\\n profile = f.profile\\n return data, profile\",\n \"def fromFile(filename: unicode) -> ghidra.framework.ApplicationProperties:\\n ...\",\n \"def dumpProfiles(profiles):\\n with open(userProfilesDir, \\\"w\\\") as outfile:\\n outfile.writelines(json.dumps(profiles, indent=4))\\n outfile.close()\",\n \"def have_profile_dir(path, maxdepth=3, filename=\\\"profiles.desc\\\"):\\n\\twhile path != \\\"/\\\" and maxdepth:\\n\\t\\tif os.path.exists(os.path.join(path, \\\"profiles\\\", filename)):\\n\\t\\t\\treturn normalize_path(path)\\n\\t\\tpath = normalize_path(path + \\\"/..\\\")\\n\\t\\tmaxdepth -= 1\",\n \"def loadState(self, file):\\n if isinstance(file, str):\\n with open(file, 'r') as f:\\n xml = f.read()\\n else:\\n xml = file.read()\\n self.context.setState(mm.XmlSerializer.deserialize(xml))\",\n \"def save_profile(file_path, profile):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n profile.save_to_xml(writer)\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\\n assert isinstance(profiles, list), \\\"Please provide a list of profiles\\\"\\n cache = ClientCache(self._conan_api.cache_folder)\\n loader = ProfileLoader(cache)\\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\\n profile.conf.validate()\\n cache.new_config.validate()\\n # Apply the new_config to the profiles the global one, so recipes get it too\\n profile.conf.rebase_conf_definition(cache.new_config)\\n return profile\",\n \"def ReadUserMappings(self, file_object):\\n # TODO: replace by:\\n # config_parser = configparser. ConfigParser(interpolation=None)\\n config_parser = configparser.RawConfigParser()\\n config_parser.readfp(file_object)\\n\\n user_mappings = {}\\n for option_name in config_parser.options(u'user_mappings'):\\n user_mapping = self._GetConfigValue(\\n config_parser, u'user_mappings', option_name)\\n\\n option_name = option_name.lower()\\n user_mappings[option_name] = user_mapping.lower()\\n\\n return user_mappings\",\n \"def get_user():\\n with open(app.config['DATA_XML'], 'r') as xmlfile:\\n root = ElementTree.parse(xmlfile).getroot()\\n\\n for item in root.iter('server'):\\n result = '{}://{}'.format(\\n item.find('protocol').text,\\n item.find('host').text\\n )\\n\\n data = {\\n user.attrib['id']: {\\n 'name': user.find('name').text,\\n 'avatar': '{}{}'.format(\\n result,\\n user.find('avatar').text\\n )\\n }\\n for user in root.iter('user')\\n }\\n return OrderedDict(\\n sorted(\\n data.items(),\\n key=lambda result: itemgetter('name')(itemgetter(1)(result)),\\n cmp=locale.strcoll\\n )\\n )\",\n \"def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\\n if not os.path.isfile(path):\\n raise ValueError(f'File {path} does not exist')\\n\\n with open(path, 'rb') as file:\\n return plistlib.load(file, fmt=format, dict_type=dict)\",\n \"def profile_files(profile):\\n flist = os.listdir(osp.join(profile, 'startup'))\\n profile_path = osp.join(osp.abspath('.'), profile)\\n return [osp.join(profile_path, 'startup', x) for x in flist]\",\n \"def read_users(self, users_path):\\n with open(users_path, mode='rb') as f:\\n users = pickle.load(f)\\n return users\",\n \"def load_users(filename):\\n with open(filename, 'rb') as f:\\n for line in f:\\n yield line.split(':', 1)[0]\",\n \"def read_file(file_path):\\n\\n output_dict = dict()\\n try:\\n if os.path.exists(file_path):\\n with open(file_path) as fd:\\n output = fd.readlines()\\n for idx in range(len(output)):\\n key_info = output[idx].split('=')[0].strip()\\n value_info = output[idx].split('=')[1].strip()\\n output_dict[key_info] = value_info\\n return output_dict\\n except Exception as e:\\n SysTools.logger.warning(\\\"Read file:%s failed, reason:%s\\\" % (file_path, str(e)))\",\n \"def loadCountryGroupMappingFromFile(file):\\n\\treturn \\\\\\n\\tcompose(\\n\\t\\tdict\\n\\t , partial(map, lambda line: (line[0], line[2].strip()))\\n\\t , partial(takewhile, lambda line: len(line) > 2 and line[0] != '')\\n\\t , lambda t: t[1]\\n\\t , lambda lines: (pop(lines), lines)\\n\\t , fileToLines\\n \\t , partial(join, getDataDirectory())\\n\\t)(file)\",\n \"def loadMenuPreferences(path):\\n with open(path, 'r') as f:\\n return json.load(f)\",\n \"def get_list(profiles_folder, logger):\\n profile_list = []\\n with scandir(profiles_folder) as it:\\n for entry in it:\\n if entry.is_file():\\n filepath = profiles_folder + entry.name\\n profile = json_from_file(filepath, logger)\\n if profile is not None:\\n try:\\n profile_list.append({\\\"filepath\\\":filepath, \\\"name\\\":profile[\\\"name\\\"], \\\"description\\\":profile[\\\"description\\\"]})\\n except AttributeError:\\n logger.error(\\\"Missing attributes in \\\" + filepath)\\n logger.error(str(profile))\\n return profile_list\",\n \"def read_profile (filename, index):\\n \\n col_names = ['m', 'r', 'F', 'P', 'rho', 'T', 'u', 's', \\n 'c_P', 'gamma_ad', 'nabla_ad', 'mu', 'n_e', \\n 'P_e', 'P_rad', 'nabla_rad', 'nabla', 'v_c',\\n 'kappa', 'q_nuc', 'q_PP', \\n 'q_CNO', 'q_3a', 'q_nunuc',\\n 'q_nu', 'q_grav', 'X', 'X_mol',\\n 'X_+', 'Y', 'Y_+', 'Y_++', 'X_C', 'X_N', \\n 'X_O', 'Psi']\\n\\n return read_ez_zip(filename, 'structure_{:05d}.txt'.format(index), col_names)\",\n \"def parse_xml(file: Path):\\n check_file(file) # Check the existency of the file\\n\\n doc = ET.parse(file)\\n data = parse_xml_tree(doc.getroot())\\n return data\",\n \"def profiles():\\n images = get_uploaded_images()\\n records = db.session.query(UserProfile).all()\\n return render_template('profiles.html', images=images, records =records)\",\n \"async def load(self, file: IO) -> dict:\",\n \"def read_file(fileName):\\r\\n\\r\\n xmlFile = open(fileName, 'r', encoding='utf-8')\\r\\n\\r\\n xmlContent = xmlFile.read()\\r\\n dictContent = xmltodict.parse(xmlContent)\\r\\n\\r\\n return dictContent\",\n \"def load_configuration(self, file_path):\\n file_path = self._get_existing_path(file_path)\\n\\n self._bp_session.test_name = self._test_configuration_file_flow.load_configuration(file_path)\\n test_model = ElementTree.parse(file_path).getroot().find(\\\"testmodel\\\")\\n network_name = test_model.get(\\\"network\\\")\\n interfaces = []\\n for interface in test_model.findall(\\\"interface\\\"):\\n interfaces.append(int(interface.get(\\\"number\\\")))\\n self._port_reservation_helper.reserve_ports(network_name, interfaces, self._bp_session)\",\n \"def load(self, file):\\n self.namespace['workflow'].configfile(file)\\n self.updateNamespace()\",\n \"def load_file(self, filepath):\\n filepath = self._yaml_extension(filepath)\\n data = self._load_data_yaml(filepath)\\n return data\",\n \"def load_settings(path):\\n if os.path.exists(path):\\n comments = lambda s: s and not s.startswith(\\\"#\\\")\\n settings = filter(comments, open(path, 'r'))\\n return dict((k.strip(), v.strip()) for k, _, v in\\n [s.partition('=') for s in settings])\\n # Handle nonexistent or empty settings file\\n return {}\",\n \"def load_settings(path):\\n if os.path.exists(path):\\n comments = lambda s: s and not s.startswith(\\\"#\\\")\\n settings = filter(comments, open(path, 'r'))\\n return dict((k.strip(), v.strip()) for k, _, v in\\n [s.partition('=') for s in settings])\\n # Handle nonexistent or empty settings file\\n return {}\",\n \"def profiles(self):\\n return self._profiles\",\n \"def profiles(self):\\n return self._profiles\",\n \"def load(file):\\n _config.load(file)\",\n \"def load_from_file(self, filepath):\\n config_file = open(filepath, \\\"r\\\")\\n config_dict = {}\\n\\n # load everything from the file\\n line = \\\"none\\\"\\n while line:\\n line = config_file.readline()\\n\\n # check for lines of only whitespace\\n if len(line.strip()) == 0:\\n continue\\n\\n # process this line\\n line_list = line.split(\\\"=\\\")\\n key = line_list[0].strip()\\n value = eval(line_list[1].strip())\\n\\n config_dict[key] = value\\n\\n # load from the dictionary into the variables\\n self.map_width = config_dict[\\\"map_width\\\"]\\n self.map_height = config_dict[\\\"map_height\\\"]\\n self.map_resolution = config_dict[\\\"map_resolution\\\"]\\n self.slam_origin = config_dict[\\\"slam_origin\\\"]\\n self.slam_rotation = config_dict[\\\"slam_rotation\\\"]\\n self.slam_uncertainty_cutoff = config_dict[\\\"slam_uncertainty_cutoff\\\"]\",\n \"def profiles(self, profiles):\\n\\n self._profiles = profiles\",\n \"def profiles_names(self):\\n url = get_url('profiles')\\n response = self._get(url)\\n raise_on_error(response)\\n return response.json()\",\n \"def get_profile_info(self):\\n\\n drill_path = str(Path.home())+\\\"/Documents/ball_e_profiles/drill_profiles/{drill_name}/{drill_name}.csv\\\".format(\\n drill_name=self.drill_name)\\n with open(drill_path) as file:\\n csv_reader = csv.reader(file, delimiter=',')\\n row_count = 0\\n info_dict = dict()\\n for row in csv_reader:\\n if row_count == 0:\\n row_count += 1\\n else:\\n info_dict[row[0]] = [row[1], row[2], row[3]]\\n row_count += 1\\n\\n return info_dict\",\n \"def read_topics_from_xml(file, topics_dict):\\n\\n # Read XML file\\n xml = read_file(file)\\n\\n # Pass to HTML\\n html = BeautifulSoup(xml, \\\"html.parser\\\")\\n\\n # Get topics\\n topics = html.findAll('topic')\\n for topic in topics:\\n # Get topic number + query and description\\n topic_number = int(topic['number'])\\n query = [topic.query.getText().strip(),\\n topic.description.getText().strip()]\\n topics_dict[topic_number]['query'] = query\\n\\n # Get subtopics\\n subtopics = topic.findAll('subtopic')\\n topics_dict[topic_number]['subtopics'] = [x.getText().strip() for x in subtopics]\\n\\n return topics_dict\",\n \"def load_training_data(file_path):\\n return load_data(file_path)\",\n \"def read_config(profile):\\n p = Path(CONFIG_FILE_PATH).expanduser()\\n p.parent.mkdir(parents=True, exist_ok=True)\\n config = configparser.ConfigParser()\\n config.read(str(p))\\n\\n if config.has_section(profile):\\n return dict(config.items(profile))\\n else:\\n return {}\",\n \"def load_accounts(path):\\n with open(path, 'r') as f:\\n acc = [x.strip() for x in f.readlines()]\\n return acc\",\n \"def load_users():\\n if path.exists(STORAGE_PATH) is False:\\n return {}\\n\\n with open(STORAGE_PATH, \\\"rb\\\") as fp:\\n return pickle.load(fp)\",\n \"def read_ini_file (path):\\n # TODO write a code to read data from ini-file to dictionary\\n\\n\\n pass\",\n \"def get_profiles(self):\\n profiles = [['Profile name', 'GUID']]\\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\\n for row in r.fetchall():\\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\\n return profiles\",\n \"def process_file(self, data, filename):\\n\\n if data:\\n data = self.update_province_info(data)\\n self.get_province_info(data, filename)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7816373","0.7494791","0.69231015","0.68052244","0.669596","0.6337234","0.6331917","0.60487854","0.6018389","0.59737855","0.59296596","0.59177107","0.57330143","0.57177866","0.5666612","0.5610871","0.56013894","0.5478734","0.54241526","0.5402424","0.5382448","0.5379713","0.5368854","0.5361319","0.5332461","0.53192705","0.53029263","0.529909","0.5262802","0.525197","0.51875424","0.5181123","0.5166576","0.5142717","0.51234096","0.5121685","0.51184237","0.5101238","0.510064","0.5092188","0.509217","0.50873077","0.5085553","0.5078232","0.5072762","0.50721216","0.5060621","0.5045805","0.5041749","0.50317836","0.5028443","0.50168794","0.50121933","0.50055915","0.50039035","0.49864826","0.49857017","0.4984214","0.49773276","0.496931","0.49614716","0.4952588","0.49523047","0.4948046","0.49463344","0.49281803","0.49281737","0.49217668","0.4920208","0.49155694","0.4915406","0.49140036","0.49108446","0.49107376","0.49010536","0.49002331","0.48948693","0.48819223","0.48816067","0.48796827","0.4876822","0.48704118","0.4865728","0.4863388","0.4863388","0.48459506","0.48459506","0.48456955","0.4841303","0.4829846","0.48217458","0.4821577","0.4820395","0.48196992","0.4815083","0.4813963","0.48095676","0.48027885","0.47885355","0.47814447"],"string":"[\n \"0.7816373\",\n \"0.7494791\",\n \"0.69231015\",\n \"0.68052244\",\n \"0.669596\",\n \"0.6337234\",\n \"0.6331917\",\n \"0.60487854\",\n \"0.6018389\",\n \"0.59737855\",\n \"0.59296596\",\n \"0.59177107\",\n \"0.57330143\",\n \"0.57177866\",\n \"0.5666612\",\n \"0.5610871\",\n \"0.56013894\",\n \"0.5478734\",\n \"0.54241526\",\n \"0.5402424\",\n \"0.5382448\",\n \"0.5379713\",\n \"0.5368854\",\n \"0.5361319\",\n \"0.5332461\",\n \"0.53192705\",\n \"0.53029263\",\n \"0.529909\",\n \"0.5262802\",\n \"0.525197\",\n \"0.51875424\",\n \"0.5181123\",\n \"0.5166576\",\n \"0.5142717\",\n \"0.51234096\",\n \"0.5121685\",\n \"0.51184237\",\n \"0.5101238\",\n \"0.510064\",\n \"0.5092188\",\n \"0.509217\",\n \"0.50873077\",\n \"0.5085553\",\n \"0.5078232\",\n \"0.5072762\",\n \"0.50721216\",\n \"0.5060621\",\n \"0.5045805\",\n \"0.5041749\",\n \"0.50317836\",\n \"0.5028443\",\n \"0.50168794\",\n \"0.50121933\",\n \"0.50055915\",\n \"0.50039035\",\n \"0.49864826\",\n \"0.49857017\",\n \"0.4984214\",\n \"0.49773276\",\n \"0.496931\",\n \"0.49614716\",\n \"0.4952588\",\n \"0.49523047\",\n \"0.4948046\",\n \"0.49463344\",\n \"0.49281803\",\n \"0.49281737\",\n \"0.49217668\",\n \"0.4920208\",\n \"0.49155694\",\n \"0.4915406\",\n \"0.49140036\",\n \"0.49108446\",\n \"0.49107376\",\n \"0.49010536\",\n \"0.49002331\",\n \"0.48948693\",\n \"0.48819223\",\n \"0.48816067\",\n \"0.48796827\",\n \"0.4876822\",\n \"0.48704118\",\n \"0.4865728\",\n \"0.4863388\",\n \"0.4863388\",\n \"0.48459506\",\n \"0.48459506\",\n \"0.48456955\",\n \"0.4841303\",\n \"0.4829846\",\n \"0.48217458\",\n \"0.4821577\",\n \"0.4820395\",\n \"0.48196992\",\n \"0.4815083\",\n \"0.4813963\",\n \"0.48095676\",\n \"0.48027885\",\n \"0.47885355\",\n \"0.47814447\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.755472"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11591,"cells":{"query":{"kind":"string","value":"Load profiles from a xml file. If no profiles are found it returns an empty dict. file_path>The absolute path to the profile file Returns a dict of the found profiles."},"document":{"kind":"string","value":"def import_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n return profiles"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused","def load_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n if len(profiles) == 0:\r\n #Just in case\r\n profiles[\"Default\"] = Profile()\r\n profiles[\"Default\"].Name = \"Default\"\r\n #Some default templates\r\n profiles[\"Default\"].FileTemplate = \"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\"\r\n profiles[\"Default\"].FolderTemplate = \"{<publisher>}\\{<imprint>}\\{<series>}{ (<startyear>{ <format>})}\"\r\n \r\n if not lastused:\r\n lastused = [profiles.keys()[0]]\r\n \r\n return profiles, lastused","def load(path):\n\n parser = ConfigParser()\n parser.read(str(path))\n\n def _get(section, option):\n try:\n return parser.get(section, option)\n except (NoSectionError, NoOptionError):\n return None\n\n profiles = {}\n\n for section in parser.sections():\n profiles[section] = Profile(\n domain=_get(section, \"domain\"),\n protocol=_get(section, \"protocol\"),\n client_id=_get(section, \"client_id\"),\n client_secret=_get(section, \"client_secret\"),\n )\n\n return profiles","def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\n Dict[str, List[str]]]:\n individuals = [[]]\n sublist = 0\n content = profiles_file.readlines()\n for i in content:\n if i != '\\n':\n individuals[sublist].append(i)\n else:\n sublist += 1\n individuals.append([])\n return sort_profile(individuals)","def loadProfiles():\n with open(userProfilesDir, \"r\") as infile:\n profiles = json.loads(\"\\n\".join(infile.readlines()))\n infile.close()\n return profiles","def _read_profiles(profile_directory):\n # Initialize key variables\n profiles = defaultdict(\n lambda: defaultdict(lambda: defaultdict()))\n\n # Read the yaml files in the profiles directory\n files = os.listdir(profile_directory)\n filenames = ['{}{}{}'.format(\n profile_directory, os.sep, nextfile) for nextfile in files]\n\n for _filename in sorted(filenames):\n # Get rid of excess os.sep separators\n pathitems = _filename.split(os.sep)\n filename = os.sep.join(pathitems)\n\n # Skip obvious\n if os.path.isfile(filename) is False:\n continue\n if filename.lower().endswith('.yaml') is False:\n continue\n\n with open(filename, 'r') as stream:\n try:\n _profiles = yaml.load(stream)['data']\n except yaml.YAMLError as exc:\n print(exc)\n\n # Create dictionary\n for item in _profiles:\n firstname = item['firstname']\n lastname = item['lastname']\n height = item['height']\n weight = item['weight']\n birthdate = item['birthdate']\n profiles[lastname][firstname][birthdate] = {\n 'height': height, 'weight': weight}\n\n return profiles","def load_profile(path, profile):\n profiles = load(path)\n try:\n return profiles[profile]\n except KeyError:\n return Profile(None, None, None, None)","def get_profile(path=\"~\"):\n global profiles\n profile = profiles.get(path,None)\n if not profile:\n profile = InitFileConfig(os.path.join(path,\".myradioprofile\"), {} )\n profiles[path] = profile\n return profile","def _recurse_load_profile(self, text, profile_path):\n try:\n inherited_profile = Profile()\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\n profile_parser = _ProfileParser(text)\n # Iterate the includes and call recursive to get the profile and variables\n # from parent profiles\n for include in profile_parser.includes:\n # Recursion !!\n profile = self._load_profile(include, cwd)\n inherited_profile.compose_profile(profile)\n\n # Current profile before update with parents (but parent variables already applied)\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\n inherited_profile)\n return inherited_profile\n except ConanException:\n raise\n except Exception as exc:\n raise ConanException(\"Error parsing the profile text file: %s\" % str(exc))","def _sloppy_parse_profiles (self, contents):\n profile_start = contents.find('profiles\":')\n profile_list_start = contents.find('profilesList')\n if int(profile_start) > -1 and int(profile_list_start) > -1:\n try:\n try:\n return json.loads('{\"a\":{\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\n except ValueError, e:\n return None\n except TypeError, e:\n return None\n return None","def populate_profiles_from_directory(self):\n\n self.profiles = []\n\n # Go over all the files and create a profile object\n _profile_files = listdir(self.save_dir)\n\n for profile_filename in _profile_files:\n # Only check for .yaml files\n if path.splitext(profile_filename)[1] == '.yaml':\n # Get the data and create a new profile\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\n _profile = Profile(path.splitext(profile_filename)[0])\n _profile.player_data = _file_data\n self.profiles.append(_profile)","def load_people(self, file_path):\n pass","def parse_file(file_path):\n with open(file_path) as f:\n return XmlPropertyListParser().parse(f)","def list_profiles(self) -> dict:\n wsc = self.read_ws_configuration()\n out = OrderedDict()\n for name, json in wsc.profiles.items():\n out[name] = Profile(name, self.ws_data_folder / name, json)\n # Try to find current profile\n try:\n out[self.current_profile_name].is_current = True\n except Exception:\n pass\n return out","def from_file(cls, file_path, ngram_sizes, profile_len):\n profile = cls.from_files((file_path, ), ngram_sizes, profile_len)\n return profile","def get_profiles(self):\n # print(self.uir) #checkpoint\n if os.path.isdir(self.uir+\"/profiles\"):\n profiles=os.listdir(self.uir+\"/profiles\")\n # print(profiles) #checkpoint\n for profile in profiles:\n wsadmin=self.uir+\"/profiles/\"+profile+\"/bin/wsadmin.bat\"\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\n self.profiles.append(self.uir+\"/profiles/\"+profile)\n\n else: print(self.uir+' Instance does not have \"profile\" folder in '+self.uir)\n return","def _load_profile(self, profile_name, cwd):\n\n profile_path = self.get_profile_path(profile_name, cwd)\n try:\n text = load_user_encoded(profile_path)\n except Exception as e:\n raise ConanException(f\"Cannot load profile:\\n{e}\")\n\n # All profiles will be now rendered with jinja2 as first pass\n base_path = os.path.dirname(profile_path)\n file_path = os.path.basename(profile_path)\n context = {\"platform\": platform,\n \"os\": os,\n \"profile_dir\": base_path,\n \"profile_name\": file_path,\n \"conan_version\": conan_version}\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\n text = rtemplate.render(context)\n\n try:\n return self._recurse_load_profile(text, profile_path)\n except ConanException as exc:\n raise ConanException(\"Error reading '%s' profile: %s\" % (profile_name, exc))","def read_plist(path: str) -> dict:\n return _read_plist(path, plistlib.FMT_XML)","def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\r\n if lastused:\r\n writer.WriteAttributeString(\"LastUsed\", \",\".join(lastused))\r\n for profile in profiles:\r\n profiles[profile].save_to_xml(writer)\r\n writer.WriteEndElement()\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def profiles(self):\n if not self._profiles:\n self.GetAllProfiles()\n return self._profiles","def get_profiles(profile_file_directory):\r\n\t\r\n\tprofile_file_path = profile_file_directory+ \"/profiles.txt\"\r\n\tlist_of_all_allele_numbers_tuple = []\r\n\tdatabase = None\r\n\tlocusList = []\r\n\t\r\n\tfor l in open(profile_file_path):\r\n\t if database is None:\r\n\t\tdatabase = {}\r\n\t\tlocusList = l.split()[1:]\r\n\t\tcontinue\r\n\t t = l.split()\r\n\t st = t[0]\r\n\t v = ' '.join([s for s in t[1:]])\r\n\t if v in database:\r\n\t\tprint >> sys.stderr, 'sequence type ' + str(st) + ' is a duplicate of ' + str(database[v])\r\n\t database[v] = st\r\n\t covert_string_to_tuple_list_of_allele_numbers = tuple(int(x) for x in re.findall(\"[0-9]+\", v)) \r\n\t list_of_all_allele_numbers_tuple.append(covert_string_to_tuple_list_of_allele_numbers)\r\n\t\t\r\n\treturn (database, locusList, list_of_all_allele_numbers_tuple)","def _load_data(self, filename):\n if not os.path.isfile(filename):\n return False\n\n with open(filename) as f:\n data = pickle.load(f)\n if data:\n self.profiles = data['profiles']\n self.user_data = data['user_data']\n self.api_data = data['api_data']\n else:\n return False","def profile(filename: str) -> 'Iterator[None]':\n profiler = Profile()\n profiler.enable()\n\n yield\n\n profiler.disable()\n profiler.create_stats()\n profiler.dump_stats('profiles/{}'.format(filename))","def loadProfile(fname):\n \n x = np.loadtxt(fname)\n return x[:,1]","def get_user():\n with open(app.config['DATA_XML'], 'r') as xmlfile:\n root = ElementTree.parse(xmlfile).getroot()\n\n for item in root.iter('server'):\n result = '{}://{}'.format(\n item.find('protocol').text,\n item.find('host').text\n )\n\n data = {\n user.attrib['id']: {\n 'name': user.find('name').text,\n 'avatar': '{}{}'.format(\n result,\n user.find('avatar').text\n )\n }\n for user in root.iter('user')\n }\n return OrderedDict(\n sorted(\n data.items(),\n key=lambda result: itemgetter('name')(itemgetter(1)(result)),\n cmp=locale.strcoll\n )\n )","def GetAllProfiles(self):\n profiles = []\n feed_uri = self._gd_client.GetFeedUri('profiles')\n while feed_uri:\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\n profiles.extend(feed.entry)\n feed_uri = feed.FindNextLink()\n self._profiles = profiles","def ReadWiredNetworkProfile(self, profilename):\n profile = {}\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n for x in config.options(profilename):\n profile[x] = misc.Noneify(config.get(profilename, x))\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\n self.WiredNetwork = profile\n return \"100: Loaded Profile\"\n else:\n self.WiredNetwork = None\n return \"500: Profile Not Found\"","def profiles_path(self) -> Path:\n return self._config.data_path / \"hmm\" / \"profiles.hmm\"","def read_pardus_profiles(self):\n\n self.lan_config = ConfigParser.ConfigParser()\n self.lan_config.read(self.lan_config_path)\n connection_type = \"802-3-ethernet\"\n for section in self.lan_config.sections():\n lan_settings = {}\n for option in self.lan_config.options(section):\n if option == \"device\":\n #To strip device name from long device string\n lan_settings[option] = self.lan_config.get(section, option).split(\"_\")[-1]\n else:\n lan_settings[option] = self.lan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, lan_settings)\n self.pardus_profiles.append(p)\n\n self.wlan_config = ConfigParser.ConfigParser()\n self.wlan_config.read(self.wlan_config_path)\n connection_type = \"802-11-wireless\"\n for section in self.wlan_config.sections():\n wlan_settings = {}\n for option in self.wlan_config.options(section):\n if option == \"device\":\n wlan_settings[option] = self.wlan_config.get(section, option).split(\"_\")[-1]\n else:\n wlan_settings[option] = self.wlan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\n self.pardus_profiles.append(p)","def have_profile_dir(path, maxdepth=3, filename=\"profiles.desc\"):\n\twhile path != \"/\" and maxdepth:\n\t\tif os.path.exists(os.path.join(path, \"profiles\", filename)):\n\t\t\treturn normalize_path(path)\n\t\tpath = normalize_path(path + \"/..\")\n\t\tmaxdepth -= 1","def read_file(filepath: str) -> dict:\n if not filepath.endswith(\".csv\"):\n raise RuntimeError(\"File extension must be .csv\")\n\n people = {}\n with open(filepath) as csv:\n for line in csv:\n email, person = Parser.parse_line(line.rstrip(\"\\n\"))\n if email not in people:\n people[email] = person\n else:\n print(\"Ignoring person with duplicate email {}\".format(email))\n return people","def get_resolved_profile_catalog(trestle_root: pathlib.Path, profile_path: pathlib.Path) -> cat.Catalog:\n logger.debug(f'get resolved profile catalog for {profile_path} via generated Import.')\n import_ = prof.Import(href=str(profile_path), include_all={})\n import_filter = ProfileResolver.Import(trestle_root, import_)\n logger.debug('launch pipeline')\n result = next(import_filter.process())\n return result","def profile_directory_path(request, file):\n return directory_path('profile', file)","def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\n assert isinstance(profiles, list), \"Please provide a list of profiles\"\n cache = ClientCache(self._conan_api.cache_folder)\n loader = ProfileLoader(cache)\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\n profile.conf.validate()\n cache.new_config.validate()\n # Apply the new_config to the profiles the global one, so recipes get it too\n profile.conf.rebase_conf_definition(cache.new_config)\n return profile","def loadPerson(fileid):\n dinf = {}\n drel = {}\n root = etree.Element(\"person\")\n text = None\n # TODO: put this in a global variable, and make a function to populate it from the DTD.\n tags = [\"commonname\", \"ctitle\", \"gname\", \"mname\", \"fname\", \"nname\", \"nameorder\", \"gender\", \"bday\", \"dday\", \"stories\", \"mention\", \"appear1ch\", \"appear1wr\", \"conflict\", \"leadrel\", \"bodytyp\", \"age\", \"skin\", \"eyes\", \"hair\", \"dmarks\", \"dress\", \"attposs\", \"asmell\", \"personality\", \"speech\", \"formocc\", \"currocc\", \"strength\", \"weak\", \"mole\", \"hobby\", \"misc\", \"ethnic\", \"origin\", \"backstory\", \"residence\", \"minchar\", \"talent\", \"abil\", \"sgoal\", \"other\", \"relat\", \"aspects\", \"update\"]\n tags.remove(\"currocc\")\n tags.remove(\"formocc\")\n tags.remove(\"relat\")\n tags.append(\"file\")\n for tag in tags:\n dinf[tag] = [\"\",False]\n dinf['currocc'] = {}\n dinf['currocc']['pos'] = [\"\",False]\n dinf['formocc'] = {}\n dinf['formocc']['pos'] = [\"\",False]\n dinf['aspects'] = {}\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n dinf['formocc']['events'] = events\n if not idExists(fileid):\n status.push(0,\"new person created... '%s'\" % fileid)\n return (dinf,drel)\n fn = os.path.join(config['realmdir'],fileid + \".xml\")\n status.push(0,\"loading person from XML... '%s'\" % fn)\n try:\n with codecs.open(fn,'rU','utf-8') as f:\n tree = etree.parse(f)\n f.close()\n root = tree.getroot()\n except IOError as e:\n print \" Could not open configuration file: %s\" % e\n\n ir = 0\n for i in range(len(root)):\n if root[i].tag is not None:\n if root[i].tag == \"relat\":\n if len(root[i]) > 0:\n node = \"\"\n node = root[i].find(\"file\")\n if node is not None and node.text:\n node = node.text.strip()\n node = common.validateFileid(node)\n drel[node] = {}\n for j in root[i]:\n if j.tag == \"events\":\n if not drel[node].get('events'): drel[node]['events'] = {}\n for k in j:\n stone = str(len(drel[node]['events']))\n drel[node]['events'][stone] = {}\n for m in k:\n if m.tag and m.text:\n drel[node]['events'][stone][m.tag] = [m.text.strip(),False]\n else: # elif j.tag != \"file\":\n if j.tag and j.text:\n drel[node][j.tag] = [j.text.strip(),False]\n if config['debug'] > 3: print drel[node]\n else:\n if config['debug'] > 0:\n print \"Invalid relat tag:\"\n for c in root[i]:\n print c.tag\n else: # no relat length\n if config['debug'] > 0: print \"Empty relat tag.\"\n elif root[i].tag == \"currocc\":\n# print \",\",\n dinf['currocc'] = {}\n try:\n dinf['currocc']['pos'] = [root[i].find(\"pos\").text.strip(),False]\n except AttributeError:\n del dinf['currocc']\n if dinf.get('currocc'):\n events = {}\n if len(root[i]) > 1:\n for j in root[i]:\n if j.tag is not None:\n if j.tag == \"events\":\n for k in j:\n if k.tag == \"mstone\":\n le = str(len(events))\n events[le] = {}\n events[le]['date'] = [\"\",False]\n events[le]['event'] = [\"\",False]\n for m in k:\n if m.tag and m.text:\n events[le][m.tag] = [m.text.strip(),False]\n else:\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n else:\n dinf['currocc'] = {}\n dinf['currocc']['pos'] = [\"\",False]\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['currocc']['events'] = events\n elif root[i].tag == \"formocc\":\n# print \",\",\n dinf['formocc'] = {}\n try:\n dinf['formocc']['pos'] = [root[i].find(\"pos\").text.strip(),False]\n except AttributeError:\n del dinf['formocc']\n if dinf.get('formocc'):\n events = {}\n if len(root[i]) > 1:\n for j in root[i]:\n if j.tag is not None:\n if j.tag == \"events\":\n for k in j:\n if k.tag == \"mstone\":\n le = str(len(events))\n events[le] = {}\n events[le]['date'] = [\"\",False]\n events[le]['event'] = [\"\",False]\n for m in k:\n if m.tag and m.text:\n events[le][m.tag] = [m.text.strip(),False]\n else:\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['formocc']['events'] = events\n else:\n dinf['formocc'] = {}\n dinf['formocc']['pos'] = [\"\",False]\n events = {}\n events['0'] = {}\n events['0']['date'] = [\"\",False]\n events['0']['event'] = [\"\",False]\n dinf['formocc']['events'] = events\n elif root[i].tag == \"aspects\":\n if len(root[i]) > 0:\n nodes = root[i]\n for node in nodes:\n k = str(len(dinf['aspects']))\n dinf['aspects'][k] = {}\n if node.tag and node.text:\n dinf['aspects'][k] = [node.text.strip(),False]\n else:\n if config['debug'] > 0:\n print \"Invalid aspects tag:\"\n print node.tag + ': ' + node.text,\n else: # no aspects length\n if config['debug'] > 0: print \"Empty aspects tag.\"\n elif root[i].text is not None:\n# print \".\",\n dinf[root[i].tag] = [root[i].text.strip(), False]\n if config['debug'] > 2: print str(i) + \" \",\n# print str(dinf)\n return (dinf,drel)","def read_config(profile):\n p = Path(CONFIG_FILE_PATH).expanduser()\n p.parent.mkdir(parents=True, exist_ok=True)\n config = configparser.ConfigParser()\n config.read(str(p))\n\n if config.has_section(profile):\n return dict(config.items(profile))\n else:\n return {}","def parse_xml(file: Path):\n check_file(file) # Check the existency of the file\n\n doc = ET.parse(file)\n data = parse_xml_tree(doc.getroot())\n return data","def _find_cb_profiles():\n dir_locations = [\".carbonblack\", os.path.join(os.path.expanduser(\"~\"), \".carbonblack\")]\n cred_file = \"credentials.response\"\n profiles = []\n\n for dir in dir_locations:\n cred_file_path = os.path.join(dir, cred_file)\n _MOD_LOGGER.debug(\"Searching CB profiles on '%s'\", cred_file_path)\n if os.path.exists(cred_file_path):\n _MOD_LOGGER.debug(\"File exists, parsing...\")\n config = configparser.ConfigParser(default_section=\"cbbackend\", strict=True)\n config.read(cred_file_path)\n profiles += [sec_name for sec_name in config.keys() if sec_name != \"cbbackend\"]\n\n if profiles:\n _MOD_LOGGER.debug(\"Requested to read 'all' profiles. Found: %s\", \",\".join(profiles))\n\n return profiles","def _read_profile_file(cls, profile_file):\n qid2title: Dict[str, str] = {}\n qid2desc: Dict[str, str] = {}\n alias2qids: Dict[str, list] = {}\n type_systems: Dict[str, Dict[str, List[str]]] = {}\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\n\n num_lines = sum(1 for _ in open(profile_file))\n with open(profile_file, \"r\") as in_f:\n for line in tqdm(in_f, total=num_lines, desc=\"Reading profile\"):\n line = ujson.loads(line)\n\n # Check keys and schema\n assert all(\n k in line.keys() for k in REQUIRED_KEYS\n ), f\"A key from {REQUIRED_KEYS} was not in {line}\"\n try:\n # Asserts the types are correct\n ent = EntityObj(\n entity_id=line[\"entity_id\"],\n mentions=line[\"mentions\"],\n title=line.get(\"title\", line[\"entity_id\"]),\n description=line.get(\"description\", \"\"),\n types=line.get(\"types\", {}),\n relations=line.get(\"relations\", []),\n )\n except ValidationError as e:\n print(e.json())\n raise e\n if ent.entity_id in qid2title:\n raise ValueError(f\"{ent.entity_id} is already in our dump\")\n qid2title[ent.entity_id] = ent.title\n qid2desc[ent.entity_id] = ent.description\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\n for men_pair in ent.mentions:\n # Lower case mentions for mention extraction\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\n if new_men not in alias2qids:\n alias2qids[new_men] = []\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\n # Add type systems of type_sys -> QID -> list of type names\n for type_sys in ent.types:\n if type_sys not in type_systems:\n type_systems[type_sys] = {}\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\n # Add kg relations QID -> relation -> list of object QIDs\n for rel_pair in ent.relations:\n if \"relation\" not in rel_pair or \"object\" not in rel_pair:\n raise ValueError(\n \"For each value in relations, it must be a JSON with keys relation and object\"\n )\n if ent.entity_id not in qid2relations:\n qid2relations[ent.entity_id] = {}\n if rel_pair[\"relation\"] not in qid2relations[ent.entity_id]:\n qid2relations[ent.entity_id][rel_pair[\"relation\"]] = []\n qid2relations[ent.entity_id][rel_pair[\"relation\"]].append(\n rel_pair[\"object\"]\n )\n\n # Sort mentions based on score, highest first\n for al in list(alias2qids.keys()):\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\n # Add all qids to the type systems and KG connections with empty values\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\n for qid in qid2title:\n for type_sys in type_systems:\n if qid not in type_systems[type_sys]:\n type_systems[type_sys][qid] = []\n if qid not in qid2relations:\n qid2relations[qid] = {}\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations","def profile_files(profile):\n flist = os.listdir(osp.join(profile, 'startup'))\n profile_path = osp.join(osp.abspath('.'), profile)\n return [osp.join(profile_path, 'startup', x) for x in flist]","def profiles_names(self):\n url = get_url('profiles')\n response = self._get(url)\n raise_on_error(response)\n return response.json()","def profiles(self):\n return self._profiles","def profiles(self):\n return self._profiles","def read_profiles(filename):\n profiles = []\n with gzip.open(filename, mode='rt', encoding='utf8') as infile:\n for line in infile:\n profiles.append(Counter(line.split()))\n return profiles","def loadProfilesFromPlist_(self, plist):\r\n \r\n LogInfo(u\"Loading update profiles with PublicationDate %@\", plist[u\"PublicationDate\"])\r\n self.profiles = dict()\r\n for name, updates in plist[u\"Profiles\"].iteritems():\r\n profile = list()\r\n for update in updates:\r\n profile.append(plist[u\"Updates\"][update])\r\n self.profiles[name] = profile\r\n self.publicationDate = plist[u\"PublicationDate\"]\r\n self.updatePaths = dict()\r\n for name, update in plist[u\"Updates\"].iteritems():\r\n filename, ext = os.path.splitext(os.path.basename(update[u\"url\"]))\r\n self.updatePaths[update[u\"sha1\"]] = u\"%s(%s)%s\" % (filename, update[u\"sha1\"][:7], ext)\r\n self.deprecatedInstallerBuilds = dict()\r\n try:\r\n for replacement, builds in plist[u\"DeprecatedInstallers\"].iteritems():\r\n for build in builds:\r\n self.deprecatedInstallerBuilds[build] = replacement\r\n except KeyError:\r\n LogWarning(u\"No deprecated installers\")\r\n if self.delegate:\r\n self.delegate.profilesUpdated()","def parsePlayerProfile(path, args):\n soup = BeautifulSoup(requests.get(\"{domain}/{pathStr}\".format(domain=args.domain[0], pathStr=path)).text, \"html.parser\")\n\n playerPathName = re.split(\"/|\\.\", path.lower())[-2]\n # set up so a=11, b=12, etc, then joins the values together\n playerID = \"\".join([char if char.isdigit() else str(ord(char) % 86) for char in playerPathName])\n birthday = soup.find(\"span\", id=\"necro-birth\").get(\"data-birth\")\n hasDebuted = soup.find(\"a\", string=\"Debut\")\n debut = isRetired.get(\"href\") if hasDebuted else None\n isRetired = soup.find(\"a\", string=\"Last Game\")\n retired = isRetired.get(\"href\") if isRetired else None\n\n player = parseProfileHeader(soup, playerID, birthday, debut, retired, args)\n\n positionTable = soup.find(\"table\", id=\"standard_fielding\")\n positions = parsePositionInfo(positionTable, args)\n player.addPositions(positions)\n\n hittingTable = soup.find(\"table\", id=\"batting_standard\")\n hittingStats = parseBattingTable(hittingTable, args)\n player.addStats(hittingStats, \"batting\")\n\n pitchingTable = soup.find(\"table\", id=\"pitching_standard\")\n advancedPathTag = soup.find(\"a\", href=re.compile(r\"-pitch.shtml\"), string=\"More Stats\")\n pitchingStats = parsePitchingTables(pitchingTable, advancedPathTag, args)\n player.addStats(pitchingStats, \"pitching\")\n\n return player","def parse_profiles(profiles):\n if type(profiles) == list:\n return [\n x\n for x in [ProfileSettingParser.parse(x) for x in profiles]\n if x is not None\n ]\n else:\n return []","def load(self, config, profile_key):\n # a none numeric argument is supposed to be 'alias'\n if re.search(r\"\\b[0-9]+\\b\", profile_key) is None:\n key = profile_key\n self.alias = key\n access_by = \"alias\"\n else:\n key = (int)(profile_key)\n self.id = key\n access_by = \"id\"\n self.logger.debug(\"==> profile will be access by his '%s'\" % access_by)\n\n # check if the profile exists into config...\n i = 0\n profile_found = False\n while (i < len(config[\"profiles\"])\n and profile_found is False):\n\n if config[\"profiles\"][i][access_by] == key:\n profile_found = True\n self.logger.debug(\"==> profile '%s' found (access by '%s')\" %\n (key, access_by))\n self.config = config[\"profiles\"][i]\n\n i += 1\n\n # ... and if not raise an exception\n if profile_found is False:\n raise ProfileLoadError(\"profile '%s' not found\" % profile_key)\n\n # set profile properties\n self.id = self.config[\"id\"]\n self.alias = self.config[\"alias\"]\n self.state_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".json\")\n self.lock_file = os.path.join(config[\"state_dir\"],\n self.config[\"alias\"] + \".lock\")\n\n # set the todo queue limit based on configuration\n # - default is 1\n try:\n todo_limit_queue = self.config[\"todo_queue_limit\"]\n self.todo_queue_limit = todo_limit_queue\n except KeyError:\n self.todo_queue_limit = 1","def get_profiles(args):\n # Use profile from cli if provided\n if args.profile and not args.update_config:\n return [args.profile]\n\n # Run config to get or set the config file\n config = configparser.ConfigParser()\n\n if os.path.isfile(SETTINGS_FILE) and not args.update_config:\n # Get profiles from config\n config.read(SETTINGS_FILE)\n else:\n # Get default profiles from user\n try:\n profiles_input = input(\n 'Please enter space separated list of profiles to use: '\n )\n except KeyboardInterrupt:\n # Avoid ugly stacktrace on ctrl-c in input\n sys.exit(1)\n # Setup config\n config.add_section('profiles')\n config.set('profiles', 'default', profiles_input)\n # Write to config\n config_file = open(SETTINGS_FILE, 'w')\n config.write(config_file)\n config_file.close()\n\n return config.get('profiles', 'default').split()","def parse_xml_file(self, filename):\n try:\n dom = parse(filename)\n process_includes(dom)\n except ExpatError, x:\n raise EzXMLError(\"Failed to parse: %s\" % x)\n return self.walkdom(dom.documentElement)","def get_profiles(self):\n profiles = [['Profile name', 'GUID']]\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\n for row in r.fetchall():\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\n return profiles","def list_profiles(self, params):\n return self.profiles","def parse_manifest_xml(manifest_path):\n dir_project_dict = {}\n parsed_xml = xml.dom.minidom.parse(manifest_path)\n projects = parsed_xml.getElementsByTagName('project')\n for project in projects:\n name = project.getAttribute('name')\n path = project.getAttribute('path')\n if path:\n dir_project_dict[path] = name\n else:\n dir_project_dict[name] = name\n return dir_project_dict","def read_topics_from_xml(file, topics_dict):\n\n # Read XML file\n xml = read_file(file)\n\n # Pass to HTML\n html = BeautifulSoup(xml, \"html.parser\")\n\n # Get topics\n topics = html.findAll('topic')\n for topic in topics:\n # Get topic number + query and description\n topic_number = int(topic['number'])\n query = [topic.query.getText().strip(),\n topic.description.getText().strip()]\n topics_dict[topic_number]['query'] = query\n\n # Get subtopics\n subtopics = topic.findAll('subtopic')\n topics_dict[topic_number]['subtopics'] = [x.getText().strip() for x in subtopics]\n\n return topics_dict","def get_list(profiles_folder, logger):\n profile_list = []\n with scandir(profiles_folder) as it:\n for entry in it:\n if entry.is_file():\n filepath = profiles_folder + entry.name\n profile = json_from_file(filepath, logger)\n if profile is not None:\n try:\n profile_list.append({\"filepath\":filepath, \"name\":profile[\"name\"], \"description\":profile[\"description\"]})\n except AttributeError:\n logger.error(\"Missing attributes in \" + filepath)\n logger.error(str(profile))\n return profile_list","def apply(self):\r\n\r\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\r\n\r\n def saving_file(xml):\r\n \"\"\" Save user profile in xml format to temp_ dir.\"\"\"\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))\r\n\r\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\r\n \"/data/sampleProfile.xml\")\r\n\r\n # The below code will parse the sample xml file\r\n # and fill important details entered by the user.\r\n root_tree = parse_xml.getroot()\r\n root_tree[0].text = self.ssid.get()\r\n root_tree[1][0][0].text = self.ssid.get()\r\n root_tree[3].text = self.connection_mode.get().lower()\r\n security = root_tree[4][0]\r\n security[0][0].text = self.authentication.get()\r\n security[0][1].text = self.encryption.get()\r\n if self.authentication.get() != \"open\":\r\n etree.SubElement(security, \"sharedKey\")\r\n etree.SubElement(security[1], \"keyType\").text = \"passPhrase\"\r\n etree.SubElement(security[1], \"protected\").text = \"false\"\r\n etree.SubElement(security[1], \"keyMaterial\").text = self.password.get()\r\n\r\n # Save the xml file\r\n saving_file(root_tree)\r\n\r\n # Add profile to the system.\r\n temp_path = 'netsh wlan add profile filename=\"' + self.app_path + \"\\\\temp_\\\\\"\r\n output_ = subprocess.run(temp_path + file_name + '.xml\"', shell=True,\r\n capture_output=True, text=True)\r\n os.remove(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\")\r\n\r\n # If unable to add profile.\r\n if output_.returncode != 0:\r\n message = \"Sorry, Unable to add profile.\\n(You entered wrong details \" \\\r\n \"or else you don't have admin rights.)\"\r\n image_ = \"error\"\r\n\r\n else:\r\n message = \"Profile added successfully (Please Refresh)\"\r\n image_ = \"warning\"\r\n\r\n MessageBox(self.parent, message, image_)","def load(cls, filepath):\n return BaseProfiler.load(filepath)","def load(cls, filepath):\n # Load profile from disk\n with open(filepath, \"rb\") as infile:\n data = pickle.load(infile)\n\n # remove profiler class if it exists\n profiler_class = data.pop('profiler_class', None)\n\n # if the user didn't load from the a given profiler class, we need\n # to determine which profiler is being loaded.\n profiler_cls = cls\n if cls is BaseProfiler:\n if profiler_class == 'StructuredProfiler':\n profiler_cls = StructuredProfiler\n elif profiler_class == 'UnstructuredProfiler':\n profiler_cls = UnstructuredProfiler\n elif profiler_class is None: # deprecated case\n profiler_cls = StructuredProfiler\n if '_empty_line_count' in data:\n profiler_cls = UnstructuredProfiler\n else:\n raise ValueError(f'Invalid profiler class {profiler_class} '\n f'failed to load.')\n\n profile_options = profiler_cls._option_class()\n profile_options.data_labeler.is_enabled = False\n profiler = profiler_cls(None, options=profile_options)\n\n for key in data:\n setattr(profiler, key, data[key])\n\n # Restore all data labelers\n profiler._restore_data_labelers()\n return profiler","def find_prefs():\n conf = SafeConfigParser()\n try:\n conf.read(PROFILES)\n except Exception as err:\n log.error('reading profiles.ini: %s', err)\n return None\n\n for section in conf.sections():\n if conf.has_option(section, 'Name') and \\\n conf.get(section, 'Name') == 'default':\n path = conf.get(section, 'Path')\n if conf.getboolean(section, 'IsRelative'):\n path = os.path.join(CONFDIR, path)\n\n return unicodify(os.path.join(path, 'prefs.js'))\n\n return None","def get_profile_info(self):\n\n drill_path = str(Path.home())+\"/Documents/ball_e_profiles/drill_profiles/{drill_name}/{drill_name}.csv\".format(\n drill_name=self.drill_name)\n with open(drill_path) as file:\n csv_reader = csv.reader(file, delimiter=',')\n row_count = 0\n info_dict = dict()\n for row in csv_reader:\n if row_count == 0:\n row_count += 1\n else:\n info_dict[row[0]] = [row[1], row[2], row[3]]\n row_count += 1\n\n return info_dict","def load_profile(dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"rb\") as f:\n logger = pickle.load(f)\n return logger","def profiles(self):\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n return list(filter(lambda x: x is not None, executor.map(self.profile_details, self.profiles_names())))","def profile(self) -> dict:\n endpoint = \"/api/users/profile/\"\n ret = self._request(endpoint=endpoint)\n return ret","def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def Load(self, filename):\n logging.info(\"Reading users file at %s\", filename)\n try:\n try:\n contents = utils.ReadFile(filename)\n except EnvironmentError as err:\n self._users = None\n if err.errno == errno.ENOENT:\n logging.warning(\"No users file at %s\", filename)\n else:\n logging.warning(\"Error while reading %s: %s\", filename, err)\n return False\n\n users = http.auth.ParsePasswordFile(contents)\n\n except Exception as err: # pylint: disable=W0703\n # We don't care about the type of exception\n logging.error(\"Error while parsing %s: %s\", filename, err)\n return False\n\n self._users = users\n\n return True","def profiles():\n images = get_uploaded_images()\n records = db.session.query(UserProfile).all()\n return render_template('profiles.html', images=images, records =records)","def _parse_profile_data (self, netflix_page_data):\n profiles = {};\n important_fields = [\n 'profileName',\n 'isActive',\n 'isAccountOwner',\n 'isKids'\n ]\n # values are accessible via dict (sloppy parsing successfull)\n if type(netflix_page_data) == dict:\n for profile_id in netflix_page_data.get('profiles'):\n if self._is_size_key(key=profile_id) == False and type(netflix_page_data['profiles'][profile_id]) == dict and netflix_page_data['profiles'][profile_id].get('avatar', False) != False:\n profile = {'id': profile_id}\n for important_field in important_fields:\n if important_field == 'profileName':\n profile.update({important_field: HTMLParser.HTMLParser().unescape(netflix_page_data['profiles'][profile_id]['summary'][important_field]).encode('utf8')})\n else:\n profile.update({important_field: netflix_page_data['profiles'][profile_id]['summary'][important_field]})\n avatar_base = netflix_page_data['nf'].get(netflix_page_data['profiles'][profile_id]['summary']['avatarName'], False);\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\n profile.update({'avatar': avatar, 'isFirstUse': False})\n profiles.update({profile_id: profile})\n return profiles\n\n # values are stored in lists (returned from JS parser)\n # TODO: get rid of this christmas tree of doom\n for item in netflix_page_data:\n if 'hasViewedRatingWelcomeModal' in dict(item).keys():\n for profile_id in item:\n if self._is_size_key(key=profile_id) == False and type(item[profile_id]) == dict and item[profile_id].get('avatar', False) != False:\n profile = {'id': profile_id}\n for important_field in important_fields:\n profile.update({important_field: item[profile_id]['summary'][important_field]})\n avatar_base = item['nf'].get(item[profile_id]['summary']['avatarName'], False);\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\n profile.update({'avatar': avatar})\n profiles.update({profile_id: profile})\n return profiles","def read_conf_file(self, file_path):\n ambari_props = {}\n if os.path.exists(file_path):\n with open(file_path, \"r\") as f:\n lines = f.readlines()\n if lines:\n Logger.debug(\"Reading file {0}, has {1} lines.\".format(file_path, len(lines)))\n for l in lines:\n l = l.strip()\n if l.startswith(\"#\"):\n continue\n parts = l.split(\"=\")\n if len(parts) >= 2:\n prop = parts[0]\n value = \"\".join(parts[1:])\n ambari_props[prop] = value\n return ambari_props","def dumpProfiles(profiles):\n with open(userProfilesDir, \"w\") as outfile:\n outfile.writelines(json.dumps(profiles, indent=4))\n outfile.close()","def parse_profile_list_page(page):\n page_tree = etree.HTML(page)\n profile_list = page_tree.xpath(\n '//table[@class=\"table_header\"]/tr[position() > 4 and position() < (last() - 3)]/td/node()[1]')\n if profile_list:\n profile_list[0] = \"main\"\n return [text.strip() for text in profile_list]\n else:\n raise ProfileException(\"Failed to get profile list\")","def addon_profiles(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input['AddonProfilesArgs']]]]:\n return pulumi.get(self, \"addon_profiles\")","def profiles(self, profiles):\n\n self._profiles = profiles","def read_file(file_path):\n\n output_dict = dict()\n try:\n if os.path.exists(file_path):\n with open(file_path) as fd:\n output = fd.readlines()\n for idx in range(len(output)):\n key_info = output[idx].split('=')[0].strip()\n value_info = output[idx].split('=')[1].strip()\n output_dict[key_info] = value_info\n return output_dict\n except Exception as e:\n SysTools.logger.warning(\"Read file:%s failed, reason:%s\" % (file_path, str(e)))","def read_file(fileName):\r\n\r\n xmlFile = open(fileName, 'r', encoding='utf-8')\r\n\r\n xmlContent = xmlFile.read()\r\n dictContent = xmltodict.parse(xmlContent)\r\n\r\n return dictContent","def load_persistent_settings(path):\n settings = {}\n if path.exists():\n with open(path, \"r\") as f:\n persistent = yaml.load(f)\n if persistent:\n settings.update(persistent)\n return settings","def ppt_file_to_dict(self, file_path):\n try:\n file = open(file_path, \"rb\")\n\n except IOError as e:\n print(e)\n return\n\n pres = Presentation(file)\n file.close()\n\n for i in range(len(pres.slides)):\n self.process_slide(pres.slides[i], i + 1, file_path)","def ReadUserMappings(self, file_object):\n # TODO: replace by:\n # config_parser = configparser. ConfigParser(interpolation=None)\n config_parser = configparser.RawConfigParser()\n config_parser.readfp(file_object)\n\n user_mappings = {}\n for option_name in config_parser.options(u'user_mappings'):\n user_mapping = self._GetConfigValue(\n config_parser, u'user_mappings', option_name)\n\n option_name = option_name.lower()\n user_mappings[option_name] = user_mapping.lower()\n\n return user_mappings","def getFileInfoDictionaryFromXML(xml_file):\n\n # Format: { lfn : [pfn, guid] }\n # Example:\n # lfn = \"EVNT.01461041._000001.pool.root.1\"\n # pfn = file_info_dictionary[lfn][0]\n # guid = file_info_dictionary[lfn][1]\n\n file_info_dictionary = {}\n file_list = getFileListFromXML(xml_file)\n for f in file_list:\n lfn, pfn, guid = getFileInfoFromXML(f)\n file_info_dictionary[lfn] = [pfn, guid]\n\n return file_info_dictionary","def getFirefoxProfile(self, profileDirectory=None):\n fp = webdriver.FirefoxProfile(profile_directory=profileDirectory)\n return fp","def _load_file(self, file_path: str) -> dict:\n raise NotImplementedError()","def list(self):\n # List is to be extended (directories should not have a trailing slash)\n paths_to_ignore = ['.DS_Store']\n\n profiles = []\n cache = ClientCache(self._conan_api.cache_folder)\n profiles_path = cache.profiles_path\n if os.path.exists(profiles_path):\n for current_directory, _, files in os.walk(profiles_path, followlinks=True):\n files = filter(lambda file: os.path.relpath(\n os.path.join(current_directory, file), profiles_path) not in paths_to_ignore, files)\n\n for filename in files:\n rel_path = os.path.relpath(os.path.join(current_directory, filename),\n profiles_path)\n profiles.append(rel_path)\n\n profiles.sort()\n return profiles","def get_peak_stats(file_path):\n peak_stats = dict(numberOfPeaks=0,\n peakMean=0,\n peakMeanStd=0,\n peakMedian=0,\n peakMedianStd=0,\n medianTagSingletons=0,\n singletons=0)\n stddevs = []\n peak_singleton_scores = []\n scores = []\n singletons = 0\n i = 0\n with open(file_path) as fh:\n for i, line in enumerate(fh):\n items = line.split('\\t')\n # Gff column 6 is score.\n score = float(items[5])\n scores.append(score)\n # Gff column 9 is a semicolon-separated list.\n attributes = items[8].split(';')\n for attribute in attributes:\n if attribute.startswith('stddev'):\n val = float(attribute.split('=')[1])\n stddevs.append(val)\n if val == 0.0:\n # We have a peakSingleton.\n singletons += 1\n peak_singleton_scores.append(score)\n break\n fh.close()\n if i > 0:\n # The number of lines in the file is the number of peaks.\n peak_stats['numberOfPeaks'] = i + 1\n peak_stats['peakMean'] = numpy.mean(scores)\n peak_stats['peakMeanStd'] = numpy.mean(stddevs)\n peak_stats['peakMedian'] = numpy.median(scores)\n peak_stats['peakMedianStd'] = numpy.median(stddevs)\n peak_stats['medianTagSingletons'] = numpy.median(peak_singleton_scores)\n peak_stats['singletons'] = singletons\n return peak_stats","def get_all_profiles(store=\"local\"):\n return {\n \"Domain Profile\": get_all_settings(profile=\"domain\", store=store),\n \"Private Profile\": get_all_settings(profile=\"private\", store=store),\n \"Public Profile\": get_all_settings(profile=\"public\", store=store),\n }","def parseXML(xmlFile):\n\n tree = etree.parse(xmlFile)\n root = tree.getroot() \n transitionTable = dict()\n transitionTable = getTransitions(tree, root, transitionTable)\n return tree, root, transitionTable","def load_vsp_pairs(path: str) -> dict:\n vsp_pairs = dict()\n with io.open(file=path, mode=\"r\", encoding=\"utf-8\") as src_file:\n for line in src_file:\n content = line.split(':', 1)\n vsp_pairs[tuple(content[0].split(','))] = content[1]\n return vsp_pairs","def parse_info_from_file(path):\n try:\n filename = os.path.split(path)[1]\n filename = os.path.splitext(filename)[0]\n age, gender, race, _ = filename.split('_')\n\n return int(age), dataset_dict['gender_id'][int(gender)], dataset_dict['race_id'][int(race)]\n except Exception as ex:\n return None, None, None","def get(self, name='default'):\r\n if name not in self._cache:\r\n full_path = os.path.join(self._base_folder, name)\r\n self._create_if_needed(full_path)\r\n self._cache[name] = Profile(full_path, name)\r\n return self._cache[name]","def loads(file_path: Path) -> tuple[dict[str, Any], list[tuple[int, str]]]:\n\n content = read_file_with_any_encoding(file_path=file_path)\n\n if not all(key in content for key in (\"resource\", \"provider\")):\n return {}, []\n\n file_lines = [(idx + 1, line) for idx, line in enumerate(content.splitlines(keepends=True))]\n\n template: \"dict[str, Any] | list[dict[str, Any]]\" = loader.loads(content=content)\n if not template:\n template = {}\n if isinstance(template, list):\n template = template[0]\n\n if template:\n template = prepare_definition(template)\n\n return template, file_lines","def read_pfile(pfile_loc):\n with open(pfile_loc, mode='r') as pfile:\n lines = pfile.readlines()\n\n profiles = {}\n nprofs = 0 # counter for total number of profiles so far\n linestart = 0 # counter for which line to start at for each profile\n nlines_tot = len(lines)\n\n while True:\n # Read the header line for each profile first\n lin1 = lines[linestart].split()\n npts_prof = int(lin1[0])\n\n xname = lin1[1]\n yname = lin1[2]\n dyname = ''.join(lin1[3:])[:-1]\n\n # Generate and populate the profile arrays\n x = np.zeros(npts_prof)\n y = np.zeros(npts_prof)\n dy = np.zeros(npts_prof)\n for i in range(npts_prof):\n split_line = lines[linestart + i + 1].split()\n x[i] = float(split_line[0])\n y[i] = float(split_line[1])\n dy[i] = float(split_line[2][:-1])\n\n # profiles[xname + '_' + yname] = x # psinorm\n profiles[xname] = x\n profiles[yname] = y\n profiles[dyname] = dy\n\n nprofs += 1\n linestart += 1 + npts_prof\n\n if linestart >= nlines_tot:\n break\n\n # Check if all psinorms are the same, consolidate if so (they are, don't bother separating)\n\n # condense = True\n # psinorm = None\n # for k in profiles.keys():\n # if k is None or k=='':\n # continue\n #\n # if k[:4] == 'psin':\n # if psinorm is None:\n # psinorm = profiles[k]\n #\n # if max(abs(profiles[k] - psinorm)) > 1e-5:\n # condense = False\n # break\n\n # if condense:\n # profiles = {key: value for key, value in profiles.items()\n # if key[:4] != 'psin' or key is None or key==''}\n # profiles['psinorm'] = psinorm\n\n return profiles","def getProfiles(context):\n\n analytics_tool = getToolByName(getSite(), 'portal_analytics')\n # short circuit if user hasn't authorized yet\n if not analytics_tool.is_auth():\n return SimpleVocabulary([])\n\n try:\n profiles = analytics_tool.makeCachedRequest('profiles')\n except error.BadAuthenticationError:\n choices = [('Please authorize with Google in the Google Analytics \\\n control panel.', None)]\n return SimpleVocabulary.fromItems(choices)\n except error.RequestTimedOutError:\n choices = [('The request to Google Analytics timed out. Please try \\\n again later.', None)]\n return SimpleVocabulary.fromItems(choices)\n if profiles:\n unique_choices = {}\n for entry in profiles:\n title = entry.get('name')\n title = crop(title, 40)\n tableId = entry.get('id')\n unique_choices.update({title: tableId})\n choices = unique_choices.items()\n else:\n choices = [('No profiles available', None)]\n return SimpleVocabulary([SimpleTerm(c[1], c[1], c[0]) for c in choices])","def list_profiles(self):\n return self._get(\"posture\", box=BoxList)","def read_pro(path,var_to_plot= None):\n\n # Which variables are you interested in?\n\n if var_to_plot:\n var_codes = ['0500','0501',pro_code_dict(var_to_plot,inverse=True)]\n else:\n var_codes = ['0500', '0501', '0502', '0503', '0506', '0508',\n '0509', '0511', '0512', '0513', '0515',\n '0516', '0521', '0535', '0540', '0541']\n\n # Set up the dictionary to be returned. Dictionary is organised by variable name.\n\n code_dict = pro_code_dict(return_all=True)\n\n variables = {}\n for var in var_codes:\n variables[code_dict[var]] = []\n\n # Open the .PRO file\n\n with open(path, \"r\") as f:\n\n # Iterate line by line\n\n for line in f.readlines():\n\n # If the variable code (first four chars) matches the variable of interest,\n # append that line to the list of lines\n\n if line[:4] in var_codes:\n variables[code_dict[line[:4]]].append(line)\n\n\n # Now remove the header data\n\n for variable in variables.keys():\n\n variables[variable].pop(0)\n\n snowpro_list = [snowpro_from_snapshot(date_index, variables) for date_index in range(len(variables['Date']))]\n\n return (snowpro_list)","def ReadWirelessNetworkProfile(self, id):\n config = ConfigParser.ConfigParser()\n config.read(self.wireless_conf)\n cur_network = self.LastScan[id]\n essid_key = \"essid:\" + cur_network[\"essid\"]\n bssid_key = cur_network[\"bssid\"]\n if self.debug_mode:\n print bssid_key\n if config.has_section(essid_key)and \\\n misc.stringToNone(config.get(essid_key, 'use_settings_globally')):\n return self._read_wireless_profile(config, cur_network, \n essid_key)\n elif config.has_section(bssid_key):\n return self._read_wireless_profile(config, cur_network, bssid_key)\n else:\n cur_network[\"has_profile\"] = False\n return \"500: Profile Not Found\"","def get_server_profile_attributes(name, attribute=None):\n general_xpaths = {\n \"name\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\n \"description\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\n \"server hardware\": FusionServerProfilesPage.ID_SERVER_HARDWARE_NAME,\n \"associated server\": FusionServerProfilesPage.ID_ASSOCIATED_SERVER,\n \"server hardware type\": FusionServerProfilesPage.ID_PROFILE_HARDWARE,\n \"enclosure group\": FusionServerProfilesPage.ID_SERVER_ENC_GROUP,\n \"affinity\": FusionServerProfilesPage.ID_SERVER_AFFINITY,\n \"server power\": FusionServerProfilesPage.ID_SERVER_POWER_STATUS,\n \"serial number\": FusionServerProfilesPage.ID_SERVER_SERIAL_NUMBER,\n \"uuid\": FusionServerProfilesPage.ID_SERVER_UUID,\n }\n\n # Navigate to Server Profile\n if not select_server_profile(name):\n logger._warn(\"Failed to find Server Profile '%s'\" % name)\n BuiltIn().sleep(4)\n # Get attribute data\n if attribute is not None:\n keys = general_xpaths.keys()\n if attribute.lower() not in keys:\n logger._warn(\"Invalid attribute '%s'.\" % attribute)\n return None\n\n # Get specified attribute\n xpath = general_xpaths[attribute.lower()]\n ui_lib.wait_for_element_visible(xpath, timeout=10)\n value = ui_lib.ignore_staleElementRefException(\"get_text\", xpath)\n\n return value\n else:\n # Get all attributes of Server Profile\n general_data = {}\n for attribute, xpath in general_xpaths.iteritems():\n value = ui_lib.ignore_staleElementRefException(\"get_text\", xpath)\n general_data[attribute] = value\n return general_data\n\n return None","def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\n if not os.path.isfile(path):\n raise ValueError(f'File {path} does not exist')\n\n with open(path, 'rb') as file:\n return plistlib.load(file, fmt=format, dict_type=dict)","def list_network_profiles(self, **params):\r\n return self.get(self.network_profiles_path, params=params)","def load_frontend_xml(xml_path):\n frontend_setting = open(xml_path, \"r\").read()\n return frontend_setting","def loadRatingScoreMappingFromFile(file):\n\treturn \\\n\tcompose(\n\t\tdict\n\t , partial(map, lambda line: ((line[0], line[1]), line[2]))\n\t , partial(takewhile, lambda line: len(line) > 2 and line[0] != '')\n\t , lambda t: t[1]\n\t , lambda lines: (pop(lines), lines)\n\t , fileToLines\n \t , partial(join, getDataDirectory())\n\t)(file)","def available_profiles(cls) -> List[str]:\n return list(cfg.get(\"profiles\"))","def load_settings(path):\n if os.path.exists(path):\n comments = lambda s: s and not s.startswith(\"#\")\n settings = filter(comments, open(path, 'r'))\n return dict((k.strip(), v.strip()) for k, _, v in\n [s.partition('=') for s in settings])\n # Handle nonexistent or empty settings file\n return {}"],"string":"[\n \"def load_profiles_from_file(file_path):\\r\\n profiles = {}\\r\\n\\r\\n lastused = \\\"\\\"\\r\\n\\r\\n if File.Exists(file_path):\\r\\n try:\\r\\n with StreamReader(file_path) as xmlfile:\\r\\n xmldoc = XmlDocument()\\r\\n xmldoc.Load(xmlfile)\\r\\n\\r\\n if xmldoc.DocumentElement.Name == \\\"Profiles\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profiles/Profile\\\")\\r\\n #Individual exported profiles are saved with the document element as Profile\\r\\n elif xmldoc.DocumentElement.Name == \\\"Profile\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profile\\\")\\r\\n\\r\\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\\r\\n elif xmldoc.DocumentElement.Name == \\\"Settings\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Settings/Setting\\\")\\r\\n elif xmldoc.DocumentElement.Name == \\\"Setting\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Setting\\\")\\r\\n\\r\\n #No valid root elements\\r\\n else:\\r\\n MessageBox.Show(file_path + \\\" is not a valid Library Organizer profile file.\\\", \\\"Not a valid profile file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n return profiles, lastused\\r\\n\\r\\n if nodes.Count > 0:\\r\\n for node in nodes: \\r\\n profile = Profile()\\r\\n profile.Name = node.Attributes[\\\"Name\\\"].Value\\r\\n result = profile.load_from_xml(node)\\r\\n\\r\\n #Error loading the profile\\r\\n if result == False:\\r\\n MessageBox.Show(\\\"An error occured loading the profile \\\" + profile.Name + \\\". That profile has been skipped.\\\")\\r\\n\\r\\n else:\\r\\n profiles[profile.Name] = profile\\r\\n\\r\\n\\r\\n #Load the last used profile\\r\\n rootnode = xmldoc.DocumentElement\\r\\n if rootnode.HasAttribute(\\\"LastUsed\\\"):\\r\\n lastused = rootnode.Attributes[\\\"LastUsed\\\"].Value.split(\\\",\\\")\\r\\n\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"Something seems to have gone wrong loading the xml file.\\\\n\\\\nThe error was:\\\\n\\\" + str(ex), \\\"Error loading file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n\\r\\n return profiles, lastused\",\n \"def load_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n if len(profiles) == 0:\\r\\n #Just in case\\r\\n profiles[\\\"Default\\\"] = Profile()\\r\\n profiles[\\\"Default\\\"].Name = \\\"Default\\\"\\r\\n #Some default templates\\r\\n profiles[\\\"Default\\\"].FileTemplate = \\\"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\\\"\\r\\n profiles[\\\"Default\\\"].FolderTemplate = \\\"{<publisher>}\\\\{<imprint>}\\\\{<series>}{ (<startyear>{ <format>})}\\\"\\r\\n \\r\\n if not lastused:\\r\\n lastused = [profiles.keys()[0]]\\r\\n \\r\\n return profiles, lastused\",\n \"def load(path):\\n\\n parser = ConfigParser()\\n parser.read(str(path))\\n\\n def _get(section, option):\\n try:\\n return parser.get(section, option)\\n except (NoSectionError, NoOptionError):\\n return None\\n\\n profiles = {}\\n\\n for section in parser.sections():\\n profiles[section] = Profile(\\n domain=_get(section, \\\"domain\\\"),\\n protocol=_get(section, \\\"protocol\\\"),\\n client_id=_get(section, \\\"client_id\\\"),\\n client_secret=_get(section, \\\"client_secret\\\"),\\n )\\n\\n return profiles\",\n \"def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\\n Dict[str, List[str]]]:\\n individuals = [[]]\\n sublist = 0\\n content = profiles_file.readlines()\\n for i in content:\\n if i != '\\\\n':\\n individuals[sublist].append(i)\\n else:\\n sublist += 1\\n individuals.append([])\\n return sort_profile(individuals)\",\n \"def loadProfiles():\\n with open(userProfilesDir, \\\"r\\\") as infile:\\n profiles = json.loads(\\\"\\\\n\\\".join(infile.readlines()))\\n infile.close()\\n return profiles\",\n \"def _read_profiles(profile_directory):\\n # Initialize key variables\\n profiles = defaultdict(\\n lambda: defaultdict(lambda: defaultdict()))\\n\\n # Read the yaml files in the profiles directory\\n files = os.listdir(profile_directory)\\n filenames = ['{}{}{}'.format(\\n profile_directory, os.sep, nextfile) for nextfile in files]\\n\\n for _filename in sorted(filenames):\\n # Get rid of excess os.sep separators\\n pathitems = _filename.split(os.sep)\\n filename = os.sep.join(pathitems)\\n\\n # Skip obvious\\n if os.path.isfile(filename) is False:\\n continue\\n if filename.lower().endswith('.yaml') is False:\\n continue\\n\\n with open(filename, 'r') as stream:\\n try:\\n _profiles = yaml.load(stream)['data']\\n except yaml.YAMLError as exc:\\n print(exc)\\n\\n # Create dictionary\\n for item in _profiles:\\n firstname = item['firstname']\\n lastname = item['lastname']\\n height = item['height']\\n weight = item['weight']\\n birthdate = item['birthdate']\\n profiles[lastname][firstname][birthdate] = {\\n 'height': height, 'weight': weight}\\n\\n return profiles\",\n \"def load_profile(path, profile):\\n profiles = load(path)\\n try:\\n return profiles[profile]\\n except KeyError:\\n return Profile(None, None, None, None)\",\n \"def get_profile(path=\\\"~\\\"):\\n global profiles\\n profile = profiles.get(path,None)\\n if not profile:\\n profile = InitFileConfig(os.path.join(path,\\\".myradioprofile\\\"), {} )\\n profiles[path] = profile\\n return profile\",\n \"def _recurse_load_profile(self, text, profile_path):\\n try:\\n inherited_profile = Profile()\\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\\n profile_parser = _ProfileParser(text)\\n # Iterate the includes and call recursive to get the profile and variables\\n # from parent profiles\\n for include in profile_parser.includes:\\n # Recursion !!\\n profile = self._load_profile(include, cwd)\\n inherited_profile.compose_profile(profile)\\n\\n # Current profile before update with parents (but parent variables already applied)\\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\\n inherited_profile)\\n return inherited_profile\\n except ConanException:\\n raise\\n except Exception as exc:\\n raise ConanException(\\\"Error parsing the profile text file: %s\\\" % str(exc))\",\n \"def _sloppy_parse_profiles (self, contents):\\n profile_start = contents.find('profiles\\\":')\\n profile_list_start = contents.find('profilesList')\\n if int(profile_start) > -1 and int(profile_list_start) > -1:\\n try:\\n try:\\n return json.loads('{\\\"a\\\":{\\\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\\n except ValueError, e:\\n return None\\n except TypeError, e:\\n return None\\n return None\",\n \"def populate_profiles_from_directory(self):\\n\\n self.profiles = []\\n\\n # Go over all the files and create a profile object\\n _profile_files = listdir(self.save_dir)\\n\\n for profile_filename in _profile_files:\\n # Only check for .yaml files\\n if path.splitext(profile_filename)[1] == '.yaml':\\n # Get the data and create a new profile\\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\\n _profile = Profile(path.splitext(profile_filename)[0])\\n _profile.player_data = _file_data\\n self.profiles.append(_profile)\",\n \"def load_people(self, file_path):\\n pass\",\n \"def parse_file(file_path):\\n with open(file_path) as f:\\n return XmlPropertyListParser().parse(f)\",\n \"def list_profiles(self) -> dict:\\n wsc = self.read_ws_configuration()\\n out = OrderedDict()\\n for name, json in wsc.profiles.items():\\n out[name] = Profile(name, self.ws_data_folder / name, json)\\n # Try to find current profile\\n try:\\n out[self.current_profile_name].is_current = True\\n except Exception:\\n pass\\n return out\",\n \"def from_file(cls, file_path, ngram_sizes, profile_len):\\n profile = cls.from_files((file_path, ), ngram_sizes, profile_len)\\n return profile\",\n \"def get_profiles(self):\\n # print(self.uir) #checkpoint\\n if os.path.isdir(self.uir+\\\"/profiles\\\"):\\n profiles=os.listdir(self.uir+\\\"/profiles\\\")\\n # print(profiles) #checkpoint\\n for profile in profiles:\\n wsadmin=self.uir+\\\"/profiles/\\\"+profile+\\\"/bin/wsadmin.bat\\\"\\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\\n self.profiles.append(self.uir+\\\"/profiles/\\\"+profile)\\n\\n else: print(self.uir+' Instance does not have \\\"profile\\\" folder in '+self.uir)\\n return\",\n \"def _load_profile(self, profile_name, cwd):\\n\\n profile_path = self.get_profile_path(profile_name, cwd)\\n try:\\n text = load_user_encoded(profile_path)\\n except Exception as e:\\n raise ConanException(f\\\"Cannot load profile:\\\\n{e}\\\")\\n\\n # All profiles will be now rendered with jinja2 as first pass\\n base_path = os.path.dirname(profile_path)\\n file_path = os.path.basename(profile_path)\\n context = {\\\"platform\\\": platform,\\n \\\"os\\\": os,\\n \\\"profile_dir\\\": base_path,\\n \\\"profile_name\\\": file_path,\\n \\\"conan_version\\\": conan_version}\\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\\n text = rtemplate.render(context)\\n\\n try:\\n return self._recurse_load_profile(text, profile_path)\\n except ConanException as exc:\\n raise ConanException(\\\"Error reading '%s' profile: %s\\\" % (profile_name, exc))\",\n \"def read_plist(path: str) -> dict:\\n return _read_plist(path, plistlib.FMT_XML)\",\n \"def save_profiles(file_path, profiles, lastused=\\\"\\\"):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n writer.WriteStartElement(\\\"Profiles\\\")\\r\\n if lastused:\\r\\n writer.WriteAttributeString(\\\"LastUsed\\\", \\\",\\\".join(lastused))\\r\\n for profile in profiles:\\r\\n profiles[profile].save_to_xml(writer)\\r\\n writer.WriteEndElement()\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def profiles(self):\\n if not self._profiles:\\n self.GetAllProfiles()\\n return self._profiles\",\n \"def get_profiles(profile_file_directory):\\r\\n\\t\\r\\n\\tprofile_file_path = profile_file_directory+ \\\"/profiles.txt\\\"\\r\\n\\tlist_of_all_allele_numbers_tuple = []\\r\\n\\tdatabase = None\\r\\n\\tlocusList = []\\r\\n\\t\\r\\n\\tfor l in open(profile_file_path):\\r\\n\\t if database is None:\\r\\n\\t\\tdatabase = {}\\r\\n\\t\\tlocusList = l.split()[1:]\\r\\n\\t\\tcontinue\\r\\n\\t t = l.split()\\r\\n\\t st = t[0]\\r\\n\\t v = ' '.join([s for s in t[1:]])\\r\\n\\t if v in database:\\r\\n\\t\\tprint >> sys.stderr, 'sequence type ' + str(st) + ' is a duplicate of ' + str(database[v])\\r\\n\\t database[v] = st\\r\\n\\t covert_string_to_tuple_list_of_allele_numbers = tuple(int(x) for x in re.findall(\\\"[0-9]+\\\", v)) \\r\\n\\t list_of_all_allele_numbers_tuple.append(covert_string_to_tuple_list_of_allele_numbers)\\r\\n\\t\\t\\r\\n\\treturn (database, locusList, list_of_all_allele_numbers_tuple)\",\n \"def _load_data(self, filename):\\n if not os.path.isfile(filename):\\n return False\\n\\n with open(filename) as f:\\n data = pickle.load(f)\\n if data:\\n self.profiles = data['profiles']\\n self.user_data = data['user_data']\\n self.api_data = data['api_data']\\n else:\\n return False\",\n \"def profile(filename: str) -> 'Iterator[None]':\\n profiler = Profile()\\n profiler.enable()\\n\\n yield\\n\\n profiler.disable()\\n profiler.create_stats()\\n profiler.dump_stats('profiles/{}'.format(filename))\",\n \"def loadProfile(fname):\\n \\n x = np.loadtxt(fname)\\n return x[:,1]\",\n \"def get_user():\\n with open(app.config['DATA_XML'], 'r') as xmlfile:\\n root = ElementTree.parse(xmlfile).getroot()\\n\\n for item in root.iter('server'):\\n result = '{}://{}'.format(\\n item.find('protocol').text,\\n item.find('host').text\\n )\\n\\n data = {\\n user.attrib['id']: {\\n 'name': user.find('name').text,\\n 'avatar': '{}{}'.format(\\n result,\\n user.find('avatar').text\\n )\\n }\\n for user in root.iter('user')\\n }\\n return OrderedDict(\\n sorted(\\n data.items(),\\n key=lambda result: itemgetter('name')(itemgetter(1)(result)),\\n cmp=locale.strcoll\\n )\\n )\",\n \"def GetAllProfiles(self):\\n profiles = []\\n feed_uri = self._gd_client.GetFeedUri('profiles')\\n while feed_uri:\\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\\n profiles.extend(feed.entry)\\n feed_uri = feed.FindNextLink()\\n self._profiles = profiles\",\n \"def ReadWiredNetworkProfile(self, profilename):\\n profile = {}\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n for x in config.options(profilename):\\n profile[x] = misc.Noneify(config.get(profilename, x))\\n profile['use_global_dns'] = bool(profile.get('use_global_dns'))\\n profile['use_static_dns'] = bool(profile.get('use_static_dns'))\\n self.WiredNetwork = profile\\n return \\\"100: Loaded Profile\\\"\\n else:\\n self.WiredNetwork = None\\n return \\\"500: Profile Not Found\\\"\",\n \"def profiles_path(self) -> Path:\\n return self._config.data_path / \\\"hmm\\\" / \\\"profiles.hmm\\\"\",\n \"def read_pardus_profiles(self):\\n\\n self.lan_config = ConfigParser.ConfigParser()\\n self.lan_config.read(self.lan_config_path)\\n connection_type = \\\"802-3-ethernet\\\"\\n for section in self.lan_config.sections():\\n lan_settings = {}\\n for option in self.lan_config.options(section):\\n if option == \\\"device\\\":\\n #To strip device name from long device string\\n lan_settings[option] = self.lan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n lan_settings[option] = self.lan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, lan_settings)\\n self.pardus_profiles.append(p)\\n\\n self.wlan_config = ConfigParser.ConfigParser()\\n self.wlan_config.read(self.wlan_config_path)\\n connection_type = \\\"802-11-wireless\\\"\\n for section in self.wlan_config.sections():\\n wlan_settings = {}\\n for option in self.wlan_config.options(section):\\n if option == \\\"device\\\":\\n wlan_settings[option] = self.wlan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n wlan_settings[option] = self.wlan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\\n self.pardus_profiles.append(p)\",\n \"def have_profile_dir(path, maxdepth=3, filename=\\\"profiles.desc\\\"):\\n\\twhile path != \\\"/\\\" and maxdepth:\\n\\t\\tif os.path.exists(os.path.join(path, \\\"profiles\\\", filename)):\\n\\t\\t\\treturn normalize_path(path)\\n\\t\\tpath = normalize_path(path + \\\"/..\\\")\\n\\t\\tmaxdepth -= 1\",\n \"def read_file(filepath: str) -> dict:\\n if not filepath.endswith(\\\".csv\\\"):\\n raise RuntimeError(\\\"File extension must be .csv\\\")\\n\\n people = {}\\n with open(filepath) as csv:\\n for line in csv:\\n email, person = Parser.parse_line(line.rstrip(\\\"\\\\n\\\"))\\n if email not in people:\\n people[email] = person\\n else:\\n print(\\\"Ignoring person with duplicate email {}\\\".format(email))\\n return people\",\n \"def get_resolved_profile_catalog(trestle_root: pathlib.Path, profile_path: pathlib.Path) -> cat.Catalog:\\n logger.debug(f'get resolved profile catalog for {profile_path} via generated Import.')\\n import_ = prof.Import(href=str(profile_path), include_all={})\\n import_filter = ProfileResolver.Import(trestle_root, import_)\\n logger.debug('launch pipeline')\\n result = next(import_filter.process())\\n return result\",\n \"def profile_directory_path(request, file):\\n return directory_path('profile', file)\",\n \"def get_profile(self, profiles, settings=None, options=None, conf=None, cwd=None):\\n assert isinstance(profiles, list), \\\"Please provide a list of profiles\\\"\\n cache = ClientCache(self._conan_api.cache_folder)\\n loader = ProfileLoader(cache)\\n profile = loader.from_cli_args(profiles, settings, options, conf, cwd)\\n profile.conf.validate()\\n cache.new_config.validate()\\n # Apply the new_config to the profiles the global one, so recipes get it too\\n profile.conf.rebase_conf_definition(cache.new_config)\\n return profile\",\n \"def loadPerson(fileid):\\n dinf = {}\\n drel = {}\\n root = etree.Element(\\\"person\\\")\\n text = None\\n # TODO: put this in a global variable, and make a function to populate it from the DTD.\\n tags = [\\\"commonname\\\", \\\"ctitle\\\", \\\"gname\\\", \\\"mname\\\", \\\"fname\\\", \\\"nname\\\", \\\"nameorder\\\", \\\"gender\\\", \\\"bday\\\", \\\"dday\\\", \\\"stories\\\", \\\"mention\\\", \\\"appear1ch\\\", \\\"appear1wr\\\", \\\"conflict\\\", \\\"leadrel\\\", \\\"bodytyp\\\", \\\"age\\\", \\\"skin\\\", \\\"eyes\\\", \\\"hair\\\", \\\"dmarks\\\", \\\"dress\\\", \\\"attposs\\\", \\\"asmell\\\", \\\"personality\\\", \\\"speech\\\", \\\"formocc\\\", \\\"currocc\\\", \\\"strength\\\", \\\"weak\\\", \\\"mole\\\", \\\"hobby\\\", \\\"misc\\\", \\\"ethnic\\\", \\\"origin\\\", \\\"backstory\\\", \\\"residence\\\", \\\"minchar\\\", \\\"talent\\\", \\\"abil\\\", \\\"sgoal\\\", \\\"other\\\", \\\"relat\\\", \\\"aspects\\\", \\\"update\\\"]\\n tags.remove(\\\"currocc\\\")\\n tags.remove(\\\"formocc\\\")\\n tags.remove(\\\"relat\\\")\\n tags.append(\\\"file\\\")\\n for tag in tags:\\n dinf[tag] = [\\\"\\\",False]\\n dinf['currocc'] = {}\\n dinf['currocc']['pos'] = [\\\"\\\",False]\\n dinf['formocc'] = {}\\n dinf['formocc']['pos'] = [\\\"\\\",False]\\n dinf['aspects'] = {}\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n dinf['formocc']['events'] = events\\n if not idExists(fileid):\\n status.push(0,\\\"new person created... '%s'\\\" % fileid)\\n return (dinf,drel)\\n fn = os.path.join(config['realmdir'],fileid + \\\".xml\\\")\\n status.push(0,\\\"loading person from XML... '%s'\\\" % fn)\\n try:\\n with codecs.open(fn,'rU','utf-8') as f:\\n tree = etree.parse(f)\\n f.close()\\n root = tree.getroot()\\n except IOError as e:\\n print \\\" Could not open configuration file: %s\\\" % e\\n\\n ir = 0\\n for i in range(len(root)):\\n if root[i].tag is not None:\\n if root[i].tag == \\\"relat\\\":\\n if len(root[i]) > 0:\\n node = \\\"\\\"\\n node = root[i].find(\\\"file\\\")\\n if node is not None and node.text:\\n node = node.text.strip()\\n node = common.validateFileid(node)\\n drel[node] = {}\\n for j in root[i]:\\n if j.tag == \\\"events\\\":\\n if not drel[node].get('events'): drel[node]['events'] = {}\\n for k in j:\\n stone = str(len(drel[node]['events']))\\n drel[node]['events'][stone] = {}\\n for m in k:\\n if m.tag and m.text:\\n drel[node]['events'][stone][m.tag] = [m.text.strip(),False]\\n else: # elif j.tag != \\\"file\\\":\\n if j.tag and j.text:\\n drel[node][j.tag] = [j.text.strip(),False]\\n if config['debug'] > 3: print drel[node]\\n else:\\n if config['debug'] > 0:\\n print \\\"Invalid relat tag:\\\"\\n for c in root[i]:\\n print c.tag\\n else: # no relat length\\n if config['debug'] > 0: print \\\"Empty relat tag.\\\"\\n elif root[i].tag == \\\"currocc\\\":\\n# print \\\",\\\",\\n dinf['currocc'] = {}\\n try:\\n dinf['currocc']['pos'] = [root[i].find(\\\"pos\\\").text.strip(),False]\\n except AttributeError:\\n del dinf['currocc']\\n if dinf.get('currocc'):\\n events = {}\\n if len(root[i]) > 1:\\n for j in root[i]:\\n if j.tag is not None:\\n if j.tag == \\\"events\\\":\\n for k in j:\\n if k.tag == \\\"mstone\\\":\\n le = str(len(events))\\n events[le] = {}\\n events[le]['date'] = [\\\"\\\",False]\\n events[le]['event'] = [\\\"\\\",False]\\n for m in k:\\n if m.tag and m.text:\\n events[le][m.tag] = [m.text.strip(),False]\\n else:\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n else:\\n dinf['currocc'] = {}\\n dinf['currocc']['pos'] = [\\\"\\\",False]\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['currocc']['events'] = events\\n elif root[i].tag == \\\"formocc\\\":\\n# print \\\",\\\",\\n dinf['formocc'] = {}\\n try:\\n dinf['formocc']['pos'] = [root[i].find(\\\"pos\\\").text.strip(),False]\\n except AttributeError:\\n del dinf['formocc']\\n if dinf.get('formocc'):\\n events = {}\\n if len(root[i]) > 1:\\n for j in root[i]:\\n if j.tag is not None:\\n if j.tag == \\\"events\\\":\\n for k in j:\\n if k.tag == \\\"mstone\\\":\\n le = str(len(events))\\n events[le] = {}\\n events[le]['date'] = [\\\"\\\",False]\\n events[le]['event'] = [\\\"\\\",False]\\n for m in k:\\n if m.tag and m.text:\\n events[le][m.tag] = [m.text.strip(),False]\\n else:\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['formocc']['events'] = events\\n else:\\n dinf['formocc'] = {}\\n dinf['formocc']['pos'] = [\\\"\\\",False]\\n events = {}\\n events['0'] = {}\\n events['0']['date'] = [\\\"\\\",False]\\n events['0']['event'] = [\\\"\\\",False]\\n dinf['formocc']['events'] = events\\n elif root[i].tag == \\\"aspects\\\":\\n if len(root[i]) > 0:\\n nodes = root[i]\\n for node in nodes:\\n k = str(len(dinf['aspects']))\\n dinf['aspects'][k] = {}\\n if node.tag and node.text:\\n dinf['aspects'][k] = [node.text.strip(),False]\\n else:\\n if config['debug'] > 0:\\n print \\\"Invalid aspects tag:\\\"\\n print node.tag + ': ' + node.text,\\n else: # no aspects length\\n if config['debug'] > 0: print \\\"Empty aspects tag.\\\"\\n elif root[i].text is not None:\\n# print \\\".\\\",\\n dinf[root[i].tag] = [root[i].text.strip(), False]\\n if config['debug'] > 2: print str(i) + \\\" \\\",\\n# print str(dinf)\\n return (dinf,drel)\",\n \"def read_config(profile):\\n p = Path(CONFIG_FILE_PATH).expanduser()\\n p.parent.mkdir(parents=True, exist_ok=True)\\n config = configparser.ConfigParser()\\n config.read(str(p))\\n\\n if config.has_section(profile):\\n return dict(config.items(profile))\\n else:\\n return {}\",\n \"def parse_xml(file: Path):\\n check_file(file) # Check the existency of the file\\n\\n doc = ET.parse(file)\\n data = parse_xml_tree(doc.getroot())\\n return data\",\n \"def _find_cb_profiles():\\n dir_locations = [\\\".carbonblack\\\", os.path.join(os.path.expanduser(\\\"~\\\"), \\\".carbonblack\\\")]\\n cred_file = \\\"credentials.response\\\"\\n profiles = []\\n\\n for dir in dir_locations:\\n cred_file_path = os.path.join(dir, cred_file)\\n _MOD_LOGGER.debug(\\\"Searching CB profiles on '%s'\\\", cred_file_path)\\n if os.path.exists(cred_file_path):\\n _MOD_LOGGER.debug(\\\"File exists, parsing...\\\")\\n config = configparser.ConfigParser(default_section=\\\"cbbackend\\\", strict=True)\\n config.read(cred_file_path)\\n profiles += [sec_name for sec_name in config.keys() if sec_name != \\\"cbbackend\\\"]\\n\\n if profiles:\\n _MOD_LOGGER.debug(\\\"Requested to read 'all' profiles. Found: %s\\\", \\\",\\\".join(profiles))\\n\\n return profiles\",\n \"def _read_profile_file(cls, profile_file):\\n qid2title: Dict[str, str] = {}\\n qid2desc: Dict[str, str] = {}\\n alias2qids: Dict[str, list] = {}\\n type_systems: Dict[str, Dict[str, List[str]]] = {}\\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\\n\\n num_lines = sum(1 for _ in open(profile_file))\\n with open(profile_file, \\\"r\\\") as in_f:\\n for line in tqdm(in_f, total=num_lines, desc=\\\"Reading profile\\\"):\\n line = ujson.loads(line)\\n\\n # Check keys and schema\\n assert all(\\n k in line.keys() for k in REQUIRED_KEYS\\n ), f\\\"A key from {REQUIRED_KEYS} was not in {line}\\\"\\n try:\\n # Asserts the types are correct\\n ent = EntityObj(\\n entity_id=line[\\\"entity_id\\\"],\\n mentions=line[\\\"mentions\\\"],\\n title=line.get(\\\"title\\\", line[\\\"entity_id\\\"]),\\n description=line.get(\\\"description\\\", \\\"\\\"),\\n types=line.get(\\\"types\\\", {}),\\n relations=line.get(\\\"relations\\\", []),\\n )\\n except ValidationError as e:\\n print(e.json())\\n raise e\\n if ent.entity_id in qid2title:\\n raise ValueError(f\\\"{ent.entity_id} is already in our dump\\\")\\n qid2title[ent.entity_id] = ent.title\\n qid2desc[ent.entity_id] = ent.description\\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\\n for men_pair in ent.mentions:\\n # Lower case mentions for mention extraction\\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\\n if new_men not in alias2qids:\\n alias2qids[new_men] = []\\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\\n # Add type systems of type_sys -> QID -> list of type names\\n for type_sys in ent.types:\\n if type_sys not in type_systems:\\n type_systems[type_sys] = {}\\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\\n # Add kg relations QID -> relation -> list of object QIDs\\n for rel_pair in ent.relations:\\n if \\\"relation\\\" not in rel_pair or \\\"object\\\" not in rel_pair:\\n raise ValueError(\\n \\\"For each value in relations, it must be a JSON with keys relation and object\\\"\\n )\\n if ent.entity_id not in qid2relations:\\n qid2relations[ent.entity_id] = {}\\n if rel_pair[\\\"relation\\\"] not in qid2relations[ent.entity_id]:\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]] = []\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]].append(\\n rel_pair[\\\"object\\\"]\\n )\\n\\n # Sort mentions based on score, highest first\\n for al in list(alias2qids.keys()):\\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\\n # Add all qids to the type systems and KG connections with empty values\\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\\n for qid in qid2title:\\n for type_sys in type_systems:\\n if qid not in type_systems[type_sys]:\\n type_systems[type_sys][qid] = []\\n if qid not in qid2relations:\\n qid2relations[qid] = {}\\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations\",\n \"def profile_files(profile):\\n flist = os.listdir(osp.join(profile, 'startup'))\\n profile_path = osp.join(osp.abspath('.'), profile)\\n return [osp.join(profile_path, 'startup', x) for x in flist]\",\n \"def profiles_names(self):\\n url = get_url('profiles')\\n response = self._get(url)\\n raise_on_error(response)\\n return response.json()\",\n \"def profiles(self):\\n return self._profiles\",\n \"def profiles(self):\\n return self._profiles\",\n \"def read_profiles(filename):\\n profiles = []\\n with gzip.open(filename, mode='rt', encoding='utf8') as infile:\\n for line in infile:\\n profiles.append(Counter(line.split()))\\n return profiles\",\n \"def loadProfilesFromPlist_(self, plist):\\r\\n \\r\\n LogInfo(u\\\"Loading update profiles with PublicationDate %@\\\", plist[u\\\"PublicationDate\\\"])\\r\\n self.profiles = dict()\\r\\n for name, updates in plist[u\\\"Profiles\\\"].iteritems():\\r\\n profile = list()\\r\\n for update in updates:\\r\\n profile.append(plist[u\\\"Updates\\\"][update])\\r\\n self.profiles[name] = profile\\r\\n self.publicationDate = plist[u\\\"PublicationDate\\\"]\\r\\n self.updatePaths = dict()\\r\\n for name, update in plist[u\\\"Updates\\\"].iteritems():\\r\\n filename, ext = os.path.splitext(os.path.basename(update[u\\\"url\\\"]))\\r\\n self.updatePaths[update[u\\\"sha1\\\"]] = u\\\"%s(%s)%s\\\" % (filename, update[u\\\"sha1\\\"][:7], ext)\\r\\n self.deprecatedInstallerBuilds = dict()\\r\\n try:\\r\\n for replacement, builds in plist[u\\\"DeprecatedInstallers\\\"].iteritems():\\r\\n for build in builds:\\r\\n self.deprecatedInstallerBuilds[build] = replacement\\r\\n except KeyError:\\r\\n LogWarning(u\\\"No deprecated installers\\\")\\r\\n if self.delegate:\\r\\n self.delegate.profilesUpdated()\",\n \"def parsePlayerProfile(path, args):\\n soup = BeautifulSoup(requests.get(\\\"{domain}/{pathStr}\\\".format(domain=args.domain[0], pathStr=path)).text, \\\"html.parser\\\")\\n\\n playerPathName = re.split(\\\"/|\\\\.\\\", path.lower())[-2]\\n # set up so a=11, b=12, etc, then joins the values together\\n playerID = \\\"\\\".join([char if char.isdigit() else str(ord(char) % 86) for char in playerPathName])\\n birthday = soup.find(\\\"span\\\", id=\\\"necro-birth\\\").get(\\\"data-birth\\\")\\n hasDebuted = soup.find(\\\"a\\\", string=\\\"Debut\\\")\\n debut = isRetired.get(\\\"href\\\") if hasDebuted else None\\n isRetired = soup.find(\\\"a\\\", string=\\\"Last Game\\\")\\n retired = isRetired.get(\\\"href\\\") if isRetired else None\\n\\n player = parseProfileHeader(soup, playerID, birthday, debut, retired, args)\\n\\n positionTable = soup.find(\\\"table\\\", id=\\\"standard_fielding\\\")\\n positions = parsePositionInfo(positionTable, args)\\n player.addPositions(positions)\\n\\n hittingTable = soup.find(\\\"table\\\", id=\\\"batting_standard\\\")\\n hittingStats = parseBattingTable(hittingTable, args)\\n player.addStats(hittingStats, \\\"batting\\\")\\n\\n pitchingTable = soup.find(\\\"table\\\", id=\\\"pitching_standard\\\")\\n advancedPathTag = soup.find(\\\"a\\\", href=re.compile(r\\\"-pitch.shtml\\\"), string=\\\"More Stats\\\")\\n pitchingStats = parsePitchingTables(pitchingTable, advancedPathTag, args)\\n player.addStats(pitchingStats, \\\"pitching\\\")\\n\\n return player\",\n \"def parse_profiles(profiles):\\n if type(profiles) == list:\\n return [\\n x\\n for x in [ProfileSettingParser.parse(x) for x in profiles]\\n if x is not None\\n ]\\n else:\\n return []\",\n \"def load(self, config, profile_key):\\n # a none numeric argument is supposed to be 'alias'\\n if re.search(r\\\"\\\\b[0-9]+\\\\b\\\", profile_key) is None:\\n key = profile_key\\n self.alias = key\\n access_by = \\\"alias\\\"\\n else:\\n key = (int)(profile_key)\\n self.id = key\\n access_by = \\\"id\\\"\\n self.logger.debug(\\\"==> profile will be access by his '%s'\\\" % access_by)\\n\\n # check if the profile exists into config...\\n i = 0\\n profile_found = False\\n while (i < len(config[\\\"profiles\\\"])\\n and profile_found is False):\\n\\n if config[\\\"profiles\\\"][i][access_by] == key:\\n profile_found = True\\n self.logger.debug(\\\"==> profile '%s' found (access by '%s')\\\" %\\n (key, access_by))\\n self.config = config[\\\"profiles\\\"][i]\\n\\n i += 1\\n\\n # ... and if not raise an exception\\n if profile_found is False:\\n raise ProfileLoadError(\\\"profile '%s' not found\\\" % profile_key)\\n\\n # set profile properties\\n self.id = self.config[\\\"id\\\"]\\n self.alias = self.config[\\\"alias\\\"]\\n self.state_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".json\\\")\\n self.lock_file = os.path.join(config[\\\"state_dir\\\"],\\n self.config[\\\"alias\\\"] + \\\".lock\\\")\\n\\n # set the todo queue limit based on configuration\\n # - default is 1\\n try:\\n todo_limit_queue = self.config[\\\"todo_queue_limit\\\"]\\n self.todo_queue_limit = todo_limit_queue\\n except KeyError:\\n self.todo_queue_limit = 1\",\n \"def get_profiles(args):\\n # Use profile from cli if provided\\n if args.profile and not args.update_config:\\n return [args.profile]\\n\\n # Run config to get or set the config file\\n config = configparser.ConfigParser()\\n\\n if os.path.isfile(SETTINGS_FILE) and not args.update_config:\\n # Get profiles from config\\n config.read(SETTINGS_FILE)\\n else:\\n # Get default profiles from user\\n try:\\n profiles_input = input(\\n 'Please enter space separated list of profiles to use: '\\n )\\n except KeyboardInterrupt:\\n # Avoid ugly stacktrace on ctrl-c in input\\n sys.exit(1)\\n # Setup config\\n config.add_section('profiles')\\n config.set('profiles', 'default', profiles_input)\\n # Write to config\\n config_file = open(SETTINGS_FILE, 'w')\\n config.write(config_file)\\n config_file.close()\\n\\n return config.get('profiles', 'default').split()\",\n \"def parse_xml_file(self, filename):\\n try:\\n dom = parse(filename)\\n process_includes(dom)\\n except ExpatError, x:\\n raise EzXMLError(\\\"Failed to parse: %s\\\" % x)\\n return self.walkdom(dom.documentElement)\",\n \"def get_profiles(self):\\n profiles = [['Profile name', 'GUID']]\\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\\n for row in r.fetchall():\\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\\n return profiles\",\n \"def list_profiles(self, params):\\n return self.profiles\",\n \"def parse_manifest_xml(manifest_path):\\n dir_project_dict = {}\\n parsed_xml = xml.dom.minidom.parse(manifest_path)\\n projects = parsed_xml.getElementsByTagName('project')\\n for project in projects:\\n name = project.getAttribute('name')\\n path = project.getAttribute('path')\\n if path:\\n dir_project_dict[path] = name\\n else:\\n dir_project_dict[name] = name\\n return dir_project_dict\",\n \"def read_topics_from_xml(file, topics_dict):\\n\\n # Read XML file\\n xml = read_file(file)\\n\\n # Pass to HTML\\n html = BeautifulSoup(xml, \\\"html.parser\\\")\\n\\n # Get topics\\n topics = html.findAll('topic')\\n for topic in topics:\\n # Get topic number + query and description\\n topic_number = int(topic['number'])\\n query = [topic.query.getText().strip(),\\n topic.description.getText().strip()]\\n topics_dict[topic_number]['query'] = query\\n\\n # Get subtopics\\n subtopics = topic.findAll('subtopic')\\n topics_dict[topic_number]['subtopics'] = [x.getText().strip() for x in subtopics]\\n\\n return topics_dict\",\n \"def get_list(profiles_folder, logger):\\n profile_list = []\\n with scandir(profiles_folder) as it:\\n for entry in it:\\n if entry.is_file():\\n filepath = profiles_folder + entry.name\\n profile = json_from_file(filepath, logger)\\n if profile is not None:\\n try:\\n profile_list.append({\\\"filepath\\\":filepath, \\\"name\\\":profile[\\\"name\\\"], \\\"description\\\":profile[\\\"description\\\"]})\\n except AttributeError:\\n logger.error(\\\"Missing attributes in \\\" + filepath)\\n logger.error(str(profile))\\n return profile_list\",\n \"def apply(self):\\r\\n\\r\\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\\r\\n\\r\\n def saving_file(xml):\\r\\n \\\"\\\"\\\" Save user profile in xml format to temp_ dir.\\\"\\\"\\\"\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\\r\\n\\r\\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\\r\\n \\\"/data/sampleProfile.xml\\\")\\r\\n\\r\\n # The below code will parse the sample xml file\\r\\n # and fill important details entered by the user.\\r\\n root_tree = parse_xml.getroot()\\r\\n root_tree[0].text = self.ssid.get()\\r\\n root_tree[1][0][0].text = self.ssid.get()\\r\\n root_tree[3].text = self.connection_mode.get().lower()\\r\\n security = root_tree[4][0]\\r\\n security[0][0].text = self.authentication.get()\\r\\n security[0][1].text = self.encryption.get()\\r\\n if self.authentication.get() != \\\"open\\\":\\r\\n etree.SubElement(security, \\\"sharedKey\\\")\\r\\n etree.SubElement(security[1], \\\"keyType\\\").text = \\\"passPhrase\\\"\\r\\n etree.SubElement(security[1], \\\"protected\\\").text = \\\"false\\\"\\r\\n etree.SubElement(security[1], \\\"keyMaterial\\\").text = self.password.get()\\r\\n\\r\\n # Save the xml file\\r\\n saving_file(root_tree)\\r\\n\\r\\n # Add profile to the system.\\r\\n temp_path = 'netsh wlan add profile filename=\\\"' + self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\"\\r\\n output_ = subprocess.run(temp_path + file_name + '.xml\\\"', shell=True,\\r\\n capture_output=True, text=True)\\r\\n os.remove(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\")\\r\\n\\r\\n # If unable to add profile.\\r\\n if output_.returncode != 0:\\r\\n message = \\\"Sorry, Unable to add profile.\\\\n(You entered wrong details \\\" \\\\\\r\\n \\\"or else you don't have admin rights.)\\\"\\r\\n image_ = \\\"error\\\"\\r\\n\\r\\n else:\\r\\n message = \\\"Profile added successfully (Please Refresh)\\\"\\r\\n image_ = \\\"warning\\\"\\r\\n\\r\\n MessageBox(self.parent, message, image_)\",\n \"def load(cls, filepath):\\n return BaseProfiler.load(filepath)\",\n \"def load(cls, filepath):\\n # Load profile from disk\\n with open(filepath, \\\"rb\\\") as infile:\\n data = pickle.load(infile)\\n\\n # remove profiler class if it exists\\n profiler_class = data.pop('profiler_class', None)\\n\\n # if the user didn't load from the a given profiler class, we need\\n # to determine which profiler is being loaded.\\n profiler_cls = cls\\n if cls is BaseProfiler:\\n if profiler_class == 'StructuredProfiler':\\n profiler_cls = StructuredProfiler\\n elif profiler_class == 'UnstructuredProfiler':\\n profiler_cls = UnstructuredProfiler\\n elif profiler_class is None: # deprecated case\\n profiler_cls = StructuredProfiler\\n if '_empty_line_count' in data:\\n profiler_cls = UnstructuredProfiler\\n else:\\n raise ValueError(f'Invalid profiler class {profiler_class} '\\n f'failed to load.')\\n\\n profile_options = profiler_cls._option_class()\\n profile_options.data_labeler.is_enabled = False\\n profiler = profiler_cls(None, options=profile_options)\\n\\n for key in data:\\n setattr(profiler, key, data[key])\\n\\n # Restore all data labelers\\n profiler._restore_data_labelers()\\n return profiler\",\n \"def find_prefs():\\n conf = SafeConfigParser()\\n try:\\n conf.read(PROFILES)\\n except Exception as err:\\n log.error('reading profiles.ini: %s', err)\\n return None\\n\\n for section in conf.sections():\\n if conf.has_option(section, 'Name') and \\\\\\n conf.get(section, 'Name') == 'default':\\n path = conf.get(section, 'Path')\\n if conf.getboolean(section, 'IsRelative'):\\n path = os.path.join(CONFDIR, path)\\n\\n return unicodify(os.path.join(path, 'prefs.js'))\\n\\n return None\",\n \"def get_profile_info(self):\\n\\n drill_path = str(Path.home())+\\\"/Documents/ball_e_profiles/drill_profiles/{drill_name}/{drill_name}.csv\\\".format(\\n drill_name=self.drill_name)\\n with open(drill_path) as file:\\n csv_reader = csv.reader(file, delimiter=',')\\n row_count = 0\\n info_dict = dict()\\n for row in csv_reader:\\n if row_count == 0:\\n row_count += 1\\n else:\\n info_dict[row[0]] = [row[1], row[2], row[3]]\\n row_count += 1\\n\\n return info_dict\",\n \"def load_profile(dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"rb\\\") as f:\\n logger = pickle.load(f)\\n return logger\",\n \"def profiles(self):\\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\\n return list(filter(lambda x: x is not None, executor.map(self.profile_details, self.profiles_names())))\",\n \"def profile(self) -> dict:\\n endpoint = \\\"/api/users/profile/\\\"\\n ret = self._request(endpoint=endpoint)\\n return ret\",\n \"def save_profile(file_path, profile):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n profile.save_to_xml(writer)\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def Load(self, filename):\\n logging.info(\\\"Reading users file at %s\\\", filename)\\n try:\\n try:\\n contents = utils.ReadFile(filename)\\n except EnvironmentError as err:\\n self._users = None\\n if err.errno == errno.ENOENT:\\n logging.warning(\\\"No users file at %s\\\", filename)\\n else:\\n logging.warning(\\\"Error while reading %s: %s\\\", filename, err)\\n return False\\n\\n users = http.auth.ParsePasswordFile(contents)\\n\\n except Exception as err: # pylint: disable=W0703\\n # We don't care about the type of exception\\n logging.error(\\\"Error while parsing %s: %s\\\", filename, err)\\n return False\\n\\n self._users = users\\n\\n return True\",\n \"def profiles():\\n images = get_uploaded_images()\\n records = db.session.query(UserProfile).all()\\n return render_template('profiles.html', images=images, records =records)\",\n \"def _parse_profile_data (self, netflix_page_data):\\n profiles = {};\\n important_fields = [\\n 'profileName',\\n 'isActive',\\n 'isAccountOwner',\\n 'isKids'\\n ]\\n # values are accessible via dict (sloppy parsing successfull)\\n if type(netflix_page_data) == dict:\\n for profile_id in netflix_page_data.get('profiles'):\\n if self._is_size_key(key=profile_id) == False and type(netflix_page_data['profiles'][profile_id]) == dict and netflix_page_data['profiles'][profile_id].get('avatar', False) != False:\\n profile = {'id': profile_id}\\n for important_field in important_fields:\\n if important_field == 'profileName':\\n profile.update({important_field: HTMLParser.HTMLParser().unescape(netflix_page_data['profiles'][profile_id]['summary'][important_field]).encode('utf8')})\\n else:\\n profile.update({important_field: netflix_page_data['profiles'][profile_id]['summary'][important_field]})\\n avatar_base = netflix_page_data['nf'].get(netflix_page_data['profiles'][profile_id]['summary']['avatarName'], False);\\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\\n profile.update({'avatar': avatar, 'isFirstUse': False})\\n profiles.update({profile_id: profile})\\n return profiles\\n\\n # values are stored in lists (returned from JS parser)\\n # TODO: get rid of this christmas tree of doom\\n for item in netflix_page_data:\\n if 'hasViewedRatingWelcomeModal' in dict(item).keys():\\n for profile_id in item:\\n if self._is_size_key(key=profile_id) == False and type(item[profile_id]) == dict and item[profile_id].get('avatar', False) != False:\\n profile = {'id': profile_id}\\n for important_field in important_fields:\\n profile.update({important_field: item[profile_id]['summary'][important_field]})\\n avatar_base = item['nf'].get(item[profile_id]['summary']['avatarName'], False);\\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\\n profile.update({'avatar': avatar})\\n profiles.update({profile_id: profile})\\n return profiles\",\n \"def read_conf_file(self, file_path):\\n ambari_props = {}\\n if os.path.exists(file_path):\\n with open(file_path, \\\"r\\\") as f:\\n lines = f.readlines()\\n if lines:\\n Logger.debug(\\\"Reading file {0}, has {1} lines.\\\".format(file_path, len(lines)))\\n for l in lines:\\n l = l.strip()\\n if l.startswith(\\\"#\\\"):\\n continue\\n parts = l.split(\\\"=\\\")\\n if len(parts) >= 2:\\n prop = parts[0]\\n value = \\\"\\\".join(parts[1:])\\n ambari_props[prop] = value\\n return ambari_props\",\n \"def dumpProfiles(profiles):\\n with open(userProfilesDir, \\\"w\\\") as outfile:\\n outfile.writelines(json.dumps(profiles, indent=4))\\n outfile.close()\",\n \"def parse_profile_list_page(page):\\n page_tree = etree.HTML(page)\\n profile_list = page_tree.xpath(\\n '//table[@class=\\\"table_header\\\"]/tr[position() > 4 and position() < (last() - 3)]/td/node()[1]')\\n if profile_list:\\n profile_list[0] = \\\"main\\\"\\n return [text.strip() for text in profile_list]\\n else:\\n raise ProfileException(\\\"Failed to get profile list\\\")\",\n \"def addon_profiles(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input['AddonProfilesArgs']]]]:\\n return pulumi.get(self, \\\"addon_profiles\\\")\",\n \"def profiles(self, profiles):\\n\\n self._profiles = profiles\",\n \"def read_file(file_path):\\n\\n output_dict = dict()\\n try:\\n if os.path.exists(file_path):\\n with open(file_path) as fd:\\n output = fd.readlines()\\n for idx in range(len(output)):\\n key_info = output[idx].split('=')[0].strip()\\n value_info = output[idx].split('=')[1].strip()\\n output_dict[key_info] = value_info\\n return output_dict\\n except Exception as e:\\n SysTools.logger.warning(\\\"Read file:%s failed, reason:%s\\\" % (file_path, str(e)))\",\n \"def read_file(fileName):\\r\\n\\r\\n xmlFile = open(fileName, 'r', encoding='utf-8')\\r\\n\\r\\n xmlContent = xmlFile.read()\\r\\n dictContent = xmltodict.parse(xmlContent)\\r\\n\\r\\n return dictContent\",\n \"def load_persistent_settings(path):\\n settings = {}\\n if path.exists():\\n with open(path, \\\"r\\\") as f:\\n persistent = yaml.load(f)\\n if persistent:\\n settings.update(persistent)\\n return settings\",\n \"def ppt_file_to_dict(self, file_path):\\n try:\\n file = open(file_path, \\\"rb\\\")\\n\\n except IOError as e:\\n print(e)\\n return\\n\\n pres = Presentation(file)\\n file.close()\\n\\n for i in range(len(pres.slides)):\\n self.process_slide(pres.slides[i], i + 1, file_path)\",\n \"def ReadUserMappings(self, file_object):\\n # TODO: replace by:\\n # config_parser = configparser. ConfigParser(interpolation=None)\\n config_parser = configparser.RawConfigParser()\\n config_parser.readfp(file_object)\\n\\n user_mappings = {}\\n for option_name in config_parser.options(u'user_mappings'):\\n user_mapping = self._GetConfigValue(\\n config_parser, u'user_mappings', option_name)\\n\\n option_name = option_name.lower()\\n user_mappings[option_name] = user_mapping.lower()\\n\\n return user_mappings\",\n \"def getFileInfoDictionaryFromXML(xml_file):\\n\\n # Format: { lfn : [pfn, guid] }\\n # Example:\\n # lfn = \\\"EVNT.01461041._000001.pool.root.1\\\"\\n # pfn = file_info_dictionary[lfn][0]\\n # guid = file_info_dictionary[lfn][1]\\n\\n file_info_dictionary = {}\\n file_list = getFileListFromXML(xml_file)\\n for f in file_list:\\n lfn, pfn, guid = getFileInfoFromXML(f)\\n file_info_dictionary[lfn] = [pfn, guid]\\n\\n return file_info_dictionary\",\n \"def getFirefoxProfile(self, profileDirectory=None):\\n fp = webdriver.FirefoxProfile(profile_directory=profileDirectory)\\n return fp\",\n \"def _load_file(self, file_path: str) -> dict:\\n raise NotImplementedError()\",\n \"def list(self):\\n # List is to be extended (directories should not have a trailing slash)\\n paths_to_ignore = ['.DS_Store']\\n\\n profiles = []\\n cache = ClientCache(self._conan_api.cache_folder)\\n profiles_path = cache.profiles_path\\n if os.path.exists(profiles_path):\\n for current_directory, _, files in os.walk(profiles_path, followlinks=True):\\n files = filter(lambda file: os.path.relpath(\\n os.path.join(current_directory, file), profiles_path) not in paths_to_ignore, files)\\n\\n for filename in files:\\n rel_path = os.path.relpath(os.path.join(current_directory, filename),\\n profiles_path)\\n profiles.append(rel_path)\\n\\n profiles.sort()\\n return profiles\",\n \"def get_peak_stats(file_path):\\n peak_stats = dict(numberOfPeaks=0,\\n peakMean=0,\\n peakMeanStd=0,\\n peakMedian=0,\\n peakMedianStd=0,\\n medianTagSingletons=0,\\n singletons=0)\\n stddevs = []\\n peak_singleton_scores = []\\n scores = []\\n singletons = 0\\n i = 0\\n with open(file_path) as fh:\\n for i, line in enumerate(fh):\\n items = line.split('\\\\t')\\n # Gff column 6 is score.\\n score = float(items[5])\\n scores.append(score)\\n # Gff column 9 is a semicolon-separated list.\\n attributes = items[8].split(';')\\n for attribute in attributes:\\n if attribute.startswith('stddev'):\\n val = float(attribute.split('=')[1])\\n stddevs.append(val)\\n if val == 0.0:\\n # We have a peakSingleton.\\n singletons += 1\\n peak_singleton_scores.append(score)\\n break\\n fh.close()\\n if i > 0:\\n # The number of lines in the file is the number of peaks.\\n peak_stats['numberOfPeaks'] = i + 1\\n peak_stats['peakMean'] = numpy.mean(scores)\\n peak_stats['peakMeanStd'] = numpy.mean(stddevs)\\n peak_stats['peakMedian'] = numpy.median(scores)\\n peak_stats['peakMedianStd'] = numpy.median(stddevs)\\n peak_stats['medianTagSingletons'] = numpy.median(peak_singleton_scores)\\n peak_stats['singletons'] = singletons\\n return peak_stats\",\n \"def get_all_profiles(store=\\\"local\\\"):\\n return {\\n \\\"Domain Profile\\\": get_all_settings(profile=\\\"domain\\\", store=store),\\n \\\"Private Profile\\\": get_all_settings(profile=\\\"private\\\", store=store),\\n \\\"Public Profile\\\": get_all_settings(profile=\\\"public\\\", store=store),\\n }\",\n \"def parseXML(xmlFile):\\n\\n tree = etree.parse(xmlFile)\\n root = tree.getroot() \\n transitionTable = dict()\\n transitionTable = getTransitions(tree, root, transitionTable)\\n return tree, root, transitionTable\",\n \"def load_vsp_pairs(path: str) -> dict:\\n vsp_pairs = dict()\\n with io.open(file=path, mode=\\\"r\\\", encoding=\\\"utf-8\\\") as src_file:\\n for line in src_file:\\n content = line.split(':', 1)\\n vsp_pairs[tuple(content[0].split(','))] = content[1]\\n return vsp_pairs\",\n \"def parse_info_from_file(path):\\n try:\\n filename = os.path.split(path)[1]\\n filename = os.path.splitext(filename)[0]\\n age, gender, race, _ = filename.split('_')\\n\\n return int(age), dataset_dict['gender_id'][int(gender)], dataset_dict['race_id'][int(race)]\\n except Exception as ex:\\n return None, None, None\",\n \"def get(self, name='default'):\\r\\n if name not in self._cache:\\r\\n full_path = os.path.join(self._base_folder, name)\\r\\n self._create_if_needed(full_path)\\r\\n self._cache[name] = Profile(full_path, name)\\r\\n return self._cache[name]\",\n \"def loads(file_path: Path) -> tuple[dict[str, Any], list[tuple[int, str]]]:\\n\\n content = read_file_with_any_encoding(file_path=file_path)\\n\\n if not all(key in content for key in (\\\"resource\\\", \\\"provider\\\")):\\n return {}, []\\n\\n file_lines = [(idx + 1, line) for idx, line in enumerate(content.splitlines(keepends=True))]\\n\\n template: \\\"dict[str, Any] | list[dict[str, Any]]\\\" = loader.loads(content=content)\\n if not template:\\n template = {}\\n if isinstance(template, list):\\n template = template[0]\\n\\n if template:\\n template = prepare_definition(template)\\n\\n return template, file_lines\",\n \"def read_pfile(pfile_loc):\\n with open(pfile_loc, mode='r') as pfile:\\n lines = pfile.readlines()\\n\\n profiles = {}\\n nprofs = 0 # counter for total number of profiles so far\\n linestart = 0 # counter for which line to start at for each profile\\n nlines_tot = len(lines)\\n\\n while True:\\n # Read the header line for each profile first\\n lin1 = lines[linestart].split()\\n npts_prof = int(lin1[0])\\n\\n xname = lin1[1]\\n yname = lin1[2]\\n dyname = ''.join(lin1[3:])[:-1]\\n\\n # Generate and populate the profile arrays\\n x = np.zeros(npts_prof)\\n y = np.zeros(npts_prof)\\n dy = np.zeros(npts_prof)\\n for i in range(npts_prof):\\n split_line = lines[linestart + i + 1].split()\\n x[i] = float(split_line[0])\\n y[i] = float(split_line[1])\\n dy[i] = float(split_line[2][:-1])\\n\\n # profiles[xname + '_' + yname] = x # psinorm\\n profiles[xname] = x\\n profiles[yname] = y\\n profiles[dyname] = dy\\n\\n nprofs += 1\\n linestart += 1 + npts_prof\\n\\n if linestart >= nlines_tot:\\n break\\n\\n # Check if all psinorms are the same, consolidate if so (they are, don't bother separating)\\n\\n # condense = True\\n # psinorm = None\\n # for k in profiles.keys():\\n # if k is None or k=='':\\n # continue\\n #\\n # if k[:4] == 'psin':\\n # if psinorm is None:\\n # psinorm = profiles[k]\\n #\\n # if max(abs(profiles[k] - psinorm)) > 1e-5:\\n # condense = False\\n # break\\n\\n # if condense:\\n # profiles = {key: value for key, value in profiles.items()\\n # if key[:4] != 'psin' or key is None or key==''}\\n # profiles['psinorm'] = psinorm\\n\\n return profiles\",\n \"def getProfiles(context):\\n\\n analytics_tool = getToolByName(getSite(), 'portal_analytics')\\n # short circuit if user hasn't authorized yet\\n if not analytics_tool.is_auth():\\n return SimpleVocabulary([])\\n\\n try:\\n profiles = analytics_tool.makeCachedRequest('profiles')\\n except error.BadAuthenticationError:\\n choices = [('Please authorize with Google in the Google Analytics \\\\\\n control panel.', None)]\\n return SimpleVocabulary.fromItems(choices)\\n except error.RequestTimedOutError:\\n choices = [('The request to Google Analytics timed out. Please try \\\\\\n again later.', None)]\\n return SimpleVocabulary.fromItems(choices)\\n if profiles:\\n unique_choices = {}\\n for entry in profiles:\\n title = entry.get('name')\\n title = crop(title, 40)\\n tableId = entry.get('id')\\n unique_choices.update({title: tableId})\\n choices = unique_choices.items()\\n else:\\n choices = [('No profiles available', None)]\\n return SimpleVocabulary([SimpleTerm(c[1], c[1], c[0]) for c in choices])\",\n \"def list_profiles(self):\\n return self._get(\\\"posture\\\", box=BoxList)\",\n \"def read_pro(path,var_to_plot= None):\\n\\n # Which variables are you interested in?\\n\\n if var_to_plot:\\n var_codes = ['0500','0501',pro_code_dict(var_to_plot,inverse=True)]\\n else:\\n var_codes = ['0500', '0501', '0502', '0503', '0506', '0508',\\n '0509', '0511', '0512', '0513', '0515',\\n '0516', '0521', '0535', '0540', '0541']\\n\\n # Set up the dictionary to be returned. Dictionary is organised by variable name.\\n\\n code_dict = pro_code_dict(return_all=True)\\n\\n variables = {}\\n for var in var_codes:\\n variables[code_dict[var]] = []\\n\\n # Open the .PRO file\\n\\n with open(path, \\\"r\\\") as f:\\n\\n # Iterate line by line\\n\\n for line in f.readlines():\\n\\n # If the variable code (first four chars) matches the variable of interest,\\n # append that line to the list of lines\\n\\n if line[:4] in var_codes:\\n variables[code_dict[line[:4]]].append(line)\\n\\n\\n # Now remove the header data\\n\\n for variable in variables.keys():\\n\\n variables[variable].pop(0)\\n\\n snowpro_list = [snowpro_from_snapshot(date_index, variables) for date_index in range(len(variables['Date']))]\\n\\n return (snowpro_list)\",\n \"def ReadWirelessNetworkProfile(self, id):\\n config = ConfigParser.ConfigParser()\\n config.read(self.wireless_conf)\\n cur_network = self.LastScan[id]\\n essid_key = \\\"essid:\\\" + cur_network[\\\"essid\\\"]\\n bssid_key = cur_network[\\\"bssid\\\"]\\n if self.debug_mode:\\n print bssid_key\\n if config.has_section(essid_key)and \\\\\\n misc.stringToNone(config.get(essid_key, 'use_settings_globally')):\\n return self._read_wireless_profile(config, cur_network, \\n essid_key)\\n elif config.has_section(bssid_key):\\n return self._read_wireless_profile(config, cur_network, bssid_key)\\n else:\\n cur_network[\\\"has_profile\\\"] = False\\n return \\\"500: Profile Not Found\\\"\",\n \"def get_server_profile_attributes(name, attribute=None):\\n general_xpaths = {\\n \\\"name\\\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\\n \\\"description\\\": FusionServerProfilesPage.ID_SERVER_DESCRIPTION,\\n \\\"server hardware\\\": FusionServerProfilesPage.ID_SERVER_HARDWARE_NAME,\\n \\\"associated server\\\": FusionServerProfilesPage.ID_ASSOCIATED_SERVER,\\n \\\"server hardware type\\\": FusionServerProfilesPage.ID_PROFILE_HARDWARE,\\n \\\"enclosure group\\\": FusionServerProfilesPage.ID_SERVER_ENC_GROUP,\\n \\\"affinity\\\": FusionServerProfilesPage.ID_SERVER_AFFINITY,\\n \\\"server power\\\": FusionServerProfilesPage.ID_SERVER_POWER_STATUS,\\n \\\"serial number\\\": FusionServerProfilesPage.ID_SERVER_SERIAL_NUMBER,\\n \\\"uuid\\\": FusionServerProfilesPage.ID_SERVER_UUID,\\n }\\n\\n # Navigate to Server Profile\\n if not select_server_profile(name):\\n logger._warn(\\\"Failed to find Server Profile '%s'\\\" % name)\\n BuiltIn().sleep(4)\\n # Get attribute data\\n if attribute is not None:\\n keys = general_xpaths.keys()\\n if attribute.lower() not in keys:\\n logger._warn(\\\"Invalid attribute '%s'.\\\" % attribute)\\n return None\\n\\n # Get specified attribute\\n xpath = general_xpaths[attribute.lower()]\\n ui_lib.wait_for_element_visible(xpath, timeout=10)\\n value = ui_lib.ignore_staleElementRefException(\\\"get_text\\\", xpath)\\n\\n return value\\n else:\\n # Get all attributes of Server Profile\\n general_data = {}\\n for attribute, xpath in general_xpaths.iteritems():\\n value = ui_lib.ignore_staleElementRefException(\\\"get_text\\\", xpath)\\n general_data[attribute] = value\\n return general_data\\n\\n return None\",\n \"def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\\n if not os.path.isfile(path):\\n raise ValueError(f'File {path} does not exist')\\n\\n with open(path, 'rb') as file:\\n return plistlib.load(file, fmt=format, dict_type=dict)\",\n \"def list_network_profiles(self, **params):\\r\\n return self.get(self.network_profiles_path, params=params)\",\n \"def load_frontend_xml(xml_path):\\n frontend_setting = open(xml_path, \\\"r\\\").read()\\n return frontend_setting\",\n \"def loadRatingScoreMappingFromFile(file):\\n\\treturn \\\\\\n\\tcompose(\\n\\t\\tdict\\n\\t , partial(map, lambda line: ((line[0], line[1]), line[2]))\\n\\t , partial(takewhile, lambda line: len(line) > 2 and line[0] != '')\\n\\t , lambda t: t[1]\\n\\t , lambda lines: (pop(lines), lines)\\n\\t , fileToLines\\n \\t , partial(join, getDataDirectory())\\n\\t)(file)\",\n \"def available_profiles(cls) -> List[str]:\\n return list(cfg.get(\\\"profiles\\\"))\",\n \"def load_settings(path):\\n if os.path.exists(path):\\n comments = lambda s: s and not s.startswith(\\\"#\\\")\\n settings = filter(comments, open(path, 'r'))\\n return dict((k.strip(), v.strip()) for k, _, v in\\n [s.partition('=') for s in settings])\\n # Handle nonexistent or empty settings file\\n return {}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7626121","0.73465997","0.66420317","0.64548403","0.6422596","0.61153334","0.60498744","0.59523565","0.5855331","0.57868946","0.5670626","0.56380814","0.5517114","0.5462929","0.54440254","0.54290825","0.5421971","0.5367816","0.53443223","0.53429097","0.52782893","0.52520305","0.5212792","0.5212367","0.5189856","0.51876795","0.5160056","0.5152693","0.5114987","0.5097414","0.5086328","0.5065623","0.50550723","0.50406367","0.5018515","0.50139517","0.49919915","0.49877903","0.49815142","0.4965874","0.4950723","0.49470863","0.49470863","0.4944592","0.49265897","0.4897313","0.48922822","0.48824298","0.48754537","0.48617885","0.4853672","0.48494604","0.4827575","0.48170343","0.4813766","0.48130554","0.48117656","0.48112392","0.4794602","0.4785146","0.47843874","0.4781947","0.47689068","0.476023","0.47442093","0.47441417","0.47271463","0.4724411","0.47236937","0.47135764","0.47091702","0.47082654","0.4698764","0.4698521","0.46971872","0.46835914","0.46788794","0.46748686","0.46738735","0.46733943","0.46732545","0.4671669","0.46629795","0.4662153","0.4661855","0.4661047","0.46605965","0.46506172","0.46498033","0.46444985","0.46422267","0.4640508","0.4638877","0.4633151","0.46269426","0.46112186","0.46084902","0.46063378","0.46049947","0.46019226"],"string":"[\n \"0.7626121\",\n \"0.73465997\",\n \"0.66420317\",\n \"0.64548403\",\n \"0.6422596\",\n \"0.61153334\",\n \"0.60498744\",\n \"0.59523565\",\n \"0.5855331\",\n \"0.57868946\",\n \"0.5670626\",\n \"0.56380814\",\n \"0.5517114\",\n \"0.5462929\",\n \"0.54440254\",\n \"0.54290825\",\n \"0.5421971\",\n \"0.5367816\",\n \"0.53443223\",\n \"0.53429097\",\n \"0.52782893\",\n \"0.52520305\",\n \"0.5212792\",\n \"0.5212367\",\n \"0.5189856\",\n \"0.51876795\",\n \"0.5160056\",\n \"0.5152693\",\n \"0.5114987\",\n \"0.5097414\",\n \"0.5086328\",\n \"0.5065623\",\n \"0.50550723\",\n \"0.50406367\",\n \"0.5018515\",\n \"0.50139517\",\n \"0.49919915\",\n \"0.49877903\",\n \"0.49815142\",\n \"0.4965874\",\n \"0.4950723\",\n \"0.49470863\",\n \"0.49470863\",\n \"0.4944592\",\n \"0.49265897\",\n \"0.4897313\",\n \"0.48922822\",\n \"0.48824298\",\n \"0.48754537\",\n \"0.48617885\",\n \"0.4853672\",\n \"0.48494604\",\n \"0.4827575\",\n \"0.48170343\",\n \"0.4813766\",\n \"0.48130554\",\n \"0.48117656\",\n \"0.48112392\",\n \"0.4794602\",\n \"0.4785146\",\n \"0.47843874\",\n \"0.4781947\",\n \"0.47689068\",\n \"0.476023\",\n \"0.47442093\",\n \"0.47441417\",\n \"0.47271463\",\n \"0.4724411\",\n \"0.47236937\",\n \"0.47135764\",\n \"0.47091702\",\n \"0.47082654\",\n \"0.4698764\",\n \"0.4698521\",\n \"0.46971872\",\n \"0.46835914\",\n \"0.46788794\",\n \"0.46748686\",\n \"0.46738735\",\n \"0.46733943\",\n \"0.46732545\",\n \"0.4671669\",\n \"0.46629795\",\n \"0.4662153\",\n \"0.4661855\",\n \"0.4661047\",\n \"0.46605965\",\n \"0.46506172\",\n \"0.46498033\",\n \"0.46444985\",\n \"0.46422267\",\n \"0.4640508\",\n \"0.4638877\",\n \"0.4633151\",\n \"0.46269426\",\n \"0.46112186\",\n \"0.46084902\",\n \"0.46063378\",\n \"0.46049947\",\n \"0.46019226\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74111307"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11592,"cells":{"query":{"kind":"string","value":"Saves the profiles to an xml file."},"document":{"kind":"string","value":"def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\r\n if lastused:\r\n writer.WriteAttributeString(\"LastUsed\", \",\".join(lastused))\r\n for profile in profiles:\r\n profiles[profile].save_to_xml(writer)\r\n writer.WriteEndElement()\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def save_to_xml(self, xwriter):\r\n\r\n xwriter.WriteStartElement(\"Profile\")\r\n xwriter.WriteAttributeString(\"Name\", self.Name)\r\n xwriter.WriteStartAttribute(\"Version\")\r\n xwriter.WriteValue(self.Version)\r\n xwriter.WriteEndAttribute()\r\n\r\n for var_name in self.__dict__:\r\n var_type = type(getattr(self, var_name))\r\n\r\n if var_type is str and var_name != \"Name\":\r\n self.write_string_to_xml(var_name, xwriter)\r\n\r\n elif var_type is bool:\r\n self.write_bool_to_xml(var_name, xwriter)\r\n\r\n elif var_type is dict:\r\n self.write_dict_to_xml(var_name, xwriter)\r\n\r\n elif var_type is list and var_name != \"ExcludeRules\":\r\n self.write_list_to_xml(var_name, xwriter)\r\n\r\n xwriter.WriteStartElement(\"ExcludeRules\")\r\n xwriter.WriteAttributeString(\"Operator\", self.ExcludeOperator)\r\n xwriter.WriteAttributeString(\"ExcludeMode\", self.ExcludeMode)\r\n for rule in self.ExcludeRules:\r\n if rule:\r\n rule.save_xml(xwriter)\r\n xwriter.WriteEndElement()\r\n \r\n xwriter.WriteEndElement()","def saving_file(xml):\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))","def save_xml(self, filename):\n if \".xml\" not in filename:\n filename = filename + \".xml\"\n\n shutil.copyfile(self.env.model_file, filename)","def save_profile(self):\n self.save()","def save_profile(self, dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"wb\") as f:\n pickle.dump(self, f)","def Save_xml(self, accounts):\n try:\n\n self.extension = \".xml\"\n\n colors.info(\"Saving as XML in {}{}\".format(self.file, self.extension))\n\n Main = ET.Element(\"SpotCheck\")\n\n SpotifyFree = ET.SubElement(Main, 'SpotifyFree')\n SpotifyPremium = ET.SubElement(Main, 'SpotifyPremium')\n PremiumFamily = ET.SubElement(Main, 'PremiumFamily')\n AdminPremiumFamily = ET.SubElement(Main, 'AdminPremiumFamily')\n BadAccounts = ET.SubElement(Main, 'BadAccounts')\n\n for account in accounts:\n if account.get(\"account_login\") == \"error\":\n temp = ET.SubElement(BadAccounts, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n else:\n if account.get(\"AccountType\") == \"Spotify Free\":\n temp = ET.SubElement(SpotifyFree, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n elif account.get(\"AccountType\") == \"Spotify Premium\":\n temp = ET.SubElement(SpotifyPremium, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n elif account.get(\"AccountType\") == \"Premium Family\":\n if account.get(\"Admin\"):\n temp = ET.SubElement(AdminPremiumFamily, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n else:\n temp = ET.SubElement(PremiumFamily, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n XML = ET.tostring(Main)\n with open(self.file + self.extension, \"w\") as output_:\n output_.write(XML)\n colors.correct(\"Done! All saved successfully\")\n except Exception as e:\n colors.error(str(e))\n _exit(1)","def Save_Current_Profile(self):\r\n #name = tkFileDialog.asksaveasfilename()\r\n #if( name == \"\" ):\r\n # return\r\n #self.system.Save_Current_Profile(name)\r\n self.system.Save_Current_Profile()","def saveSessionToXML(self, filename):\r\n xmlStr = self.createXMLStr()\r\n \r\n #Write to the file\r\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\r\n xmlFile = open(filename, 'w')\r\n xmlFile.write(xmlStr)\r\n xmlFile.close()","def save(self, save_dir):\n ProfileManager.save_data_to_disk(self.player_data, path.join(save_dir, self.player_name + '.yaml'))","def apply(self):\r\n\r\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\r\n\r\n def saving_file(xml):\r\n \"\"\" Save user profile in xml format to temp_ dir.\"\"\"\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))\r\n\r\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\r\n \"/data/sampleProfile.xml\")\r\n\r\n # The below code will parse the sample xml file\r\n # and fill important details entered by the user.\r\n root_tree = parse_xml.getroot()\r\n root_tree[0].text = self.ssid.get()\r\n root_tree[1][0][0].text = self.ssid.get()\r\n root_tree[3].text = self.connection_mode.get().lower()\r\n security = root_tree[4][0]\r\n security[0][0].text = self.authentication.get()\r\n security[0][1].text = self.encryption.get()\r\n if self.authentication.get() != \"open\":\r\n etree.SubElement(security, \"sharedKey\")\r\n etree.SubElement(security[1], \"keyType\").text = \"passPhrase\"\r\n etree.SubElement(security[1], \"protected\").text = \"false\"\r\n etree.SubElement(security[1], \"keyMaterial\").text = self.password.get()\r\n\r\n # Save the xml file\r\n saving_file(root_tree)\r\n\r\n # Add profile to the system.\r\n temp_path = 'netsh wlan add profile filename=\"' + self.app_path + \"\\\\temp_\\\\\"\r\n output_ = subprocess.run(temp_path + file_name + '.xml\"', shell=True,\r\n capture_output=True, text=True)\r\n os.remove(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\")\r\n\r\n # If unable to add profile.\r\n if output_.returncode != 0:\r\n message = \"Sorry, Unable to add profile.\\n(You entered wrong details \" \\\r\n \"or else you don't have admin rights.)\"\r\n image_ = \"error\"\r\n\r\n else:\r\n message = \"Profile added successfully (Please Refresh)\"\r\n image_ = \"warning\"\r\n\r\n MessageBox(self.parent, message, image_)","def saveFile(self, filename):\n ret = libxml2mod.xmlSaveFile(filename, self._o)\n return ret","def save(self, filename=None):\n f = filename if filename else self.path\n etree.register_namespace('', TEI)\n etree.register_namespace('mith', MITH)\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')","def save(self, save_path=None):\n if self._xml is None:\n raise IOError(\"There's nothing to save\")\n\n path = self._path_to_xml if save_path is None else save_path\n\n with open(path, 'w') as f:\n rough_string = Et.tostring(self._xml, 'utf-8')\n par = etree.XMLParser(remove_blank_text=True)\n elem = etree.XML(rough_string, parser=par)\n parsed = minidom.parseString(etree.tostring(elem))\n f.write(parsed.toprettyxml(indent=\" \"))","def save_profiles(self, fout, save_hybrid_meta=True):\n\n self._init_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\n self._write_h5_out(fout, save_hybrid_meta=save_hybrid_meta)","def save(self):\n # TODO: save the file","def write_tessprofiles(tessprofiles: Dict, online=False):\n logger.info(f'Writing tessprofiles')\n profile = TESSPROFILE_ONLINE_PATH if online else TESSPROFILE_PATH\n with open(profile, 'w') as f:\n json.dump(tessprofiles, f, indent=4)","def save(self, filename):\n pass","def SaveXMLToDB(xmlFileName):","def save(self):\n with self.open(self.filename, 'wt') as fd:\n for node in self.elements:\n fd.write(node.text)","def save(self, fname):\n pass","def save(self):\n self.lock.acquire()\n try:\n self.xml.set(\"name\",self.name)\n self.xml.set(\"room\",self.room)\n self.xml.set(\"type\",self.type)\n self.xml.find(\"address\").text = \":\".join([str(x) for x in self.address])\n if self.pos is not None:\n self.xml.find(\"pos\").text = \" \".join([str(x) for x in self.pos])\n self.xml.find(\"icon\").text = self.icon\n \n finally:\n self.lock.release()\n \n self.house.save_devices()","def dumpProfiles(profiles):\n with open(userProfilesDir, \"w\") as outfile:\n outfile.writelines(json.dumps(profiles, indent=4))\n outfile.close()","def saveUsersProfiles_(self, plist):\r\n \r\n LogInfo(u\"Saving update profiles with PublicationDate %@\", plist[u\"PublicationDate\"])\r\n if not plist.writeToFile_atomically_(self.userUpdateProfilesPath, False):\r\n LogError(u\"Failed to write %@\", self.userUpdateProfilesPath)","def save_file(self):\n if self.select_path.text() != \"\":\n filepath = self.select_path.text()\n road_network = self.map_selection.currentText()\n if self.map_selection.currentText() == \"User Defined\":\n road_network = self.map_selection_user_defined.text()\n gen_xml = GenerateXML(filepath, road_network)\n gen_xml.main()\n # remember Road Network for future\n set_metadata(road_network_filepath=road_network)\n else:\n message = \"No export path was selected\"\n iface.messageBar().pushMessage(\"Warning\", message, level=Qgis.Warning)\n QgsMessageLog.logMessage(message, level=Qgis.Warning)","def saveSettings(self):\n self.userFiles.applyData()\n self.userPersonal.applyData()","def _save(self):\n file = open(\"settings.ini\", \"w\")\n self._parser.write(file)\n file.close()","def save_as(self, fname, base = None, indent = '', topns = True, namespaces = {}):\n with codecs.open(fname, \"w\", encoding=\"utf-8\") as outf:\n self.serialize_xml(outf.write, base=base, indent=indent, topns=topns, namespaces=namespaces)","def save(self, filename: str):\n dump(self, filename)","def save(self, pretty=True):\n self.endInstance()\n if pretty:\n _indent(self.root, whitespace=self._whiteSpace)\n tree = ET.ElementTree(self.root)\n tree.write(self.path, encoding=\"utf-8\", method='xml', xml_declaration=True)\n if self.logger:\n self.logger.info(\"Writing %s\", self.path)","def save():","def save(self):\n settings_path = os.path.join(self.file_path, \"__file_data.json\")\n f = open( settings_path, 'w' )\n f.write( simplejson.dumps( self.fileList ) )\n f.close()\n\n settings_path = os.path.join(self.file_path, \"__user_data.json\")\n f = open( settings_path, 'w' )\n f.write( simplejson.dumps( self.userList ) )\n f.close()","def save(self):\r\n with open(self.filename, 'wb') as configfile:\r\n self.write(configfile)","def save(self, filename):\n with open(filename, \"w\") as fp:\n dump(self, fp)","def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused","def saveToXml(self) -> org.jdom.Element:\n ...","def save_xml_file():\n global output_on_display, import_lst, column_names, data\n if data_base == '':\n mistake_load_table()\n else:\n column_names = data[0]\n step = len(column_names)\n\n save_name = asksaveasfilename(title=\"Select file\", filetypes=((\"XML\", \"*.xml\"), (\"all files\", \"*.*\")),\n confirmoverwrite=True, defaultextension='.xml')\n data = import_lst\n\n if len(data[0]) == step:\n pass\n else:\n data = import_lst[step::]\n\n data2 = list(map(list, zip(*data)))\n\n data3 = {key: value for key, value in zip(column_names, data2)}\n\n column = list(data3.keys())\n\n df = pd.DataFrame(data3, columns=column)\n\n data_dict = df.to_dict(orient=\"records\")\n with open('output.json', \"w+\") as f:\n json.dump(data_dict, f, indent=4)\n\n xml_data = dicttoxml(data_dict).decode()\n with open(save_name, \"w+\") as f:\n f.write(xml_data)\n\n data.clear()\n data2.clear()\n data3.clear()","def export_to_file(self, filename):\n if len(filename.split(\".\")) == 1:\n filename += \".xml\"\n xmlstring = self._dommodel.toprettyxml(\" \", \"\\n\")\n with open(filename, \"w\") as f:\n f.write(xmlstring)","def save(wn, change_list=None):\n wordnet_yaml.save(wn, change_list)\n save_all_xml(wn, change_list)\n with codecs.open(\"wn.xml\", \"w\", \"utf-8\") as outp:\n wn.to_xml(outp, True)\n pickle.dump(wn, open(\"wn.pickle\", \"wb\"))","def save_current_to_profile(self, profile_name, prof_desc='', prof_path='',\n self_contained=False):\n # Open the already existing profile\n new_profile = profile(profile_name, workdir=os.path.dirname(prof_path))\n\n # shortcut\n w3af_plugins = self._w3af_core.plugins\n\n # Save the enabled plugins\n for plugin_type in w3af_plugins.get_plugin_types():\n enabled_plugins = []\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\n enabled_plugins.append(plugin_name)\n new_profile.set_enabled_plugins(plugin_type, enabled_plugins)\n\n # Save the plugin options\n for plugin_type in w3af_plugins.get_plugin_types():\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\n plugin_options = w3af_plugins.get_plugin_options(plugin_type,\n plugin_name)\n if plugin_options:\n new_profile.set_plugin_options(plugin_type,\n plugin_name,\n plugin_options,\n self_contained=self_contained)\n\n # Save the profile targets\n targets = cf.cf.get('targets')\n if targets:\n new_profile.set_target(' , '.join(t.url_string for t in targets))\n\n # Save the misc and http settings\n misc_settings = MiscSettings()\n new_profile.set_misc_settings(misc_settings.get_options())\n new_profile.set_http_settings(\n self._w3af_core.uri_opener.settings.get_options())\n\n # Save the profile name and description\n new_profile.set_desc(prof_desc)\n new_profile.set_name(profile_name)\n\n # Save the profile to the file\n new_profile.save(profile_name)\n\n return new_profile","def save_changes(self):\n\n velib, autolib, subway = None, None, None\n for key, value in VELIB_SUBSCRIPTIONS.iteritems():\n if self._velib.get() == value:\n velib = key\n break\n for key, value in AUTOLIB_SUBSCRIPTIONS.iteritems():\n if self._autolib.get() == value:\n autolib = key\n break\n for key, value in SUBWAY_SUBSCRIPTIONS.iteritems():\n if self._subway.get() == value:\n subway = key\n break\n preferences = {\n FASTEST: self._fastest.get(),\n SHORTEST: self._shortest.get(),\n CHEAPEST: self._cheapest.get(),\n SIMPLEST: self._simplest.get(),\n WEATHER_IMPACT: self._weather_impact.get(),\n LESS_PAINFUL: self._less_painful.get(),\n LESS_WALKING: self._less_walking.get()\n }\n\n result = self._system.set_profile_settings(velib, autolib, subway, self._driving_licence.get(), preferences)\n if not result[\"success\"]:\n showerror('Erreur système', result[\"error\"])\n return\n\n # Redirection vers la page principale\n from settings import RideSettingsPage\n self.pack_forget()\n RideSettingsPage(self._window, self._system)","def saveProfile(self, request):\n return self._doProfile(request)","def save(self):\n file = open(self.path, 'w')\n self.config.write(file)\n file.close()","def save(self) -> None:\n self.save_metadata()\n self.save_files()","def saveToFile(self,filename):\n path = os.path.dirname(__file__)+\"/\"+filename\n stream = open(path,\"w\")\n yaml.dump(self.parameters(),stream)","def save(self, filename:str):\n dump(self, filename=filename)","def saveSettings(self):\n self.genFiles.applyData()\n self.genGraph.applyData()","def _save(self):\n self.stat=[]\n for i in range(len(self.xml_names)):\n self.stat.append(self.check[i].isChecked())\n self.close()","def save(self):\n for p, c in self.configs_:\n c.write(p)","def save(self):\n\t\tself.CONFIG.save()\n\t\tself.temp_files.save()","def save_xml(tree, file_name, folder_name):\r\n import os # ändrar plats för filer\r\n os.chdir(folder_name)\r\n tree.write(file_name) # Namnet på ny fil\r","def save_calibration_profile(self, filename: str) -> None:\n pass","def save(self):\n file = open(self.path, 'w')\n self.parser.write(file)\n file.close()","def save(self):\n\t\tPath(self.PATH).mkdir(parents=True,exist_ok=True)\n\n\t\twith open(self.account_file, \"wb\") as file:\n\t\t\tpickle.dump(self, file)","def save_profile(sender, instance, **kwargs):\n instance.profile.save()","def write_network_manager_profiles(self):\n \n if len(self.network_manager_profiles) > 0: # Make sure we have some profiles to migrate\n for profile in self.network_manager_profiles:\n profile.write_config()\n profile.set_ownership()\n with open(migration_sign, \"w\") as f:\n pass","def save():\n pass","def save_prefs(self):\n prefs_file = open(expanduser(self.prefs_path), 'w')\n pickle.dump(self.prefs, prefs_file)","def save_file(self):\n # paginate over deputies and senators getting their fields\n fieldnames = set([])\n congressmen = self.deputies + self.senators\n for data in congressmen:\n fieldnames = fieldnames.union(data.dump().keys())\n\n\n with open(IDENTITY_FILE_UPDATED, 'a') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=list(fieldnames), delimiter=';')\n writer.writeheader()\n\n for data in congressmen:\n writer.writerow(data.dump())","def to_xml_file(self, xml_file_path):\n s = self.to_xml()\n with open(xml_file_path, \"w+b\") as f:\n f.write(s)","def saveState(self, file):\n state = self.context.getState(getPositions=True, getVelocities=True, getParameters=True, getIntegratorParameters=True)\n xml = mm.XmlSerializer.serialize(state)\n if isinstance(file, str):\n with open(file, 'w') as f:\n f.write(xml)\n else:\n file.write(xml)","def save(self, filename):\n o = open(filename, 'w')\n o.write(self.write())\n o.close()","def Save(file=CONFIG):\n\tif file in files:\n\t\tfiles[file].SaveFile()","def save(self, filename):\n with open(filename, 'w') as f:\n pickle.dump((self.mean, self.std_dev), f)","def save(self, filename):\n \n raise NotImplementedError(\"not implemented!\")","def dump(self, filename, plist):\r\n xml = self.tostring(plist)\r\n fd = open(filename, 'wb')\r\n try:\r\n fd.write(xml)\r\n finally:\r\n fd.close()","def saveXML(self, filename):\n root = ET.Element('root')\n pklot = ET.SubElement(root, \"ParkingLot\")\n\n lotname = ET.SubElement(pklot, \"LotName\", name=str(self.name))\n idCounter = ET.SubElement(pklot, \"NextAvailableID\", counter=str(self.spotIDCounter))\n for spot in self.parkingSpots:\n ET.SubElement(pklot, 'Spot', id=str(spot.id), location=' '.join(str(x) for x in spot.location))\n\n tree = ET.ElementTree(root)\n tree.write(filename)","def save_model(self, fname, pretty=False):\n with open(fname, \"w\") as f:\n xml_str = ET.tostring(self.root, encoding=\"unicode\")\n if pretty:\n parsed_xml = xml.dom.minidom.parseString(xml_str)\n xml_str = parsed_xml.toprettyxml(newl=\"\")\n f.write(xml_str)","def save():\n log.info(\"Saving settings file\")\n with open(SETTINGS_FILE, \"w\") as file:\n json.dump(_names, file)","def generate_files(input_path, output_path):\n profiles = pd.read_csv(f'{input_path}/Profile/Profile.csv')\n\n # call all predictors\n predicted = {f: pr.all_predictors[f].predict(profiles=profiles, base_folder=input_path)\n for f in pr.features if f in pr.all_predictors}\n\n for i, (index, row) in enumerate(profiles.iterrows()):\n file = open(os.path.join(output_path, f'{row[\"userid\"]}.xml'), 'w')\n gender = pr.get_gender(predicted['gender'][row[1]]) if row[1] in predicted['gender'] else predicted['age_group_alternative'][row[1]]\n open_ = predicted['open'][row[1]] if row[1] in predicted['open'] else 'null'\n conscientious = predicted['conscientious'][row[1]] if row[1] in predicted['conscientious'] else 'null'\n extrovert = predicted['extrovert'][row[1]] if row[1] in predicted['extrovert'] else 'null'\n agreeable = predicted['agreeable'][row[1]] if row[1] in predicted['agreeable'] else 'null'\n neurotic = predicted['neurotic'][row[1]] if row[1] in predicted['neurotic'] else 'null'\n age = predicted['age_group'][row[1]] if row[1] in predicted['age_group'] else 'null'\n\n file.write(f'<user id=\"{row[1]}\" ')\n file.write(f'age_group=\"{age}\" ')\n file.write(f'gender=\"{gender}\" ')\n file.write(f'extrovert=\"{extrovert}\" ')\n file.write(f'neurotic=\"{neurotic}\" ')\n file.write(f'agreeable=\"{agreeable}\" ')\n file.write(f'open=\"{open_}\" ')\n file.write(f'conscientious=\"{conscientious}\" />')\n file.close()","def save(self):\n # TODO (Pierre): code","def save(self, filename):\n raise NotImplementedError","def write(self, file_or_filename):\n etMap = revert(self)\n xmlTree = ET.ElementTree(etMap)\n xmlTree.write(file_or_filename)","def save_profile(self, request):\n return self.profile_service.do_profile(request)","def saveAs(self):\n self.saveFile()","def save(self):\n path = self.user.get_session_path()\n with open(path, 'a', encoding='utf8') as file:\n self.write(file=file)","def xmlwrite(self, doc, filename):\n pathname = os.path.join(self.session.session_dir, filename)\n f = open(pathname, \"w\")\n doc.writexml(writer=f, indent=\"\", addindent=\" \", newl=\"\\n\", encoding=\"UTF-8\")\n f.close()","def save(self, filename):\n pickle.dump(self, open(filename + '.p', 'wb'), 2)","def save_profile():\n state = request.get_json()\n logger.debug(\"Roast Profile: %s\" % state)\n c = mongo.db[app.config['PROFILE_COLLECTION']]\n item = {'coffee': state.get('coffee'), 'roast': state.get('roast'),\n 'drop_temp': state.get('drop_temp'),\n 'brew_methods': state.get('brew_methods'),\n 'notes': state.get('notes'), 'datetime': now_time(),\n 'user': current_user.get_id()}\n _id = c.insert(item)\n return jsonify({'success': True})","def save(self, includeInDefaultProfile=True):\n\n self.prefab.core.file_write(self.pathProfile, str(self), showout=True)\n\n # make sure we include our custom profile in the default\n if includeInDefaultProfile is True:\n if self.pathProfile != self.bash.profileDefault.pathProfile:\n print(\"INCLUDE IN DEFAULT PROFILE:%s\" % self.pathProfile)\n out = \"\"\n inProfile = self.prefab.core.file_read(self.bash.profileDefault.pathProfile)\n for line in inProfile.split(\"\\n\"):\n if line.find(self.pathProfile) != -1:\n continue\n out += \"%s\\n\" % line\n\n out += \"\\nsource %s\\n\" % self.pathProfile\n if out.replace(\"\\n\", \"\") != inProfile.replace(\"\\n\", \"\"):\n self.prefab.core.file_write(self.bash.profileDefault.pathProfile, out)\n self.bash.profileDefault.load()\n\n self.bash.reset() # do not remove !","def save(self):\n memento = self.create_memento()\n import datetime\n f = open(str(datetime.datetime.now()).replace(' ','_')+'.saved_story','w')\n cPickle.dump(memento,f)\n f.close()\n zcanvas.message(\"Saved!\")","def write_xml(self, xmlfile):\n system.xml.write_file(xmlfile, self.status, 'status')","def saveSettings(self):\n helpers.saveFile(self.dataDir, self.settingsFilename, json.dumps(self.settings))","def SaveWiredNetworkProfile(self, profilename):\n def write_script_ent(prof, conf, script):\n if not conf.has_option(prof, script):\n conf.set(prof, script, None)\n if profilename == \"\":\n return \"500: Bad Profile name\"\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n config.remove_section(profilename)\n config.add_section(profilename)\n for x in self.WiredNetwork:\n config.set(profilename, x, self.WiredNetwork[x])\n write_script_ent(profilename, config, \"beforescript\")\n write_script_ent(profilename, config, \"afterscript\")\n write_script_ent(profilename, config, \"disconnectscript\")\n config.write(open(self.wired_conf, \"w\"))\n return \"100: Profile Written\"","def saveGraph(self, filename):\n nx.write_yaml(self.G,filename)","def _save_data(self, filename):\n if not os.path.isdir(os.path.dirname(filename)):\n return False\n with open(filename, 'w') as f:\n f.truncate()\n pickle.dump({\n 'user_data': self.user_data,\n 'api_data': self.api_data,\n 'profiles': self.profiles\n }, f)","def saveToFile(self, filePath):\n d = self.save()\n with open(filePath, 'wb') as f:\n f.write(d)","def save(self,file):\n\n with open(file,\"w\") as f:\n f.write(self.to_string())","def save(self, export_path: str):","def save(self, filename):\n with open(filename, 'w') as f:\n yaml.dump(self.to_dict(), f, sort_keys=False)","def saver(filename = None):\n save(self, filename)","def save_xml(self, fileName, outputTextFileName, predictedObjects):\n if self.verbose:\n print('Saving xml file', outputTextFileName)\n # root element\n root = ET.Element(\"annotation\")\n # annotation.filename\n ET.SubElement(root, \"filename\").text = inputFileName\n # For each predicted object\n for i in range(len(predictedObjects)):\n # annotation.object\n predObject = ET.SubElement(root, \"object\")\n # annotation.object.name\n ET.SubElement(\n predObject, \"name\").text = predictedObjects[i][0]\n # annotation.object.confidence\n ET.SubElement(predObject, \"confidence\").text = str(\n predictedObjects[i][5])\n # annotation.object.bndBox\n bndBox = ET.SubElement(predObject, \"bndBox\")\n # annotation.object.bndBox.xmin\n ET.SubElement(bndBox, \"xmin\").text = str(\n predictedObjects[i][1])\n # annotation.object.bndBox.ymin\n ET.SubElement(bndBox, \"ymin\").text = str(\n predictedObjects[i][2])\n # annotation.object.bndBox.xmax\n ET.SubElement(bndBox, \"xmax\").text = str(\n predictedObjects[i][3])\n # annotation.object.bndBox.ymax\n ET.SubElement(bndBox, \"ymax\").text = str(\n predictedObjects[i][4])\n # Making the xml string\n xmlString = minidom.parseString(\n ET.tostring(root)).toprettyxml(indent=\" \")\n # Saving the xml file\n with open(outputTextFileName, 'w') as f:\n f.write(xmlString)","def save (self, filename) :\n\t\tserialFile = open (filename, \"wb\")\n\t\tpickle.dump (self.production_rules, serialFile)\n\t\tpickle.dump (self.unitrelation, serialFile)\n\t\tpickle.dump (self.labels, serialFile)\n\t\tpickle.dump (self.keeper, serialFile)\n\t\tpickle.dump (self.strnodes, serialFile)\n\t\tpickle.dump (self.tokens, serialFile)\n\t\tserialFile.close()","def save(self):\n Preferences.setPrinter(\n \"PrinterName\",\n self.printerNameEdit.text())\n if self.printerColorButton.isChecked():\n Preferences.setPrinter(\"ColorMode\", 1)\n else:\n Preferences.setPrinter(\"ColorMode\", 0)\n if self.printFirstPageFirstButton.isChecked():\n Preferences.setPrinter(\"FirstPageFirst\", 1)\n else:\n Preferences.setPrinter(\"FirstPageFirst\", 0)\n Preferences.setPrinter(\n \"Magnification\",\n self.printMagnificationSpinBox.value())\n Preferences.setPrinter(\"HeaderFont\", self.printheaderFont)\n Preferences.setPrinter(\n \"LeftMargin\",\n self.leftMarginSpinBox.value())\n Preferences.setPrinter(\n \"RightMargin\",\n self.rightMarginSpinBox.value())\n Preferences.setPrinter(\n \"TopMargin\",\n self.topMarginSpinBox.value())\n Preferences.setPrinter(\n \"BottomMargin\",\n self.bottomMarginSpinBox.value())\n Preferences.setPrinter(\n \"Resolution\",\n self.resolutionSpinBox.value())","def _save_settings(self):\n # data to be save :\n # -----------------\n # futurePivot node\n\n # create attributes\n self._create_data_attribute()\n\n # connect futurePivot node\n pm.connectAttr(\n \"%s%s\" % (self._futurePivot.name(), \".message\"),\n self._object.attr(\"pivotData.futurePivot\"),\n f=True,\n )","def save_config_xml(self, fileName: str):\n self._sim.saveConfigXML(fileName)","def save(self):\n sublime.save_settings(self.file_name)","def write(self, filename):\n \n return self.model.write(filename,xml_declaration=True, encoding='utf-8')","def save_manifest(self, filename: Text) -> None:\r\n file = open(filename, \"w\")\r\n for element in self.elements:\r\n line = element.url\r\n for tag in element.tags:\r\n line += \",\" + tag\r\n file.write(line + \"\\n\")\r\n file.close()","def save(self, file_name, file_type=\"toml\"):\n if file_type == \"toml\":\n with open(file_name, mode=\"w\") as f:\n toml.dump(self._dict_to_save(), f)"],"string":"[\n \"def save_profile(file_path, profile):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n profile.save_to_xml(writer)\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def save_to_xml(self, xwriter):\\r\\n\\r\\n xwriter.WriteStartElement(\\\"Profile\\\")\\r\\n xwriter.WriteAttributeString(\\\"Name\\\", self.Name)\\r\\n xwriter.WriteStartAttribute(\\\"Version\\\")\\r\\n xwriter.WriteValue(self.Version)\\r\\n xwriter.WriteEndAttribute()\\r\\n\\r\\n for var_name in self.__dict__:\\r\\n var_type = type(getattr(self, var_name))\\r\\n\\r\\n if var_type is str and var_name != \\\"Name\\\":\\r\\n self.write_string_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is bool:\\r\\n self.write_bool_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is dict:\\r\\n self.write_dict_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is list and var_name != \\\"ExcludeRules\\\":\\r\\n self.write_list_to_xml(var_name, xwriter)\\r\\n\\r\\n xwriter.WriteStartElement(\\\"ExcludeRules\\\")\\r\\n xwriter.WriteAttributeString(\\\"Operator\\\", self.ExcludeOperator)\\r\\n xwriter.WriteAttributeString(\\\"ExcludeMode\\\", self.ExcludeMode)\\r\\n for rule in self.ExcludeRules:\\r\\n if rule:\\r\\n rule.save_xml(xwriter)\\r\\n xwriter.WriteEndElement()\\r\\n \\r\\n xwriter.WriteEndElement()\",\n \"def saving_file(xml):\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def save_xml(self, filename):\\n if \\\".xml\\\" not in filename:\\n filename = filename + \\\".xml\\\"\\n\\n shutil.copyfile(self.env.model_file, filename)\",\n \"def save_profile(self):\\n self.save()\",\n \"def save_profile(self, dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"wb\\\") as f:\\n pickle.dump(self, f)\",\n \"def Save_xml(self, accounts):\\n try:\\n\\n self.extension = \\\".xml\\\"\\n\\n colors.info(\\\"Saving as XML in {}{}\\\".format(self.file, self.extension))\\n\\n Main = ET.Element(\\\"SpotCheck\\\")\\n\\n SpotifyFree = ET.SubElement(Main, 'SpotifyFree')\\n SpotifyPremium = ET.SubElement(Main, 'SpotifyPremium')\\n PremiumFamily = ET.SubElement(Main, 'PremiumFamily')\\n AdminPremiumFamily = ET.SubElement(Main, 'AdminPremiumFamily')\\n BadAccounts = ET.SubElement(Main, 'BadAccounts')\\n\\n for account in accounts:\\n if account.get(\\\"account_login\\\") == \\\"error\\\":\\n temp = ET.SubElement(BadAccounts, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n else:\\n if account.get(\\\"AccountType\\\") == \\\"Spotify Free\\\":\\n temp = ET.SubElement(SpotifyFree, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n elif account.get(\\\"AccountType\\\") == \\\"Spotify Premium\\\":\\n temp = ET.SubElement(SpotifyPremium, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n elif account.get(\\\"AccountType\\\") == \\\"Premium Family\\\":\\n if account.get(\\\"Admin\\\"):\\n temp = ET.SubElement(AdminPremiumFamily, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n else:\\n temp = ET.SubElement(PremiumFamily, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n XML = ET.tostring(Main)\\n with open(self.file + self.extension, \\\"w\\\") as output_:\\n output_.write(XML)\\n colors.correct(\\\"Done! All saved successfully\\\")\\n except Exception as e:\\n colors.error(str(e))\\n _exit(1)\",\n \"def Save_Current_Profile(self):\\r\\n #name = tkFileDialog.asksaveasfilename()\\r\\n #if( name == \\\"\\\" ):\\r\\n # return\\r\\n #self.system.Save_Current_Profile(name)\\r\\n self.system.Save_Current_Profile()\",\n \"def saveSessionToXML(self, filename):\\r\\n xmlStr = self.createXMLStr()\\r\\n \\r\\n #Write to the file\\r\\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\\r\\n xmlFile = open(filename, 'w')\\r\\n xmlFile.write(xmlStr)\\r\\n xmlFile.close()\",\n \"def save(self, save_dir):\\n ProfileManager.save_data_to_disk(self.player_data, path.join(save_dir, self.player_name + '.yaml'))\",\n \"def apply(self):\\r\\n\\r\\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\\r\\n\\r\\n def saving_file(xml):\\r\\n \\\"\\\"\\\" Save user profile in xml format to temp_ dir.\\\"\\\"\\\"\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\\r\\n\\r\\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\\r\\n \\\"/data/sampleProfile.xml\\\")\\r\\n\\r\\n # The below code will parse the sample xml file\\r\\n # and fill important details entered by the user.\\r\\n root_tree = parse_xml.getroot()\\r\\n root_tree[0].text = self.ssid.get()\\r\\n root_tree[1][0][0].text = self.ssid.get()\\r\\n root_tree[3].text = self.connection_mode.get().lower()\\r\\n security = root_tree[4][0]\\r\\n security[0][0].text = self.authentication.get()\\r\\n security[0][1].text = self.encryption.get()\\r\\n if self.authentication.get() != \\\"open\\\":\\r\\n etree.SubElement(security, \\\"sharedKey\\\")\\r\\n etree.SubElement(security[1], \\\"keyType\\\").text = \\\"passPhrase\\\"\\r\\n etree.SubElement(security[1], \\\"protected\\\").text = \\\"false\\\"\\r\\n etree.SubElement(security[1], \\\"keyMaterial\\\").text = self.password.get()\\r\\n\\r\\n # Save the xml file\\r\\n saving_file(root_tree)\\r\\n\\r\\n # Add profile to the system.\\r\\n temp_path = 'netsh wlan add profile filename=\\\"' + self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\"\\r\\n output_ = subprocess.run(temp_path + file_name + '.xml\\\"', shell=True,\\r\\n capture_output=True, text=True)\\r\\n os.remove(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\")\\r\\n\\r\\n # If unable to add profile.\\r\\n if output_.returncode != 0:\\r\\n message = \\\"Sorry, Unable to add profile.\\\\n(You entered wrong details \\\" \\\\\\r\\n \\\"or else you don't have admin rights.)\\\"\\r\\n image_ = \\\"error\\\"\\r\\n\\r\\n else:\\r\\n message = \\\"Profile added successfully (Please Refresh)\\\"\\r\\n image_ = \\\"warning\\\"\\r\\n\\r\\n MessageBox(self.parent, message, image_)\",\n \"def saveFile(self, filename):\\n ret = libxml2mod.xmlSaveFile(filename, self._o)\\n return ret\",\n \"def save(self, filename=None):\\n f = filename if filename else self.path\\n etree.register_namespace('', TEI)\\n etree.register_namespace('mith', MITH)\\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')\",\n \"def save(self, save_path=None):\\n if self._xml is None:\\n raise IOError(\\\"There's nothing to save\\\")\\n\\n path = self._path_to_xml if save_path is None else save_path\\n\\n with open(path, 'w') as f:\\n rough_string = Et.tostring(self._xml, 'utf-8')\\n par = etree.XMLParser(remove_blank_text=True)\\n elem = etree.XML(rough_string, parser=par)\\n parsed = minidom.parseString(etree.tostring(elem))\\n f.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def save_profiles(self, fout, save_hybrid_meta=True):\\n\\n self._init_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\\n self._write_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\",\n \"def save(self):\\n # TODO: save the file\",\n \"def write_tessprofiles(tessprofiles: Dict, online=False):\\n logger.info(f'Writing tessprofiles')\\n profile = TESSPROFILE_ONLINE_PATH if online else TESSPROFILE_PATH\\n with open(profile, 'w') as f:\\n json.dump(tessprofiles, f, indent=4)\",\n \"def save(self, filename):\\n pass\",\n \"def SaveXMLToDB(xmlFileName):\",\n \"def save(self):\\n with self.open(self.filename, 'wt') as fd:\\n for node in self.elements:\\n fd.write(node.text)\",\n \"def save(self, fname):\\n pass\",\n \"def save(self):\\n self.lock.acquire()\\n try:\\n self.xml.set(\\\"name\\\",self.name)\\n self.xml.set(\\\"room\\\",self.room)\\n self.xml.set(\\\"type\\\",self.type)\\n self.xml.find(\\\"address\\\").text = \\\":\\\".join([str(x) for x in self.address])\\n if self.pos is not None:\\n self.xml.find(\\\"pos\\\").text = \\\" \\\".join([str(x) for x in self.pos])\\n self.xml.find(\\\"icon\\\").text = self.icon\\n \\n finally:\\n self.lock.release()\\n \\n self.house.save_devices()\",\n \"def dumpProfiles(profiles):\\n with open(userProfilesDir, \\\"w\\\") as outfile:\\n outfile.writelines(json.dumps(profiles, indent=4))\\n outfile.close()\",\n \"def saveUsersProfiles_(self, plist):\\r\\n \\r\\n LogInfo(u\\\"Saving update profiles with PublicationDate %@\\\", plist[u\\\"PublicationDate\\\"])\\r\\n if not plist.writeToFile_atomically_(self.userUpdateProfilesPath, False):\\r\\n LogError(u\\\"Failed to write %@\\\", self.userUpdateProfilesPath)\",\n \"def save_file(self):\\n if self.select_path.text() != \\\"\\\":\\n filepath = self.select_path.text()\\n road_network = self.map_selection.currentText()\\n if self.map_selection.currentText() == \\\"User Defined\\\":\\n road_network = self.map_selection_user_defined.text()\\n gen_xml = GenerateXML(filepath, road_network)\\n gen_xml.main()\\n # remember Road Network for future\\n set_metadata(road_network_filepath=road_network)\\n else:\\n message = \\\"No export path was selected\\\"\\n iface.messageBar().pushMessage(\\\"Warning\\\", message, level=Qgis.Warning)\\n QgsMessageLog.logMessage(message, level=Qgis.Warning)\",\n \"def saveSettings(self):\\n self.userFiles.applyData()\\n self.userPersonal.applyData()\",\n \"def _save(self):\\n file = open(\\\"settings.ini\\\", \\\"w\\\")\\n self._parser.write(file)\\n file.close()\",\n \"def save_as(self, fname, base = None, indent = '', topns = True, namespaces = {}):\\n with codecs.open(fname, \\\"w\\\", encoding=\\\"utf-8\\\") as outf:\\n self.serialize_xml(outf.write, base=base, indent=indent, topns=topns, namespaces=namespaces)\",\n \"def save(self, filename: str):\\n dump(self, filename)\",\n \"def save(self, pretty=True):\\n self.endInstance()\\n if pretty:\\n _indent(self.root, whitespace=self._whiteSpace)\\n tree = ET.ElementTree(self.root)\\n tree.write(self.path, encoding=\\\"utf-8\\\", method='xml', xml_declaration=True)\\n if self.logger:\\n self.logger.info(\\\"Writing %s\\\", self.path)\",\n \"def save():\",\n \"def save(self):\\n settings_path = os.path.join(self.file_path, \\\"__file_data.json\\\")\\n f = open( settings_path, 'w' )\\n f.write( simplejson.dumps( self.fileList ) )\\n f.close()\\n\\n settings_path = os.path.join(self.file_path, \\\"__user_data.json\\\")\\n f = open( settings_path, 'w' )\\n f.write( simplejson.dumps( self.userList ) )\\n f.close()\",\n \"def save(self):\\r\\n with open(self.filename, 'wb') as configfile:\\r\\n self.write(configfile)\",\n \"def save(self, filename):\\n with open(filename, \\\"w\\\") as fp:\\n dump(self, fp)\",\n \"def load_profiles_from_file(file_path):\\r\\n profiles = {}\\r\\n\\r\\n lastused = \\\"\\\"\\r\\n\\r\\n if File.Exists(file_path):\\r\\n try:\\r\\n with StreamReader(file_path) as xmlfile:\\r\\n xmldoc = XmlDocument()\\r\\n xmldoc.Load(xmlfile)\\r\\n\\r\\n if xmldoc.DocumentElement.Name == \\\"Profiles\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profiles/Profile\\\")\\r\\n #Individual exported profiles are saved with the document element as Profile\\r\\n elif xmldoc.DocumentElement.Name == \\\"Profile\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profile\\\")\\r\\n\\r\\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\\r\\n elif xmldoc.DocumentElement.Name == \\\"Settings\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Settings/Setting\\\")\\r\\n elif xmldoc.DocumentElement.Name == \\\"Setting\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Setting\\\")\\r\\n\\r\\n #No valid root elements\\r\\n else:\\r\\n MessageBox.Show(file_path + \\\" is not a valid Library Organizer profile file.\\\", \\\"Not a valid profile file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n return profiles, lastused\\r\\n\\r\\n if nodes.Count > 0:\\r\\n for node in nodes: \\r\\n profile = Profile()\\r\\n profile.Name = node.Attributes[\\\"Name\\\"].Value\\r\\n result = profile.load_from_xml(node)\\r\\n\\r\\n #Error loading the profile\\r\\n if result == False:\\r\\n MessageBox.Show(\\\"An error occured loading the profile \\\" + profile.Name + \\\". That profile has been skipped.\\\")\\r\\n\\r\\n else:\\r\\n profiles[profile.Name] = profile\\r\\n\\r\\n\\r\\n #Load the last used profile\\r\\n rootnode = xmldoc.DocumentElement\\r\\n if rootnode.HasAttribute(\\\"LastUsed\\\"):\\r\\n lastused = rootnode.Attributes[\\\"LastUsed\\\"].Value.split(\\\",\\\")\\r\\n\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"Something seems to have gone wrong loading the xml file.\\\\n\\\\nThe error was:\\\\n\\\" + str(ex), \\\"Error loading file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n\\r\\n return profiles, lastused\",\n \"def saveToXml(self) -> org.jdom.Element:\\n ...\",\n \"def save_xml_file():\\n global output_on_display, import_lst, column_names, data\\n if data_base == '':\\n mistake_load_table()\\n else:\\n column_names = data[0]\\n step = len(column_names)\\n\\n save_name = asksaveasfilename(title=\\\"Select file\\\", filetypes=((\\\"XML\\\", \\\"*.xml\\\"), (\\\"all files\\\", \\\"*.*\\\")),\\n confirmoverwrite=True, defaultextension='.xml')\\n data = import_lst\\n\\n if len(data[0]) == step:\\n pass\\n else:\\n data = import_lst[step::]\\n\\n data2 = list(map(list, zip(*data)))\\n\\n data3 = {key: value for key, value in zip(column_names, data2)}\\n\\n column = list(data3.keys())\\n\\n df = pd.DataFrame(data3, columns=column)\\n\\n data_dict = df.to_dict(orient=\\\"records\\\")\\n with open('output.json', \\\"w+\\\") as f:\\n json.dump(data_dict, f, indent=4)\\n\\n xml_data = dicttoxml(data_dict).decode()\\n with open(save_name, \\\"w+\\\") as f:\\n f.write(xml_data)\\n\\n data.clear()\\n data2.clear()\\n data3.clear()\",\n \"def export_to_file(self, filename):\\n if len(filename.split(\\\".\\\")) == 1:\\n filename += \\\".xml\\\"\\n xmlstring = self._dommodel.toprettyxml(\\\" \\\", \\\"\\\\n\\\")\\n with open(filename, \\\"w\\\") as f:\\n f.write(xmlstring)\",\n \"def save(wn, change_list=None):\\n wordnet_yaml.save(wn, change_list)\\n save_all_xml(wn, change_list)\\n with codecs.open(\\\"wn.xml\\\", \\\"w\\\", \\\"utf-8\\\") as outp:\\n wn.to_xml(outp, True)\\n pickle.dump(wn, open(\\\"wn.pickle\\\", \\\"wb\\\"))\",\n \"def save_current_to_profile(self, profile_name, prof_desc='', prof_path='',\\n self_contained=False):\\n # Open the already existing profile\\n new_profile = profile(profile_name, workdir=os.path.dirname(prof_path))\\n\\n # shortcut\\n w3af_plugins = self._w3af_core.plugins\\n\\n # Save the enabled plugins\\n for plugin_type in w3af_plugins.get_plugin_types():\\n enabled_plugins = []\\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\\n enabled_plugins.append(plugin_name)\\n new_profile.set_enabled_plugins(plugin_type, enabled_plugins)\\n\\n # Save the plugin options\\n for plugin_type in w3af_plugins.get_plugin_types():\\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\\n plugin_options = w3af_plugins.get_plugin_options(plugin_type,\\n plugin_name)\\n if plugin_options:\\n new_profile.set_plugin_options(plugin_type,\\n plugin_name,\\n plugin_options,\\n self_contained=self_contained)\\n\\n # Save the profile targets\\n targets = cf.cf.get('targets')\\n if targets:\\n new_profile.set_target(' , '.join(t.url_string for t in targets))\\n\\n # Save the misc and http settings\\n misc_settings = MiscSettings()\\n new_profile.set_misc_settings(misc_settings.get_options())\\n new_profile.set_http_settings(\\n self._w3af_core.uri_opener.settings.get_options())\\n\\n # Save the profile name and description\\n new_profile.set_desc(prof_desc)\\n new_profile.set_name(profile_name)\\n\\n # Save the profile to the file\\n new_profile.save(profile_name)\\n\\n return new_profile\",\n \"def save_changes(self):\\n\\n velib, autolib, subway = None, None, None\\n for key, value in VELIB_SUBSCRIPTIONS.iteritems():\\n if self._velib.get() == value:\\n velib = key\\n break\\n for key, value in AUTOLIB_SUBSCRIPTIONS.iteritems():\\n if self._autolib.get() == value:\\n autolib = key\\n break\\n for key, value in SUBWAY_SUBSCRIPTIONS.iteritems():\\n if self._subway.get() == value:\\n subway = key\\n break\\n preferences = {\\n FASTEST: self._fastest.get(),\\n SHORTEST: self._shortest.get(),\\n CHEAPEST: self._cheapest.get(),\\n SIMPLEST: self._simplest.get(),\\n WEATHER_IMPACT: self._weather_impact.get(),\\n LESS_PAINFUL: self._less_painful.get(),\\n LESS_WALKING: self._less_walking.get()\\n }\\n\\n result = self._system.set_profile_settings(velib, autolib, subway, self._driving_licence.get(), preferences)\\n if not result[\\\"success\\\"]:\\n showerror('Erreur système', result[\\\"error\\\"])\\n return\\n\\n # Redirection vers la page principale\\n from settings import RideSettingsPage\\n self.pack_forget()\\n RideSettingsPage(self._window, self._system)\",\n \"def saveProfile(self, request):\\n return self._doProfile(request)\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.config.write(file)\\n file.close()\",\n \"def save(self) -> None:\\n self.save_metadata()\\n self.save_files()\",\n \"def saveToFile(self,filename):\\n path = os.path.dirname(__file__)+\\\"/\\\"+filename\\n stream = open(path,\\\"w\\\")\\n yaml.dump(self.parameters(),stream)\",\n \"def save(self, filename:str):\\n dump(self, filename=filename)\",\n \"def saveSettings(self):\\n self.genFiles.applyData()\\n self.genGraph.applyData()\",\n \"def _save(self):\\n self.stat=[]\\n for i in range(len(self.xml_names)):\\n self.stat.append(self.check[i].isChecked())\\n self.close()\",\n \"def save(self):\\n for p, c in self.configs_:\\n c.write(p)\",\n \"def save(self):\\n\\t\\tself.CONFIG.save()\\n\\t\\tself.temp_files.save()\",\n \"def save_xml(tree, file_name, folder_name):\\r\\n import os # ändrar plats för filer\\r\\n os.chdir(folder_name)\\r\\n tree.write(file_name) # Namnet på ny fil\\r\",\n \"def save_calibration_profile(self, filename: str) -> None:\\n pass\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.parser.write(file)\\n file.close()\",\n \"def save(self):\\n\\t\\tPath(self.PATH).mkdir(parents=True,exist_ok=True)\\n\\n\\t\\twith open(self.account_file, \\\"wb\\\") as file:\\n\\t\\t\\tpickle.dump(self, file)\",\n \"def save_profile(sender, instance, **kwargs):\\n instance.profile.save()\",\n \"def write_network_manager_profiles(self):\\n \\n if len(self.network_manager_profiles) > 0: # Make sure we have some profiles to migrate\\n for profile in self.network_manager_profiles:\\n profile.write_config()\\n profile.set_ownership()\\n with open(migration_sign, \\\"w\\\") as f:\\n pass\",\n \"def save():\\n pass\",\n \"def save_prefs(self):\\n prefs_file = open(expanduser(self.prefs_path), 'w')\\n pickle.dump(self.prefs, prefs_file)\",\n \"def save_file(self):\\n # paginate over deputies and senators getting their fields\\n fieldnames = set([])\\n congressmen = self.deputies + self.senators\\n for data in congressmen:\\n fieldnames = fieldnames.union(data.dump().keys())\\n\\n\\n with open(IDENTITY_FILE_UPDATED, 'a') as csvfile:\\n writer = csv.DictWriter(csvfile, fieldnames=list(fieldnames), delimiter=';')\\n writer.writeheader()\\n\\n for data in congressmen:\\n writer.writerow(data.dump())\",\n \"def to_xml_file(self, xml_file_path):\\n s = self.to_xml()\\n with open(xml_file_path, \\\"w+b\\\") as f:\\n f.write(s)\",\n \"def saveState(self, file):\\n state = self.context.getState(getPositions=True, getVelocities=True, getParameters=True, getIntegratorParameters=True)\\n xml = mm.XmlSerializer.serialize(state)\\n if isinstance(file, str):\\n with open(file, 'w') as f:\\n f.write(xml)\\n else:\\n file.write(xml)\",\n \"def save(self, filename):\\n o = open(filename, 'w')\\n o.write(self.write())\\n o.close()\",\n \"def Save(file=CONFIG):\\n\\tif file in files:\\n\\t\\tfiles[file].SaveFile()\",\n \"def save(self, filename):\\n with open(filename, 'w') as f:\\n pickle.dump((self.mean, self.std_dev), f)\",\n \"def save(self, filename):\\n \\n raise NotImplementedError(\\\"not implemented!\\\")\",\n \"def dump(self, filename, plist):\\r\\n xml = self.tostring(plist)\\r\\n fd = open(filename, 'wb')\\r\\n try:\\r\\n fd.write(xml)\\r\\n finally:\\r\\n fd.close()\",\n \"def saveXML(self, filename):\\n root = ET.Element('root')\\n pklot = ET.SubElement(root, \\\"ParkingLot\\\")\\n\\n lotname = ET.SubElement(pklot, \\\"LotName\\\", name=str(self.name))\\n idCounter = ET.SubElement(pklot, \\\"NextAvailableID\\\", counter=str(self.spotIDCounter))\\n for spot in self.parkingSpots:\\n ET.SubElement(pklot, 'Spot', id=str(spot.id), location=' '.join(str(x) for x in spot.location))\\n\\n tree = ET.ElementTree(root)\\n tree.write(filename)\",\n \"def save_model(self, fname, pretty=False):\\n with open(fname, \\\"w\\\") as f:\\n xml_str = ET.tostring(self.root, encoding=\\\"unicode\\\")\\n if pretty:\\n parsed_xml = xml.dom.minidom.parseString(xml_str)\\n xml_str = parsed_xml.toprettyxml(newl=\\\"\\\")\\n f.write(xml_str)\",\n \"def save():\\n log.info(\\\"Saving settings file\\\")\\n with open(SETTINGS_FILE, \\\"w\\\") as file:\\n json.dump(_names, file)\",\n \"def generate_files(input_path, output_path):\\n profiles = pd.read_csv(f'{input_path}/Profile/Profile.csv')\\n\\n # call all predictors\\n predicted = {f: pr.all_predictors[f].predict(profiles=profiles, base_folder=input_path)\\n for f in pr.features if f in pr.all_predictors}\\n\\n for i, (index, row) in enumerate(profiles.iterrows()):\\n file = open(os.path.join(output_path, f'{row[\\\"userid\\\"]}.xml'), 'w')\\n gender = pr.get_gender(predicted['gender'][row[1]]) if row[1] in predicted['gender'] else predicted['age_group_alternative'][row[1]]\\n open_ = predicted['open'][row[1]] if row[1] in predicted['open'] else 'null'\\n conscientious = predicted['conscientious'][row[1]] if row[1] in predicted['conscientious'] else 'null'\\n extrovert = predicted['extrovert'][row[1]] if row[1] in predicted['extrovert'] else 'null'\\n agreeable = predicted['agreeable'][row[1]] if row[1] in predicted['agreeable'] else 'null'\\n neurotic = predicted['neurotic'][row[1]] if row[1] in predicted['neurotic'] else 'null'\\n age = predicted['age_group'][row[1]] if row[1] in predicted['age_group'] else 'null'\\n\\n file.write(f'<user id=\\\"{row[1]}\\\" ')\\n file.write(f'age_group=\\\"{age}\\\" ')\\n file.write(f'gender=\\\"{gender}\\\" ')\\n file.write(f'extrovert=\\\"{extrovert}\\\" ')\\n file.write(f'neurotic=\\\"{neurotic}\\\" ')\\n file.write(f'agreeable=\\\"{agreeable}\\\" ')\\n file.write(f'open=\\\"{open_}\\\" ')\\n file.write(f'conscientious=\\\"{conscientious}\\\" />')\\n file.close()\",\n \"def save(self):\\n # TODO (Pierre): code\",\n \"def save(self, filename):\\n raise NotImplementedError\",\n \"def write(self, file_or_filename):\\n etMap = revert(self)\\n xmlTree = ET.ElementTree(etMap)\\n xmlTree.write(file_or_filename)\",\n \"def save_profile(self, request):\\n return self.profile_service.do_profile(request)\",\n \"def saveAs(self):\\n self.saveFile()\",\n \"def save(self):\\n path = self.user.get_session_path()\\n with open(path, 'a', encoding='utf8') as file:\\n self.write(file=file)\",\n \"def xmlwrite(self, doc, filename):\\n pathname = os.path.join(self.session.session_dir, filename)\\n f = open(pathname, \\\"w\\\")\\n doc.writexml(writer=f, indent=\\\"\\\", addindent=\\\" \\\", newl=\\\"\\\\n\\\", encoding=\\\"UTF-8\\\")\\n f.close()\",\n \"def save(self, filename):\\n pickle.dump(self, open(filename + '.p', 'wb'), 2)\",\n \"def save_profile():\\n state = request.get_json()\\n logger.debug(\\\"Roast Profile: %s\\\" % state)\\n c = mongo.db[app.config['PROFILE_COLLECTION']]\\n item = {'coffee': state.get('coffee'), 'roast': state.get('roast'),\\n 'drop_temp': state.get('drop_temp'),\\n 'brew_methods': state.get('brew_methods'),\\n 'notes': state.get('notes'), 'datetime': now_time(),\\n 'user': current_user.get_id()}\\n _id = c.insert(item)\\n return jsonify({'success': True})\",\n \"def save(self, includeInDefaultProfile=True):\\n\\n self.prefab.core.file_write(self.pathProfile, str(self), showout=True)\\n\\n # make sure we include our custom profile in the default\\n if includeInDefaultProfile is True:\\n if self.pathProfile != self.bash.profileDefault.pathProfile:\\n print(\\\"INCLUDE IN DEFAULT PROFILE:%s\\\" % self.pathProfile)\\n out = \\\"\\\"\\n inProfile = self.prefab.core.file_read(self.bash.profileDefault.pathProfile)\\n for line in inProfile.split(\\\"\\\\n\\\"):\\n if line.find(self.pathProfile) != -1:\\n continue\\n out += \\\"%s\\\\n\\\" % line\\n\\n out += \\\"\\\\nsource %s\\\\n\\\" % self.pathProfile\\n if out.replace(\\\"\\\\n\\\", \\\"\\\") != inProfile.replace(\\\"\\\\n\\\", \\\"\\\"):\\n self.prefab.core.file_write(self.bash.profileDefault.pathProfile, out)\\n self.bash.profileDefault.load()\\n\\n self.bash.reset() # do not remove !\",\n \"def save(self):\\n memento = self.create_memento()\\n import datetime\\n f = open(str(datetime.datetime.now()).replace(' ','_')+'.saved_story','w')\\n cPickle.dump(memento,f)\\n f.close()\\n zcanvas.message(\\\"Saved!\\\")\",\n \"def write_xml(self, xmlfile):\\n system.xml.write_file(xmlfile, self.status, 'status')\",\n \"def saveSettings(self):\\n helpers.saveFile(self.dataDir, self.settingsFilename, json.dumps(self.settings))\",\n \"def SaveWiredNetworkProfile(self, profilename):\\n def write_script_ent(prof, conf, script):\\n if not conf.has_option(prof, script):\\n conf.set(prof, script, None)\\n if profilename == \\\"\\\":\\n return \\\"500: Bad Profile name\\\"\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n config.remove_section(profilename)\\n config.add_section(profilename)\\n for x in self.WiredNetwork:\\n config.set(profilename, x, self.WiredNetwork[x])\\n write_script_ent(profilename, config, \\\"beforescript\\\")\\n write_script_ent(profilename, config, \\\"afterscript\\\")\\n write_script_ent(profilename, config, \\\"disconnectscript\\\")\\n config.write(open(self.wired_conf, \\\"w\\\"))\\n return \\\"100: Profile Written\\\"\",\n \"def saveGraph(self, filename):\\n nx.write_yaml(self.G,filename)\",\n \"def _save_data(self, filename):\\n if not os.path.isdir(os.path.dirname(filename)):\\n return False\\n with open(filename, 'w') as f:\\n f.truncate()\\n pickle.dump({\\n 'user_data': self.user_data,\\n 'api_data': self.api_data,\\n 'profiles': self.profiles\\n }, f)\",\n \"def saveToFile(self, filePath):\\n d = self.save()\\n with open(filePath, 'wb') as f:\\n f.write(d)\",\n \"def save(self,file):\\n\\n with open(file,\\\"w\\\") as f:\\n f.write(self.to_string())\",\n \"def save(self, export_path: str):\",\n \"def save(self, filename):\\n with open(filename, 'w') as f:\\n yaml.dump(self.to_dict(), f, sort_keys=False)\",\n \"def saver(filename = None):\\n save(self, filename)\",\n \"def save_xml(self, fileName, outputTextFileName, predictedObjects):\\n if self.verbose:\\n print('Saving xml file', outputTextFileName)\\n # root element\\n root = ET.Element(\\\"annotation\\\")\\n # annotation.filename\\n ET.SubElement(root, \\\"filename\\\").text = inputFileName\\n # For each predicted object\\n for i in range(len(predictedObjects)):\\n # annotation.object\\n predObject = ET.SubElement(root, \\\"object\\\")\\n # annotation.object.name\\n ET.SubElement(\\n predObject, \\\"name\\\").text = predictedObjects[i][0]\\n # annotation.object.confidence\\n ET.SubElement(predObject, \\\"confidence\\\").text = str(\\n predictedObjects[i][5])\\n # annotation.object.bndBox\\n bndBox = ET.SubElement(predObject, \\\"bndBox\\\")\\n # annotation.object.bndBox.xmin\\n ET.SubElement(bndBox, \\\"xmin\\\").text = str(\\n predictedObjects[i][1])\\n # annotation.object.bndBox.ymin\\n ET.SubElement(bndBox, \\\"ymin\\\").text = str(\\n predictedObjects[i][2])\\n # annotation.object.bndBox.xmax\\n ET.SubElement(bndBox, \\\"xmax\\\").text = str(\\n predictedObjects[i][3])\\n # annotation.object.bndBox.ymax\\n ET.SubElement(bndBox, \\\"ymax\\\").text = str(\\n predictedObjects[i][4])\\n # Making the xml string\\n xmlString = minidom.parseString(\\n ET.tostring(root)).toprettyxml(indent=\\\" \\\")\\n # Saving the xml file\\n with open(outputTextFileName, 'w') as f:\\n f.write(xmlString)\",\n \"def save (self, filename) :\\n\\t\\tserialFile = open (filename, \\\"wb\\\")\\n\\t\\tpickle.dump (self.production_rules, serialFile)\\n\\t\\tpickle.dump (self.unitrelation, serialFile)\\n\\t\\tpickle.dump (self.labels, serialFile)\\n\\t\\tpickle.dump (self.keeper, serialFile)\\n\\t\\tpickle.dump (self.strnodes, serialFile)\\n\\t\\tpickle.dump (self.tokens, serialFile)\\n\\t\\tserialFile.close()\",\n \"def save(self):\\n Preferences.setPrinter(\\n \\\"PrinterName\\\",\\n self.printerNameEdit.text())\\n if self.printerColorButton.isChecked():\\n Preferences.setPrinter(\\\"ColorMode\\\", 1)\\n else:\\n Preferences.setPrinter(\\\"ColorMode\\\", 0)\\n if self.printFirstPageFirstButton.isChecked():\\n Preferences.setPrinter(\\\"FirstPageFirst\\\", 1)\\n else:\\n Preferences.setPrinter(\\\"FirstPageFirst\\\", 0)\\n Preferences.setPrinter(\\n \\\"Magnification\\\",\\n self.printMagnificationSpinBox.value())\\n Preferences.setPrinter(\\\"HeaderFont\\\", self.printheaderFont)\\n Preferences.setPrinter(\\n \\\"LeftMargin\\\",\\n self.leftMarginSpinBox.value())\\n Preferences.setPrinter(\\n \\\"RightMargin\\\",\\n self.rightMarginSpinBox.value())\\n Preferences.setPrinter(\\n \\\"TopMargin\\\",\\n self.topMarginSpinBox.value())\\n Preferences.setPrinter(\\n \\\"BottomMargin\\\",\\n self.bottomMarginSpinBox.value())\\n Preferences.setPrinter(\\n \\\"Resolution\\\",\\n self.resolutionSpinBox.value())\",\n \"def _save_settings(self):\\n # data to be save :\\n # -----------------\\n # futurePivot node\\n\\n # create attributes\\n self._create_data_attribute()\\n\\n # connect futurePivot node\\n pm.connectAttr(\\n \\\"%s%s\\\" % (self._futurePivot.name(), \\\".message\\\"),\\n self._object.attr(\\\"pivotData.futurePivot\\\"),\\n f=True,\\n )\",\n \"def save_config_xml(self, fileName: str):\\n self._sim.saveConfigXML(fileName)\",\n \"def save(self):\\n sublime.save_settings(self.file_name)\",\n \"def write(self, filename):\\n \\n return self.model.write(filename,xml_declaration=True, encoding='utf-8')\",\n \"def save_manifest(self, filename: Text) -> None:\\r\\n file = open(filename, \\\"w\\\")\\r\\n for element in self.elements:\\r\\n line = element.url\\r\\n for tag in element.tags:\\r\\n line += \\\",\\\" + tag\\r\\n file.write(line + \\\"\\\\n\\\")\\r\\n file.close()\",\n \"def save(self, file_name, file_type=\\\"toml\\\"):\\n if file_type == \\\"toml\\\":\\n with open(file_name, mode=\\\"w\\\") as f:\\n toml.dump(self._dict_to_save(), f)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77402055","0.7153504","0.68901503","0.65989304","0.6585187","0.6567334","0.65197617","0.64477056","0.6292227","0.62706214","0.62006843","0.61452764","0.6128959","0.610915","0.6092176","0.6072949","0.60326505","0.60216737","0.5989765","0.5988323","0.5958493","0.59531224","0.594053","0.5936368","0.5935535","0.5903034","0.5895587","0.5894408","0.5844191","0.58375555","0.58342576","0.5823713","0.5805062","0.5795571","0.5781049","0.5775747","0.5773318","0.576982","0.57587415","0.5757878","0.5753509","0.57454044","0.5739221","0.5724335","0.5720253","0.57165885","0.57076395","0.57027537","0.5686365","0.56833005","0.5675989","0.5675286","0.5674545","0.5662083","0.5657937","0.5652499","0.56513727","0.5647197","0.56462455","0.5630456","0.56299186","0.5628525","0.5612299","0.5597681","0.5574267","0.556656","0.55661255","0.5554859","0.55497956","0.5546197","0.5541442","0.5541002","0.552605","0.5519419","0.5502621","0.5501084","0.5494734","0.5494139","0.5483182","0.5469911","0.5467914","0.5460067","0.54528224","0.54526305","0.5449979","0.5446024","0.54344475","0.5430417","0.54299986","0.54286903","0.5428155","0.54067427","0.54045284","0.54029924","0.540211","0.5400488","0.5393231","0.53753364","0.5367603","0.53665954"],"string":"[\n \"0.77402055\",\n \"0.7153504\",\n \"0.68901503\",\n \"0.65989304\",\n \"0.6585187\",\n \"0.6567334\",\n \"0.65197617\",\n \"0.64477056\",\n \"0.6292227\",\n \"0.62706214\",\n \"0.62006843\",\n \"0.61452764\",\n \"0.6128959\",\n \"0.610915\",\n \"0.6092176\",\n \"0.6072949\",\n \"0.60326505\",\n \"0.60216737\",\n \"0.5989765\",\n \"0.5988323\",\n \"0.5958493\",\n \"0.59531224\",\n \"0.594053\",\n \"0.5936368\",\n \"0.5935535\",\n \"0.5903034\",\n \"0.5895587\",\n \"0.5894408\",\n \"0.5844191\",\n \"0.58375555\",\n \"0.58342576\",\n \"0.5823713\",\n \"0.5805062\",\n \"0.5795571\",\n \"0.5781049\",\n \"0.5775747\",\n \"0.5773318\",\n \"0.576982\",\n \"0.57587415\",\n \"0.5757878\",\n \"0.5753509\",\n \"0.57454044\",\n \"0.5739221\",\n \"0.5724335\",\n \"0.5720253\",\n \"0.57165885\",\n \"0.57076395\",\n \"0.57027537\",\n \"0.5686365\",\n \"0.56833005\",\n \"0.5675989\",\n \"0.5675286\",\n \"0.5674545\",\n \"0.5662083\",\n \"0.5657937\",\n \"0.5652499\",\n \"0.56513727\",\n \"0.5647197\",\n \"0.56462455\",\n \"0.5630456\",\n \"0.56299186\",\n \"0.5628525\",\n \"0.5612299\",\n \"0.5597681\",\n \"0.5574267\",\n \"0.556656\",\n \"0.55661255\",\n \"0.5554859\",\n \"0.55497956\",\n \"0.5546197\",\n \"0.5541442\",\n \"0.5541002\",\n \"0.552605\",\n \"0.5519419\",\n \"0.5502621\",\n \"0.5501084\",\n \"0.5494734\",\n \"0.5494139\",\n \"0.5483182\",\n \"0.5469911\",\n \"0.5467914\",\n \"0.5460067\",\n \"0.54528224\",\n \"0.54526305\",\n \"0.5449979\",\n \"0.5446024\",\n \"0.54344475\",\n \"0.5430417\",\n \"0.54299986\",\n \"0.54286903\",\n \"0.5428155\",\n \"0.54067427\",\n \"0.54045284\",\n \"0.54029924\",\n \"0.540211\",\n \"0.5400488\",\n \"0.5393231\",\n \"0.53753364\",\n \"0.5367603\",\n \"0.53665954\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76685476"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11593,"cells":{"query":{"kind":"string","value":"Saves a single profile to an xml file."},"document":{"kind":"string","value":"def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save_profile(self):\n self.save()","def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\r\n if lastused:\r\n writer.WriteAttributeString(\"LastUsed\", \",\".join(lastused))\r\n for profile in profiles:\r\n profiles[profile].save_to_xml(writer)\r\n writer.WriteEndElement()\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured writing the settings file. The error was:\\n\\n\" + ex.message, \"Error saving settings file\", MessageBoxButtons.OK, MessageBoxIcon.Error)","def save_profile(self, dir):\n filename = \"profile.pkl\"\n with open(osp.join(dir, filename), \"wb\") as f:\n pickle.dump(self, f)","def save_to_xml(self, xwriter):\r\n\r\n xwriter.WriteStartElement(\"Profile\")\r\n xwriter.WriteAttributeString(\"Name\", self.Name)\r\n xwriter.WriteStartAttribute(\"Version\")\r\n xwriter.WriteValue(self.Version)\r\n xwriter.WriteEndAttribute()\r\n\r\n for var_name in self.__dict__:\r\n var_type = type(getattr(self, var_name))\r\n\r\n if var_type is str and var_name != \"Name\":\r\n self.write_string_to_xml(var_name, xwriter)\r\n\r\n elif var_type is bool:\r\n self.write_bool_to_xml(var_name, xwriter)\r\n\r\n elif var_type is dict:\r\n self.write_dict_to_xml(var_name, xwriter)\r\n\r\n elif var_type is list and var_name != \"ExcludeRules\":\r\n self.write_list_to_xml(var_name, xwriter)\r\n\r\n xwriter.WriteStartElement(\"ExcludeRules\")\r\n xwriter.WriteAttributeString(\"Operator\", self.ExcludeOperator)\r\n xwriter.WriteAttributeString(\"ExcludeMode\", self.ExcludeMode)\r\n for rule in self.ExcludeRules:\r\n if rule:\r\n rule.save_xml(xwriter)\r\n xwriter.WriteEndElement()\r\n \r\n xwriter.WriteEndElement()","def Save_Current_Profile(self):\r\n #name = tkFileDialog.asksaveasfilename()\r\n #if( name == \"\" ):\r\n # return\r\n #self.system.Save_Current_Profile(name)\r\n self.system.Save_Current_Profile()","def save_profile(sender, instance, **kwargs):\n instance.profile.save()","def saveProfile(self, request):\n return self._doProfile(request)","def saving_file(xml):\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))","def save_user_profile(instance, **_):\n instance.profile.save()","def save_current_to_profile(self, profile_name, prof_desc='', prof_path='',\n self_contained=False):\n # Open the already existing profile\n new_profile = profile(profile_name, workdir=os.path.dirname(prof_path))\n\n # shortcut\n w3af_plugins = self._w3af_core.plugins\n\n # Save the enabled plugins\n for plugin_type in w3af_plugins.get_plugin_types():\n enabled_plugins = []\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\n enabled_plugins.append(plugin_name)\n new_profile.set_enabled_plugins(plugin_type, enabled_plugins)\n\n # Save the plugin options\n for plugin_type in w3af_plugins.get_plugin_types():\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\n plugin_options = w3af_plugins.get_plugin_options(plugin_type,\n plugin_name)\n if plugin_options:\n new_profile.set_plugin_options(plugin_type,\n plugin_name,\n plugin_options,\n self_contained=self_contained)\n\n # Save the profile targets\n targets = cf.cf.get('targets')\n if targets:\n new_profile.set_target(' , '.join(t.url_string for t in targets))\n\n # Save the misc and http settings\n misc_settings = MiscSettings()\n new_profile.set_misc_settings(misc_settings.get_options())\n new_profile.set_http_settings(\n self._w3af_core.uri_opener.settings.get_options())\n\n # Save the profile name and description\n new_profile.set_desc(prof_desc)\n new_profile.set_name(profile_name)\n\n # Save the profile to the file\n new_profile.save(profile_name)\n\n return new_profile","def save(self, save_dir):\n ProfileManager.save_data_to_disk(self.player_data, path.join(save_dir, self.player_name + '.yaml'))","def save_profile(self, request):\n return self.profile_service.do_profile(request)","def save(self, save_path=None):\n if self._xml is None:\n raise IOError(\"There's nothing to save\")\n\n path = self._path_to_xml if save_path is None else save_path\n\n with open(path, 'w') as f:\n rough_string = Et.tostring(self._xml, 'utf-8')\n par = etree.XMLParser(remove_blank_text=True)\n elem = etree.XML(rough_string, parser=par)\n parsed = minidom.parseString(etree.tostring(elem))\n f.write(parsed.toprettyxml(indent=\" \"))","def save(self, fname):\n pass","def save_profile():\n state = request.get_json()\n logger.debug(\"Roast Profile: %s\" % state)\n c = mongo.db[app.config['PROFILE_COLLECTION']]\n item = {'coffee': state.get('coffee'), 'roast': state.get('roast'),\n 'drop_temp': state.get('drop_temp'),\n 'brew_methods': state.get('brew_methods'),\n 'notes': state.get('notes'), 'datetime': now_time(),\n 'user': current_user.get_id()}\n _id = c.insert(item)\n return jsonify({'success': True})","def save_xml(self, filename):\n if \".xml\" not in filename:\n filename = filename + \".xml\"\n\n shutil.copyfile(self.env.model_file, filename)","def save_user_profile(sender, instance, **kwargs):\n instance.profile.save()","def save_user_profile(sender, instance, **kwargs):\n instance.profile.save()","def save_calibration_profile(self, filename: str) -> None:\n pass","def saveSessionToXML(self, filename):\r\n xmlStr = self.createXMLStr()\r\n \r\n #Write to the file\r\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\r\n xmlFile = open(filename, 'w')\r\n xmlFile.write(xmlStr)\r\n xmlFile.close()","def saveFile(self, filename):\n ret = libxml2mod.xmlSaveFile(filename, self._o)\n return ret","def save(self, filename):\n pass","def save():","def apply(self):\r\n\r\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\r\n\r\n def saving_file(xml):\r\n \"\"\" Save user profile in xml format to temp_ dir.\"\"\"\r\n\r\n xml_string = etree.tostring(xml)\r\n parsed = minidom.parseString(xml_string)\r\n with open(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\", \"w\") as file:\r\n file.write(parsed.toprettyxml(indent=\" \"))\r\n\r\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\r\n \"/data/sampleProfile.xml\")\r\n\r\n # The below code will parse the sample xml file\r\n # and fill important details entered by the user.\r\n root_tree = parse_xml.getroot()\r\n root_tree[0].text = self.ssid.get()\r\n root_tree[1][0][0].text = self.ssid.get()\r\n root_tree[3].text = self.connection_mode.get().lower()\r\n security = root_tree[4][0]\r\n security[0][0].text = self.authentication.get()\r\n security[0][1].text = self.encryption.get()\r\n if self.authentication.get() != \"open\":\r\n etree.SubElement(security, \"sharedKey\")\r\n etree.SubElement(security[1], \"keyType\").text = \"passPhrase\"\r\n etree.SubElement(security[1], \"protected\").text = \"false\"\r\n etree.SubElement(security[1], \"keyMaterial\").text = self.password.get()\r\n\r\n # Save the xml file\r\n saving_file(root_tree)\r\n\r\n # Add profile to the system.\r\n temp_path = 'netsh wlan add profile filename=\"' + self.app_path + \"\\\\temp_\\\\\"\r\n output_ = subprocess.run(temp_path + file_name + '.xml\"', shell=True,\r\n capture_output=True, text=True)\r\n os.remove(self.app_path + \"\\\\temp_\\\\\" + file_name + \".xml\")\r\n\r\n # If unable to add profile.\r\n if output_.returncode != 0:\r\n message = \"Sorry, Unable to add profile.\\n(You entered wrong details \" \\\r\n \"or else you don't have admin rights.)\"\r\n image_ = \"error\"\r\n\r\n else:\r\n message = \"Profile added successfully (Please Refresh)\"\r\n image_ = \"warning\"\r\n\r\n MessageBox(self.parent, message, image_)","def save(self, filename=None):\n f = filename if filename else self.path\n etree.register_namespace('', TEI)\n etree.register_namespace('mith', MITH)\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')","def write_tessprofiles(tessprofiles: Dict, online=False):\n logger.info(f'Writing tessprofiles')\n profile = TESSPROFILE_ONLINE_PATH if online else TESSPROFILE_PATH\n with open(profile, 'w') as f:\n json.dump(tessprofiles, f, indent=4)","def Save_xml(self, accounts):\n try:\n\n self.extension = \".xml\"\n\n colors.info(\"Saving as XML in {}{}\".format(self.file, self.extension))\n\n Main = ET.Element(\"SpotCheck\")\n\n SpotifyFree = ET.SubElement(Main, 'SpotifyFree')\n SpotifyPremium = ET.SubElement(Main, 'SpotifyPremium')\n PremiumFamily = ET.SubElement(Main, 'PremiumFamily')\n AdminPremiumFamily = ET.SubElement(Main, 'AdminPremiumFamily')\n BadAccounts = ET.SubElement(Main, 'BadAccounts')\n\n for account in accounts:\n if account.get(\"account_login\") == \"error\":\n temp = ET.SubElement(BadAccounts, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n else:\n if account.get(\"AccountType\") == \"Spotify Free\":\n temp = ET.SubElement(SpotifyFree, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n elif account.get(\"AccountType\") == \"Spotify Premium\":\n temp = ET.SubElement(SpotifyPremium, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n elif account.get(\"AccountType\") == \"Premium Family\":\n if account.get(\"Admin\"):\n temp = ET.SubElement(AdminPremiumFamily, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n else:\n temp = ET.SubElement(PremiumFamily, \"account\")\n temp.set(\"Username\", account[\"Username\"])\n temp.set(\"Password\", account[\"Password\"])\n temp.set(\"Country\", account[\"Country\"])\n XML = ET.tostring(Main)\n with open(self.file + self.extension, \"w\") as output_:\n output_.write(XML)\n colors.correct(\"Done! All saved successfully\")\n except Exception as e:\n colors.error(str(e))\n _exit(1)","def save(self, filename):\n with open(filename, \"w\") as fp:\n dump(self, fp)","def save(self):\n # TODO: save the file","def save_as(self, fname, base = None, indent = '', topns = True, namespaces = {}):\n with codecs.open(fname, \"w\", encoding=\"utf-8\") as outf:\n self.serialize_xml(outf.write, base=base, indent=indent, topns=topns, namespaces=namespaces)","def save(self):\n path = self.user.get_session_path()\n with open(path, 'a', encoding='utf8') as file:\n self.write(file=file)","def save():\n pass","def save(self, pretty=True):\n self.endInstance()\n if pretty:\n _indent(self.root, whitespace=self._whiteSpace)\n tree = ET.ElementTree(self.root)\n tree.write(self.path, encoding=\"utf-8\", method='xml', xml_declaration=True)\n if self.logger:\n self.logger.info(\"Writing %s\", self.path)","def save(self, filename: str):\n dump(self, filename)","def save(self, filename):\n o = open(filename, 'w')\n o.write(self.write())\n o.close()","def save_profiles(self, fout, save_hybrid_meta=True):\n\n self._init_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\n self._write_h5_out(fout, save_hybrid_meta=save_hybrid_meta)","def save_current_to_new_profile(self, profile_name, profile_desc='',\n self_contained=False):\n # Create the new profile.\n profile_inst = profile()\n profile_inst.set_desc(profile_desc)\n profile_inst.set_name(profile_name)\n profile_inst.save(profile_name)\n\n # Save current to profile\n return self.save_current_to_profile(profile_name, profile_desc,\n self_contained=self_contained)","def _save(self):\n file = open(\"settings.ini\", \"w\")\n self._parser.write(file)\n file.close()","def _save_helper(self, filepath, data_dict):\n # Set Default filepath\n if filepath is None:\n filepath = \"profile-{}.pkl\".format(\n datetime.now().strftime(\"%d-%b-%Y-%H:%M:%S.%f\"))\n\n # Remove data labelers as they can't be pickled\n data_labelers = self._remove_data_labelers()\n\n # add profiler class to data_dict\n data_dict['profiler_class'] = self.__class__.__name__\n\n # Pickle and save profile to disk\n with open(filepath, \"wb\") as outfile:\n pickle.dump(data_dict, outfile)\n\n # Restore all data labelers\n self._restore_data_labelers(data_labelers)","def save(self, file_name, file_type=\"toml\"):\n if file_type == \"toml\":\n with open(file_name, mode=\"w\") as f:\n toml.dump(self._dict_to_save(), f)","def saveOkcupidProfile(self, username, profile_source):\n\t\tdata = lzma.compress(profile_source.encode())\n\t\tencoded = base64.b64encode(data).decode('utf-8')\n\t\tuser = self.getOkcupidUser(username)\n\t\tif not user:\n\t\t\tself.logger.info(\"Storing user profile: %s\", username)\n\t\t\tuser = Models.Okcupid(username)\n\t\t\tuser.source = encoded\n\t\t\tself.session.add(user)\n\t\t\tself.session.commit()\n\t\telse:\n\t\t\tself.logger.info(\"Updating user profile: %s\", username)\n\t\t\tuser.source = encoded\n\t\t\tself.session.commit()","def save(self):\n\t\tPath(self.PATH).mkdir(parents=True,exist_ok=True)\n\n\t\twith open(self.account_file, \"wb\") as file:\n\t\t\tpickle.dump(self, file)","def saver(filename = None):\n save(self, filename)","def save(self, filename:str):\n dump(self, filename=filename)","def save(self, filename):\n raise NotImplementedError","def save(self):\n self.lock.acquire()\n try:\n self.xml.set(\"name\",self.name)\n self.xml.set(\"room\",self.room)\n self.xml.set(\"type\",self.type)\n self.xml.find(\"address\").text = \":\".join([str(x) for x in self.address])\n if self.pos is not None:\n self.xml.find(\"pos\").text = \" \".join([str(x) for x in self.pos])\n self.xml.find(\"icon\").text = self.icon\n \n finally:\n self.lock.release()\n \n self.house.save_devices()","def save(self):\n memento = self.create_memento()\n import datetime\n f = open(str(datetime.datetime.now()).replace(' ','_')+'.saved_story','w')\n cPickle.dump(memento,f)\n f.close()\n zcanvas.message(\"Saved!\")","def save(self, filename):\n \n raise NotImplementedError(\"not implemented!\")","def save(self, includeInDefaultProfile=True):\n\n self.prefab.core.file_write(self.pathProfile, str(self), showout=True)\n\n # make sure we include our custom profile in the default\n if includeInDefaultProfile is True:\n if self.pathProfile != self.bash.profileDefault.pathProfile:\n print(\"INCLUDE IN DEFAULT PROFILE:%s\" % self.pathProfile)\n out = \"\"\n inProfile = self.prefab.core.file_read(self.bash.profileDefault.pathProfile)\n for line in inProfile.split(\"\\n\"):\n if line.find(self.pathProfile) != -1:\n continue\n out += \"%s\\n\" % line\n\n out += \"\\nsource %s\\n\" % self.pathProfile\n if out.replace(\"\\n\", \"\") != inProfile.replace(\"\\n\", \"\"):\n self.prefab.core.file_write(self.bash.profileDefault.pathProfile, out)\n self.bash.profileDefault.load()\n\n self.bash.reset() # do not remove !","def saveState(self, file):\n state = self.context.getState(getPositions=True, getVelocities=True, getParameters=True, getIntegratorParameters=True)\n xml = mm.XmlSerializer.serialize(state)\n if isinstance(file, str):\n with open(file, 'w') as f:\n f.write(xml)\n else:\n file.write(xml)","def Save(file=CONFIG):\n\tif file in files:\n\t\tfiles[file].SaveFile()","def save_user_profile(user_profile):\r\n config_settings = config.get_config()\r\n db_client = boto3.resource('dynamodb', region_name = config_settings['region'])\r\n table = db_client.Table(config_settings['table_name'])\r\n\r\n try:\r\n table.put_item(Item=user_profile)\r\n except ClientError as e:\r\n logger.error(\"Failed to save profile {}:{}\"\r\n .format(jsons.dumps(user_profile), e.response['Error']['Message']))","def saveUsersProfiles_(self, plist):\r\n \r\n LogInfo(u\"Saving update profiles with PublicationDate %@\", plist[u\"PublicationDate\"])\r\n if not plist.writeToFile_atomically_(self.userUpdateProfilesPath, False):\r\n LogError(u\"Failed to write %@\", self.userUpdateProfilesPath)","def save(self):\n file = open(self.path, 'w')\n self.parser.write(file)\n file.close()","def save_file(self):\n if self.select_path.text() != \"\":\n filepath = self.select_path.text()\n road_network = self.map_selection.currentText()\n if self.map_selection.currentText() == \"User Defined\":\n road_network = self.map_selection_user_defined.text()\n gen_xml = GenerateXML(filepath, road_network)\n gen_xml.main()\n # remember Road Network for future\n set_metadata(road_network_filepath=road_network)\n else:\n message = \"No export path was selected\"\n iface.messageBar().pushMessage(\"Warning\", message, level=Qgis.Warning)\n QgsMessageLog.logMessage(message, level=Qgis.Warning)","def save(self):\r\n with open(self.filename, 'wb') as configfile:\r\n self.write(configfile)","def save(self, filename):\n pass","def postEditUser(self, request):\n\n userForm = UserEditForm(instance=request.user, data=request.POST)\n profileForm = ProfileEditForm(instance=request.user.profile, data=request.POST)\n\n if userForm.is_valid() and profileForm.is_valid():\n userForm.save()\n profileForm.save()\n else:\n with open('file.txt', 'w', encoding='utf-8') as file:\n print(f'not valid', file=file)","def save_model(self, fname, pretty=False):\n with open(fname, \"w\") as f:\n xml_str = ET.tostring(self.root, encoding=\"unicode\")\n if pretty:\n parsed_xml = xml.dom.minidom.parseString(xml_str)\n xml_str = parsed_xml.toprettyxml(newl=\"\")\n f.write(xml_str)","def save(self, filepath):\n writer = json.dump if Config.isjson(filepath) else yaml.dump\n with open(filepath, 'w') as f:\n writer(dict(self), f)","def save(self, *args, **kwargs):\n\n kwargs[\"commit\"] = False\n user = super(JOSProfileForm, self).save(*args, **kwargs)\n\n try:\n profile = get_profile_for_user(user)\n profile_form = self.get_profile_fields_form()\n profile_form(self.data, self.files, instance=profile).save()\n except ProfileNotConfigured:\n pass\n\n return user","def saveToFile(self,filename):\n path = os.path.dirname(__file__)+\"/\"+filename\n stream = open(path,\"w\")\n yaml.dump(self.parameters(),stream)","def save(self, filename):\n pickle.dump(self, open(filename + '.p', 'wb'), 2)","def save(self,file):\n\n with open(file,\"w\") as f:\n f.write(self.to_string())","def saveToXml(self) -> org.jdom.Element:\n ...","def remove_saved_profile(self, profile):\n\n self.profiles.remove(profile)\n gamedata.GameData._delete_game_data_file(path.join(self.save_dir, profile.player_name + '.yaml'))","def save(self, fp):\n fp.write(self.dump())","def save(cls):\n playerdata = getAttributes(cls)\n Data.object_dump(playerdata, \"savedata.dat\")\n del playerdata","def SaveWiredNetworkProfile(self, profilename):\n def write_script_ent(prof, conf, script):\n if not conf.has_option(prof, script):\n conf.set(prof, script, None)\n if profilename == \"\":\n return \"500: Bad Profile name\"\n profilename = misc.to_unicode(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n config.remove_section(profilename)\n config.add_section(profilename)\n for x in self.WiredNetwork:\n config.set(profilename, x, self.WiredNetwork[x])\n write_script_ent(profilename, config, \"beforescript\")\n write_script_ent(profilename, config, \"afterscript\")\n write_script_ent(profilename, config, \"disconnectscript\")\n config.write(open(self.wired_conf, \"w\"))\n return \"100: Profile Written\"","def save(self, filepath=None):\n raise NotImplementedError()","def save(self, filename):\n with open(filename, 'w') as f:\n pickle.dump((self.mean, self.std_dev), f)","def save(self):\n file = open(self.path, 'w')\n self.config.write(file)\n file.close()","def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused","def SaveXMLToDB(xmlFileName):","def saveToFile(self, filePath):\n d = self.save()\n with open(filePath, 'wb') as f:\n f.write(d)","def save(self, filename):\n Application.save(self, self.MNU_SAVE)\n ooldtp.context(self.name)\n\n ldtp.waittillguiexist(self.SAVE_DLG)\n save_dialog = ooldtp.context(self.SAVE_DLG)\n \n save_dlg_txt_filename = save_dialog.getchild(self.SAVE_DLG_TXT_NAME)\n ldtp.wait(2)\n save_dlg_txt_filename.settextvalue(filename)\n\n save_dlg_btn_save = save_dialog.getchild(self.SAVE_DLG_BTN_SAVE)\n \n save_dlg_btn_save.click()\n\n ldtp.waittillguinotexist(self.SAVE_DLG)\n ldtp.wait(1)","def save(self, fname = None):\n return True","def wlanGetProfile(self, iface, profile, saveToFile=None):\n log.debug('WlanInterface wlanGetProfile() - profile:\"%s\" saveToFile:%s' % (profile,saveToFile))\n sProfile = c_wchar_p(profile)\n sProfileXML = c_wchar_p() # create_unicode_buffer(1024)\n flags = DWORD(0)\n grantedAccess = DWORD()\n ret = WlanGetProfile( self._handle, \n byref(iface.InterfaceGuid), \n sProfile,\n None,\n byref(sProfileXML),\n byref(flags),\n byref(grantedAccess))\n if ret != ERROR_SUCCESS:\n raise WinError(ret)\n profileXML = sProfileXML.value\n if saveToFile:\n open(saveToFile,'w').write(profileXML)\n return profileXML","def save(self):\n # TODO (Pierre): code","def save(self, path: str):\n pass","def save(self, path):\n pass","def save(self, path):\n pass","def save(self, path):\n pass","def saveAs(self):\n self.saveFile()","def dumpProfiles(profiles):\n with open(userProfilesDir, \"w\") as outfile:\n outfile.writelines(json.dumps(profiles, indent=4))\n outfile.close()","def save(obj, filename):\n import pickle\n with open(filename, 'w') as f:\n pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)","def save(self, export_path: str):","def save_xml(tree, file_name, folder_name):\r\n import os # ändrar plats för filer\r\n os.chdir(folder_name)\r\n tree.write(file_name) # Namnet på ny fil\r","def save(self):\n return self.save_as(self.filename)","def save_changes(self):\n\n velib, autolib, subway = None, None, None\n for key, value in VELIB_SUBSCRIPTIONS.iteritems():\n if self._velib.get() == value:\n velib = key\n break\n for key, value in AUTOLIB_SUBSCRIPTIONS.iteritems():\n if self._autolib.get() == value:\n autolib = key\n break\n for key, value in SUBWAY_SUBSCRIPTIONS.iteritems():\n if self._subway.get() == value:\n subway = key\n break\n preferences = {\n FASTEST: self._fastest.get(),\n SHORTEST: self._shortest.get(),\n CHEAPEST: self._cheapest.get(),\n SIMPLEST: self._simplest.get(),\n WEATHER_IMPACT: self._weather_impact.get(),\n LESS_PAINFUL: self._less_painful.get(),\n LESS_WALKING: self._less_walking.get()\n }\n\n result = self._system.set_profile_settings(velib, autolib, subway, self._driving_licence.get(), preferences)\n if not result[\"success\"]:\n showerror('Erreur système', result[\"error\"])\n return\n\n # Redirection vers la page principale\n from settings import RideSettingsPage\n self.pack_forget()\n RideSettingsPage(self._window, self._system)","def save(self, filename='test'):\n file = open(filename+'.txt','w')\n pickle.dump(self, file)\n file.close()","def saveSettings(self):\n self.userFiles.applyData()\n self.userPersonal.applyData()","def save(self, filename=\"fitter.pickle\"):\n\n with open(filename, \"wb\") as outfile:\n pickle.dump(self, outfile)","def save(self,filename):\n with open(filename,'wb') as f:\n pickle.dump(self,f)","def wlanCopyProfile(self, iface, profile, newProfile, ssid=None, pass_phrase=None, saveOrigProfile=None, saveNewProfile=None):\n sXML = self.wlanGetProfile(iface, profile, saveToFile=saveOrigProfile)\n #print sXML\n reProf = re.compile('<{0}>.*</{0}>'.format('name'))\n reProf2 = re.compile('<{0}>.*</{0}>'.format('name2'))\n reHex = re.compile('<{0}>.*</{0}>'.format('hex'))\n reProt = re.compile('<{0}>.*</{0}>'.format('protected'))\n reKeyM = re.compile('<{0}>.*</{0}>'.format('keyMaterial'))\n sNewXML = sXML\n # change the name of the profile\n sNewXML = reProf.sub('<{0}>{1}</{0}>'.format( 'name2',newProfile), sNewXML, 1) \n # remove the hex element in <SSID>\n sNewXML = reHex.sub('', sNewXML, 1)\n if ssid is not None:\n # change the name element in <SSID> to new SSID name\n sNewXML = reProf.sub('<{0}>{1}</{0}>'.format( 'name',ssid), sNewXML, 1) \n if pass_phrase is not None: \n # change the protected element from true to false in <sharedKey>\n sNewXML = reProt.sub('<{0}>{1}</{0}>'.format( 'protected','false'), sNewXML, 1) \n # change the keyMaterial element to the new pass phrase in <sharedKey>\n sNewXML = reKeyM.sub('<{0}>{1}</{0}>'.format( 'keyMaterial', pass_phrase), sNewXML, 1)\n # rename <name2> back to <name>\n sNewXML = reProf2.sub('<{0}>{1}</{0}>'.format( 'name',newProfile), sNewXML, 1) \n #print sNewXML\n if saveNewProfile is not None:\n open(saveNewProfile,'w').write(sNewXML)\n # set the new profile\n self.wlanSetProfile(iface, sNewXML)","def save(self, *args, **kwargs):\r\n\r\n if not self.trackerid:\r\n self.trackerid = generate_trackerid()\r\n super(Profile, self).save(*args, **kwargs)","def save(wn, change_list=None):\n wordnet_yaml.save(wn, change_list)\n save_all_xml(wn, change_list)\n with codecs.open(\"wn.xml\", \"w\", \"utf-8\") as outp:\n wn.to_xml(outp, True)\n pickle.dump(wn, open(\"wn.pickle\", \"wb\"))","def SaveWirelessNetworkProfile(self, id):\n def write_script_ent(prof, conf, script):\n if not conf.has_option(prof, script):\n conf.set(prof, script, None)\n config = ConfigParser.ConfigParser()\n config.read(self.wireless_conf)\n cur_network = self.LastScan[id]\n bssid_key = cur_network[\"bssid\"]\n essid_key = \"essid:\" + cur_network[\"essid\"]\n if config.has_section(bssid_key):\n config.remove_section(bssid_key)\n config.add_section(bssid_key)\n if cur_network[\"use_settings_globally\"]:\n if config.has_section(essid_key):\n config.remove_section(essid_key)\n config.add_section(essid_key)\n for x in cur_network:\n config.set(bssid_key, x, cur_network[x])\n if cur_network[\"use_settings_globally\"]:\n config.set(essid_key, x, cur_network[x])\n write_script_ent(bssid_key, config, \"beforescript\")\n write_script_ent(bssid_key, config, \"afterscript\")\n write_script_ent(bssid_key, config, \"disconnect\")\n if cur_network[\"use_settings_globally\"]:\n write_script_ent(essid_key, config, \"beforescript\")\n write_script_ent(essid_key, config, \"afterscript\")\n write_script_ent(essid_key, config, \"disconnect\")\n config.write(open(self.wireless_conf, \"w\"))","def save(self):\n self.click(\".action-save\")\n self.page.wait_for_ajax()","def save(self,filename): \n with open(filename, 'wb') as f:\n pickle.dump(self,f)"],"string":"[\n \"def save_profile(self):\\n self.save()\",\n \"def save_profiles(file_path, profiles, lastused=\\\"\\\"):\\r\\n try:\\r\\n xSettings = XmlWriterSettings()\\r\\n xSettings.Indent = True\\r\\n with XmlWriter.Create(file_path, xSettings) as writer:\\r\\n writer.WriteStartElement(\\\"Profiles\\\")\\r\\n if lastused:\\r\\n writer.WriteAttributeString(\\\"LastUsed\\\", \\\",\\\".join(lastused))\\r\\n for profile in profiles:\\r\\n profiles[profile].save_to_xml(writer)\\r\\n writer.WriteEndElement()\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"An error occured writing the settings file. The error was:\\\\n\\\\n\\\" + ex.message, \\\"Error saving settings file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\",\n \"def save_profile(self, dir):\\n filename = \\\"profile.pkl\\\"\\n with open(osp.join(dir, filename), \\\"wb\\\") as f:\\n pickle.dump(self, f)\",\n \"def save_to_xml(self, xwriter):\\r\\n\\r\\n xwriter.WriteStartElement(\\\"Profile\\\")\\r\\n xwriter.WriteAttributeString(\\\"Name\\\", self.Name)\\r\\n xwriter.WriteStartAttribute(\\\"Version\\\")\\r\\n xwriter.WriteValue(self.Version)\\r\\n xwriter.WriteEndAttribute()\\r\\n\\r\\n for var_name in self.__dict__:\\r\\n var_type = type(getattr(self, var_name))\\r\\n\\r\\n if var_type is str and var_name != \\\"Name\\\":\\r\\n self.write_string_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is bool:\\r\\n self.write_bool_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is dict:\\r\\n self.write_dict_to_xml(var_name, xwriter)\\r\\n\\r\\n elif var_type is list and var_name != \\\"ExcludeRules\\\":\\r\\n self.write_list_to_xml(var_name, xwriter)\\r\\n\\r\\n xwriter.WriteStartElement(\\\"ExcludeRules\\\")\\r\\n xwriter.WriteAttributeString(\\\"Operator\\\", self.ExcludeOperator)\\r\\n xwriter.WriteAttributeString(\\\"ExcludeMode\\\", self.ExcludeMode)\\r\\n for rule in self.ExcludeRules:\\r\\n if rule:\\r\\n rule.save_xml(xwriter)\\r\\n xwriter.WriteEndElement()\\r\\n \\r\\n xwriter.WriteEndElement()\",\n \"def Save_Current_Profile(self):\\r\\n #name = tkFileDialog.asksaveasfilename()\\r\\n #if( name == \\\"\\\" ):\\r\\n # return\\r\\n #self.system.Save_Current_Profile(name)\\r\\n self.system.Save_Current_Profile()\",\n \"def save_profile(sender, instance, **kwargs):\\n instance.profile.save()\",\n \"def saveProfile(self, request):\\n return self._doProfile(request)\",\n \"def saving_file(xml):\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def save_user_profile(instance, **_):\\n instance.profile.save()\",\n \"def save_current_to_profile(self, profile_name, prof_desc='', prof_path='',\\n self_contained=False):\\n # Open the already existing profile\\n new_profile = profile(profile_name, workdir=os.path.dirname(prof_path))\\n\\n # shortcut\\n w3af_plugins = self._w3af_core.plugins\\n\\n # Save the enabled plugins\\n for plugin_type in w3af_plugins.get_plugin_types():\\n enabled_plugins = []\\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\\n enabled_plugins.append(plugin_name)\\n new_profile.set_enabled_plugins(plugin_type, enabled_plugins)\\n\\n # Save the plugin options\\n for plugin_type in w3af_plugins.get_plugin_types():\\n for plugin_name in w3af_plugins.get_enabled_plugins(plugin_type):\\n plugin_options = w3af_plugins.get_plugin_options(plugin_type,\\n plugin_name)\\n if plugin_options:\\n new_profile.set_plugin_options(plugin_type,\\n plugin_name,\\n plugin_options,\\n self_contained=self_contained)\\n\\n # Save the profile targets\\n targets = cf.cf.get('targets')\\n if targets:\\n new_profile.set_target(' , '.join(t.url_string for t in targets))\\n\\n # Save the misc and http settings\\n misc_settings = MiscSettings()\\n new_profile.set_misc_settings(misc_settings.get_options())\\n new_profile.set_http_settings(\\n self._w3af_core.uri_opener.settings.get_options())\\n\\n # Save the profile name and description\\n new_profile.set_desc(prof_desc)\\n new_profile.set_name(profile_name)\\n\\n # Save the profile to the file\\n new_profile.save(profile_name)\\n\\n return new_profile\",\n \"def save(self, save_dir):\\n ProfileManager.save_data_to_disk(self.player_data, path.join(save_dir, self.player_name + '.yaml'))\",\n \"def save_profile(self, request):\\n return self.profile_service.do_profile(request)\",\n \"def save(self, save_path=None):\\n if self._xml is None:\\n raise IOError(\\\"There's nothing to save\\\")\\n\\n path = self._path_to_xml if save_path is None else save_path\\n\\n with open(path, 'w') as f:\\n rough_string = Et.tostring(self._xml, 'utf-8')\\n par = etree.XMLParser(remove_blank_text=True)\\n elem = etree.XML(rough_string, parser=par)\\n parsed = minidom.parseString(etree.tostring(elem))\\n f.write(parsed.toprettyxml(indent=\\\" \\\"))\",\n \"def save(self, fname):\\n pass\",\n \"def save_profile():\\n state = request.get_json()\\n logger.debug(\\\"Roast Profile: %s\\\" % state)\\n c = mongo.db[app.config['PROFILE_COLLECTION']]\\n item = {'coffee': state.get('coffee'), 'roast': state.get('roast'),\\n 'drop_temp': state.get('drop_temp'),\\n 'brew_methods': state.get('brew_methods'),\\n 'notes': state.get('notes'), 'datetime': now_time(),\\n 'user': current_user.get_id()}\\n _id = c.insert(item)\\n return jsonify({'success': True})\",\n \"def save_xml(self, filename):\\n if \\\".xml\\\" not in filename:\\n filename = filename + \\\".xml\\\"\\n\\n shutil.copyfile(self.env.model_file, filename)\",\n \"def save_user_profile(sender, instance, **kwargs):\\n instance.profile.save()\",\n \"def save_user_profile(sender, instance, **kwargs):\\n instance.profile.save()\",\n \"def save_calibration_profile(self, filename: str) -> None:\\n pass\",\n \"def saveSessionToXML(self, filename):\\r\\n xmlStr = self.createXMLStr()\\r\\n \\r\\n #Write to the file\\r\\n #xml.dom.ext.PrettyPrint(doc, open(filename, 'w'))\\r\\n xmlFile = open(filename, 'w')\\r\\n xmlFile.write(xmlStr)\\r\\n xmlFile.close()\",\n \"def saveFile(self, filename):\\n ret = libxml2mod.xmlSaveFile(filename, self._o)\\n return ret\",\n \"def save(self, filename):\\n pass\",\n \"def save():\",\n \"def apply(self):\\r\\n\\r\\n file_name = str(sum([ord(i) for i in self.ssid.get()]))\\r\\n\\r\\n def saving_file(xml):\\r\\n \\\"\\\"\\\" Save user profile in xml format to temp_ dir.\\\"\\\"\\\"\\r\\n\\r\\n xml_string = etree.tostring(xml)\\r\\n parsed = minidom.parseString(xml_string)\\r\\n with open(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\", \\\"w\\\") as file:\\r\\n file.write(parsed.toprettyxml(indent=\\\" \\\"))\\r\\n\\r\\n parse_xml = etree.parse(os.path.dirname(os.path.realpath(__file__)) +\\r\\n \\\"/data/sampleProfile.xml\\\")\\r\\n\\r\\n # The below code will parse the sample xml file\\r\\n # and fill important details entered by the user.\\r\\n root_tree = parse_xml.getroot()\\r\\n root_tree[0].text = self.ssid.get()\\r\\n root_tree[1][0][0].text = self.ssid.get()\\r\\n root_tree[3].text = self.connection_mode.get().lower()\\r\\n security = root_tree[4][0]\\r\\n security[0][0].text = self.authentication.get()\\r\\n security[0][1].text = self.encryption.get()\\r\\n if self.authentication.get() != \\\"open\\\":\\r\\n etree.SubElement(security, \\\"sharedKey\\\")\\r\\n etree.SubElement(security[1], \\\"keyType\\\").text = \\\"passPhrase\\\"\\r\\n etree.SubElement(security[1], \\\"protected\\\").text = \\\"false\\\"\\r\\n etree.SubElement(security[1], \\\"keyMaterial\\\").text = self.password.get()\\r\\n\\r\\n # Save the xml file\\r\\n saving_file(root_tree)\\r\\n\\r\\n # Add profile to the system.\\r\\n temp_path = 'netsh wlan add profile filename=\\\"' + self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\"\\r\\n output_ = subprocess.run(temp_path + file_name + '.xml\\\"', shell=True,\\r\\n capture_output=True, text=True)\\r\\n os.remove(self.app_path + \\\"\\\\\\\\temp_\\\\\\\\\\\" + file_name + \\\".xml\\\")\\r\\n\\r\\n # If unable to add profile.\\r\\n if output_.returncode != 0:\\r\\n message = \\\"Sorry, Unable to add profile.\\\\n(You entered wrong details \\\" \\\\\\r\\n \\\"or else you don't have admin rights.)\\\"\\r\\n image_ = \\\"error\\\"\\r\\n\\r\\n else:\\r\\n message = \\\"Profile added successfully (Please Refresh)\\\"\\r\\n image_ = \\\"warning\\\"\\r\\n\\r\\n MessageBox(self.parent, message, image_)\",\n \"def save(self, filename=None):\\n f = filename if filename else self.path\\n etree.register_namespace('', TEI)\\n etree.register_namespace('mith', MITH)\\n self.doc.write(f, xml_declaration=True, encoding='utf-8', method='xml')\",\n \"def write_tessprofiles(tessprofiles: Dict, online=False):\\n logger.info(f'Writing tessprofiles')\\n profile = TESSPROFILE_ONLINE_PATH if online else TESSPROFILE_PATH\\n with open(profile, 'w') as f:\\n json.dump(tessprofiles, f, indent=4)\",\n \"def Save_xml(self, accounts):\\n try:\\n\\n self.extension = \\\".xml\\\"\\n\\n colors.info(\\\"Saving as XML in {}{}\\\".format(self.file, self.extension))\\n\\n Main = ET.Element(\\\"SpotCheck\\\")\\n\\n SpotifyFree = ET.SubElement(Main, 'SpotifyFree')\\n SpotifyPremium = ET.SubElement(Main, 'SpotifyPremium')\\n PremiumFamily = ET.SubElement(Main, 'PremiumFamily')\\n AdminPremiumFamily = ET.SubElement(Main, 'AdminPremiumFamily')\\n BadAccounts = ET.SubElement(Main, 'BadAccounts')\\n\\n for account in accounts:\\n if account.get(\\\"account_login\\\") == \\\"error\\\":\\n temp = ET.SubElement(BadAccounts, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n else:\\n if account.get(\\\"AccountType\\\") == \\\"Spotify Free\\\":\\n temp = ET.SubElement(SpotifyFree, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n elif account.get(\\\"AccountType\\\") == \\\"Spotify Premium\\\":\\n temp = ET.SubElement(SpotifyPremium, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n elif account.get(\\\"AccountType\\\") == \\\"Premium Family\\\":\\n if account.get(\\\"Admin\\\"):\\n temp = ET.SubElement(AdminPremiumFamily, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n else:\\n temp = ET.SubElement(PremiumFamily, \\\"account\\\")\\n temp.set(\\\"Username\\\", account[\\\"Username\\\"])\\n temp.set(\\\"Password\\\", account[\\\"Password\\\"])\\n temp.set(\\\"Country\\\", account[\\\"Country\\\"])\\n XML = ET.tostring(Main)\\n with open(self.file + self.extension, \\\"w\\\") as output_:\\n output_.write(XML)\\n colors.correct(\\\"Done! All saved successfully\\\")\\n except Exception as e:\\n colors.error(str(e))\\n _exit(1)\",\n \"def save(self, filename):\\n with open(filename, \\\"w\\\") as fp:\\n dump(self, fp)\",\n \"def save(self):\\n # TODO: save the file\",\n \"def save_as(self, fname, base = None, indent = '', topns = True, namespaces = {}):\\n with codecs.open(fname, \\\"w\\\", encoding=\\\"utf-8\\\") as outf:\\n self.serialize_xml(outf.write, base=base, indent=indent, topns=topns, namespaces=namespaces)\",\n \"def save(self):\\n path = self.user.get_session_path()\\n with open(path, 'a', encoding='utf8') as file:\\n self.write(file=file)\",\n \"def save():\\n pass\",\n \"def save(self, pretty=True):\\n self.endInstance()\\n if pretty:\\n _indent(self.root, whitespace=self._whiteSpace)\\n tree = ET.ElementTree(self.root)\\n tree.write(self.path, encoding=\\\"utf-8\\\", method='xml', xml_declaration=True)\\n if self.logger:\\n self.logger.info(\\\"Writing %s\\\", self.path)\",\n \"def save(self, filename: str):\\n dump(self, filename)\",\n \"def save(self, filename):\\n o = open(filename, 'w')\\n o.write(self.write())\\n o.close()\",\n \"def save_profiles(self, fout, save_hybrid_meta=True):\\n\\n self._init_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\\n self._write_h5_out(fout, save_hybrid_meta=save_hybrid_meta)\",\n \"def save_current_to_new_profile(self, profile_name, profile_desc='',\\n self_contained=False):\\n # Create the new profile.\\n profile_inst = profile()\\n profile_inst.set_desc(profile_desc)\\n profile_inst.set_name(profile_name)\\n profile_inst.save(profile_name)\\n\\n # Save current to profile\\n return self.save_current_to_profile(profile_name, profile_desc,\\n self_contained=self_contained)\",\n \"def _save(self):\\n file = open(\\\"settings.ini\\\", \\\"w\\\")\\n self._parser.write(file)\\n file.close()\",\n \"def _save_helper(self, filepath, data_dict):\\n # Set Default filepath\\n if filepath is None:\\n filepath = \\\"profile-{}.pkl\\\".format(\\n datetime.now().strftime(\\\"%d-%b-%Y-%H:%M:%S.%f\\\"))\\n\\n # Remove data labelers as they can't be pickled\\n data_labelers = self._remove_data_labelers()\\n\\n # add profiler class to data_dict\\n data_dict['profiler_class'] = self.__class__.__name__\\n\\n # Pickle and save profile to disk\\n with open(filepath, \\\"wb\\\") as outfile:\\n pickle.dump(data_dict, outfile)\\n\\n # Restore all data labelers\\n self._restore_data_labelers(data_labelers)\",\n \"def save(self, file_name, file_type=\\\"toml\\\"):\\n if file_type == \\\"toml\\\":\\n with open(file_name, mode=\\\"w\\\") as f:\\n toml.dump(self._dict_to_save(), f)\",\n \"def saveOkcupidProfile(self, username, profile_source):\\n\\t\\tdata = lzma.compress(profile_source.encode())\\n\\t\\tencoded = base64.b64encode(data).decode('utf-8')\\n\\t\\tuser = self.getOkcupidUser(username)\\n\\t\\tif not user:\\n\\t\\t\\tself.logger.info(\\\"Storing user profile: %s\\\", username)\\n\\t\\t\\tuser = Models.Okcupid(username)\\n\\t\\t\\tuser.source = encoded\\n\\t\\t\\tself.session.add(user)\\n\\t\\t\\tself.session.commit()\\n\\t\\telse:\\n\\t\\t\\tself.logger.info(\\\"Updating user profile: %s\\\", username)\\n\\t\\t\\tuser.source = encoded\\n\\t\\t\\tself.session.commit()\",\n \"def save(self):\\n\\t\\tPath(self.PATH).mkdir(parents=True,exist_ok=True)\\n\\n\\t\\twith open(self.account_file, \\\"wb\\\") as file:\\n\\t\\t\\tpickle.dump(self, file)\",\n \"def saver(filename = None):\\n save(self, filename)\",\n \"def save(self, filename:str):\\n dump(self, filename=filename)\",\n \"def save(self, filename):\\n raise NotImplementedError\",\n \"def save(self):\\n self.lock.acquire()\\n try:\\n self.xml.set(\\\"name\\\",self.name)\\n self.xml.set(\\\"room\\\",self.room)\\n self.xml.set(\\\"type\\\",self.type)\\n self.xml.find(\\\"address\\\").text = \\\":\\\".join([str(x) for x in self.address])\\n if self.pos is not None:\\n self.xml.find(\\\"pos\\\").text = \\\" \\\".join([str(x) for x in self.pos])\\n self.xml.find(\\\"icon\\\").text = self.icon\\n \\n finally:\\n self.lock.release()\\n \\n self.house.save_devices()\",\n \"def save(self):\\n memento = self.create_memento()\\n import datetime\\n f = open(str(datetime.datetime.now()).replace(' ','_')+'.saved_story','w')\\n cPickle.dump(memento,f)\\n f.close()\\n zcanvas.message(\\\"Saved!\\\")\",\n \"def save(self, filename):\\n \\n raise NotImplementedError(\\\"not implemented!\\\")\",\n \"def save(self, includeInDefaultProfile=True):\\n\\n self.prefab.core.file_write(self.pathProfile, str(self), showout=True)\\n\\n # make sure we include our custom profile in the default\\n if includeInDefaultProfile is True:\\n if self.pathProfile != self.bash.profileDefault.pathProfile:\\n print(\\\"INCLUDE IN DEFAULT PROFILE:%s\\\" % self.pathProfile)\\n out = \\\"\\\"\\n inProfile = self.prefab.core.file_read(self.bash.profileDefault.pathProfile)\\n for line in inProfile.split(\\\"\\\\n\\\"):\\n if line.find(self.pathProfile) != -1:\\n continue\\n out += \\\"%s\\\\n\\\" % line\\n\\n out += \\\"\\\\nsource %s\\\\n\\\" % self.pathProfile\\n if out.replace(\\\"\\\\n\\\", \\\"\\\") != inProfile.replace(\\\"\\\\n\\\", \\\"\\\"):\\n self.prefab.core.file_write(self.bash.profileDefault.pathProfile, out)\\n self.bash.profileDefault.load()\\n\\n self.bash.reset() # do not remove !\",\n \"def saveState(self, file):\\n state = self.context.getState(getPositions=True, getVelocities=True, getParameters=True, getIntegratorParameters=True)\\n xml = mm.XmlSerializer.serialize(state)\\n if isinstance(file, str):\\n with open(file, 'w') as f:\\n f.write(xml)\\n else:\\n file.write(xml)\",\n \"def Save(file=CONFIG):\\n\\tif file in files:\\n\\t\\tfiles[file].SaveFile()\",\n \"def save_user_profile(user_profile):\\r\\n config_settings = config.get_config()\\r\\n db_client = boto3.resource('dynamodb', region_name = config_settings['region'])\\r\\n table = db_client.Table(config_settings['table_name'])\\r\\n\\r\\n try:\\r\\n table.put_item(Item=user_profile)\\r\\n except ClientError as e:\\r\\n logger.error(\\\"Failed to save profile {}:{}\\\"\\r\\n .format(jsons.dumps(user_profile), e.response['Error']['Message']))\",\n \"def saveUsersProfiles_(self, plist):\\r\\n \\r\\n LogInfo(u\\\"Saving update profiles with PublicationDate %@\\\", plist[u\\\"PublicationDate\\\"])\\r\\n if not plist.writeToFile_atomically_(self.userUpdateProfilesPath, False):\\r\\n LogError(u\\\"Failed to write %@\\\", self.userUpdateProfilesPath)\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.parser.write(file)\\n file.close()\",\n \"def save_file(self):\\n if self.select_path.text() != \\\"\\\":\\n filepath = self.select_path.text()\\n road_network = self.map_selection.currentText()\\n if self.map_selection.currentText() == \\\"User Defined\\\":\\n road_network = self.map_selection_user_defined.text()\\n gen_xml = GenerateXML(filepath, road_network)\\n gen_xml.main()\\n # remember Road Network for future\\n set_metadata(road_network_filepath=road_network)\\n else:\\n message = \\\"No export path was selected\\\"\\n iface.messageBar().pushMessage(\\\"Warning\\\", message, level=Qgis.Warning)\\n QgsMessageLog.logMessage(message, level=Qgis.Warning)\",\n \"def save(self):\\r\\n with open(self.filename, 'wb') as configfile:\\r\\n self.write(configfile)\",\n \"def save(self, filename):\\n pass\",\n \"def postEditUser(self, request):\\n\\n userForm = UserEditForm(instance=request.user, data=request.POST)\\n profileForm = ProfileEditForm(instance=request.user.profile, data=request.POST)\\n\\n if userForm.is_valid() and profileForm.is_valid():\\n userForm.save()\\n profileForm.save()\\n else:\\n with open('file.txt', 'w', encoding='utf-8') as file:\\n print(f'not valid', file=file)\",\n \"def save_model(self, fname, pretty=False):\\n with open(fname, \\\"w\\\") as f:\\n xml_str = ET.tostring(self.root, encoding=\\\"unicode\\\")\\n if pretty:\\n parsed_xml = xml.dom.minidom.parseString(xml_str)\\n xml_str = parsed_xml.toprettyxml(newl=\\\"\\\")\\n f.write(xml_str)\",\n \"def save(self, filepath):\\n writer = json.dump if Config.isjson(filepath) else yaml.dump\\n with open(filepath, 'w') as f:\\n writer(dict(self), f)\",\n \"def save(self, *args, **kwargs):\\n\\n kwargs[\\\"commit\\\"] = False\\n user = super(JOSProfileForm, self).save(*args, **kwargs)\\n\\n try:\\n profile = get_profile_for_user(user)\\n profile_form = self.get_profile_fields_form()\\n profile_form(self.data, self.files, instance=profile).save()\\n except ProfileNotConfigured:\\n pass\\n\\n return user\",\n \"def saveToFile(self,filename):\\n path = os.path.dirname(__file__)+\\\"/\\\"+filename\\n stream = open(path,\\\"w\\\")\\n yaml.dump(self.parameters(),stream)\",\n \"def save(self, filename):\\n pickle.dump(self, open(filename + '.p', 'wb'), 2)\",\n \"def save(self,file):\\n\\n with open(file,\\\"w\\\") as f:\\n f.write(self.to_string())\",\n \"def saveToXml(self) -> org.jdom.Element:\\n ...\",\n \"def remove_saved_profile(self, profile):\\n\\n self.profiles.remove(profile)\\n gamedata.GameData._delete_game_data_file(path.join(self.save_dir, profile.player_name + '.yaml'))\",\n \"def save(self, fp):\\n fp.write(self.dump())\",\n \"def save(cls):\\n playerdata = getAttributes(cls)\\n Data.object_dump(playerdata, \\\"savedata.dat\\\")\\n del playerdata\",\n \"def SaveWiredNetworkProfile(self, profilename):\\n def write_script_ent(prof, conf, script):\\n if not conf.has_option(prof, script):\\n conf.set(prof, script, None)\\n if profilename == \\\"\\\":\\n return \\\"500: Bad Profile name\\\"\\n profilename = misc.to_unicode(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n config.remove_section(profilename)\\n config.add_section(profilename)\\n for x in self.WiredNetwork:\\n config.set(profilename, x, self.WiredNetwork[x])\\n write_script_ent(profilename, config, \\\"beforescript\\\")\\n write_script_ent(profilename, config, \\\"afterscript\\\")\\n write_script_ent(profilename, config, \\\"disconnectscript\\\")\\n config.write(open(self.wired_conf, \\\"w\\\"))\\n return \\\"100: Profile Written\\\"\",\n \"def save(self, filepath=None):\\n raise NotImplementedError()\",\n \"def save(self, filename):\\n with open(filename, 'w') as f:\\n pickle.dump((self.mean, self.std_dev), f)\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.config.write(file)\\n file.close()\",\n \"def load_profiles_from_file(file_path):\\r\\n profiles = {}\\r\\n\\r\\n lastused = \\\"\\\"\\r\\n\\r\\n if File.Exists(file_path):\\r\\n try:\\r\\n with StreamReader(file_path) as xmlfile:\\r\\n xmldoc = XmlDocument()\\r\\n xmldoc.Load(xmlfile)\\r\\n\\r\\n if xmldoc.DocumentElement.Name == \\\"Profiles\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profiles/Profile\\\")\\r\\n #Individual exported profiles are saved with the document element as Profile\\r\\n elif xmldoc.DocumentElement.Name == \\\"Profile\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profile\\\")\\r\\n\\r\\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\\r\\n elif xmldoc.DocumentElement.Name == \\\"Settings\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Settings/Setting\\\")\\r\\n elif xmldoc.DocumentElement.Name == \\\"Setting\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Setting\\\")\\r\\n\\r\\n #No valid root elements\\r\\n else:\\r\\n MessageBox.Show(file_path + \\\" is not a valid Library Organizer profile file.\\\", \\\"Not a valid profile file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n return profiles, lastused\\r\\n\\r\\n if nodes.Count > 0:\\r\\n for node in nodes: \\r\\n profile = Profile()\\r\\n profile.Name = node.Attributes[\\\"Name\\\"].Value\\r\\n result = profile.load_from_xml(node)\\r\\n\\r\\n #Error loading the profile\\r\\n if result == False:\\r\\n MessageBox.Show(\\\"An error occured loading the profile \\\" + profile.Name + \\\". That profile has been skipped.\\\")\\r\\n\\r\\n else:\\r\\n profiles[profile.Name] = profile\\r\\n\\r\\n\\r\\n #Load the last used profile\\r\\n rootnode = xmldoc.DocumentElement\\r\\n if rootnode.HasAttribute(\\\"LastUsed\\\"):\\r\\n lastused = rootnode.Attributes[\\\"LastUsed\\\"].Value.split(\\\",\\\")\\r\\n\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"Something seems to have gone wrong loading the xml file.\\\\n\\\\nThe error was:\\\\n\\\" + str(ex), \\\"Error loading file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n\\r\\n return profiles, lastused\",\n \"def SaveXMLToDB(xmlFileName):\",\n \"def saveToFile(self, filePath):\\n d = self.save()\\n with open(filePath, 'wb') as f:\\n f.write(d)\",\n \"def save(self, filename):\\n Application.save(self, self.MNU_SAVE)\\n ooldtp.context(self.name)\\n\\n ldtp.waittillguiexist(self.SAVE_DLG)\\n save_dialog = ooldtp.context(self.SAVE_DLG)\\n \\n save_dlg_txt_filename = save_dialog.getchild(self.SAVE_DLG_TXT_NAME)\\n ldtp.wait(2)\\n save_dlg_txt_filename.settextvalue(filename)\\n\\n save_dlg_btn_save = save_dialog.getchild(self.SAVE_DLG_BTN_SAVE)\\n \\n save_dlg_btn_save.click()\\n\\n ldtp.waittillguinotexist(self.SAVE_DLG)\\n ldtp.wait(1)\",\n \"def save(self, fname = None):\\n return True\",\n \"def wlanGetProfile(self, iface, profile, saveToFile=None):\\n log.debug('WlanInterface wlanGetProfile() - profile:\\\"%s\\\" saveToFile:%s' % (profile,saveToFile))\\n sProfile = c_wchar_p(profile)\\n sProfileXML = c_wchar_p() # create_unicode_buffer(1024)\\n flags = DWORD(0)\\n grantedAccess = DWORD()\\n ret = WlanGetProfile( self._handle, \\n byref(iface.InterfaceGuid), \\n sProfile,\\n None,\\n byref(sProfileXML),\\n byref(flags),\\n byref(grantedAccess))\\n if ret != ERROR_SUCCESS:\\n raise WinError(ret)\\n profileXML = sProfileXML.value\\n if saveToFile:\\n open(saveToFile,'w').write(profileXML)\\n return profileXML\",\n \"def save(self):\\n # TODO (Pierre): code\",\n \"def save(self, path: str):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def save(self, path):\\n pass\",\n \"def saveAs(self):\\n self.saveFile()\",\n \"def dumpProfiles(profiles):\\n with open(userProfilesDir, \\\"w\\\") as outfile:\\n outfile.writelines(json.dumps(profiles, indent=4))\\n outfile.close()\",\n \"def save(obj, filename):\\n import pickle\\n with open(filename, 'w') as f:\\n pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"def save(self, export_path: str):\",\n \"def save_xml(tree, file_name, folder_name):\\r\\n import os # ändrar plats för filer\\r\\n os.chdir(folder_name)\\r\\n tree.write(file_name) # Namnet på ny fil\\r\",\n \"def save(self):\\n return self.save_as(self.filename)\",\n \"def save_changes(self):\\n\\n velib, autolib, subway = None, None, None\\n for key, value in VELIB_SUBSCRIPTIONS.iteritems():\\n if self._velib.get() == value:\\n velib = key\\n break\\n for key, value in AUTOLIB_SUBSCRIPTIONS.iteritems():\\n if self._autolib.get() == value:\\n autolib = key\\n break\\n for key, value in SUBWAY_SUBSCRIPTIONS.iteritems():\\n if self._subway.get() == value:\\n subway = key\\n break\\n preferences = {\\n FASTEST: self._fastest.get(),\\n SHORTEST: self._shortest.get(),\\n CHEAPEST: self._cheapest.get(),\\n SIMPLEST: self._simplest.get(),\\n WEATHER_IMPACT: self._weather_impact.get(),\\n LESS_PAINFUL: self._less_painful.get(),\\n LESS_WALKING: self._less_walking.get()\\n }\\n\\n result = self._system.set_profile_settings(velib, autolib, subway, self._driving_licence.get(), preferences)\\n if not result[\\\"success\\\"]:\\n showerror('Erreur système', result[\\\"error\\\"])\\n return\\n\\n # Redirection vers la page principale\\n from settings import RideSettingsPage\\n self.pack_forget()\\n RideSettingsPage(self._window, self._system)\",\n \"def save(self, filename='test'):\\n file = open(filename+'.txt','w')\\n pickle.dump(self, file)\\n file.close()\",\n \"def saveSettings(self):\\n self.userFiles.applyData()\\n self.userPersonal.applyData()\",\n \"def save(self, filename=\\\"fitter.pickle\\\"):\\n\\n with open(filename, \\\"wb\\\") as outfile:\\n pickle.dump(self, outfile)\",\n \"def save(self,filename):\\n with open(filename,'wb') as f:\\n pickle.dump(self,f)\",\n \"def wlanCopyProfile(self, iface, profile, newProfile, ssid=None, pass_phrase=None, saveOrigProfile=None, saveNewProfile=None):\\n sXML = self.wlanGetProfile(iface, profile, saveToFile=saveOrigProfile)\\n #print sXML\\n reProf = re.compile('<{0}>.*</{0}>'.format('name'))\\n reProf2 = re.compile('<{0}>.*</{0}>'.format('name2'))\\n reHex = re.compile('<{0}>.*</{0}>'.format('hex'))\\n reProt = re.compile('<{0}>.*</{0}>'.format('protected'))\\n reKeyM = re.compile('<{0}>.*</{0}>'.format('keyMaterial'))\\n sNewXML = sXML\\n # change the name of the profile\\n sNewXML = reProf.sub('<{0}>{1}</{0}>'.format( 'name2',newProfile), sNewXML, 1) \\n # remove the hex element in <SSID>\\n sNewXML = reHex.sub('', sNewXML, 1)\\n if ssid is not None:\\n # change the name element in <SSID> to new SSID name\\n sNewXML = reProf.sub('<{0}>{1}</{0}>'.format( 'name',ssid), sNewXML, 1) \\n if pass_phrase is not None: \\n # change the protected element from true to false in <sharedKey>\\n sNewXML = reProt.sub('<{0}>{1}</{0}>'.format( 'protected','false'), sNewXML, 1) \\n # change the keyMaterial element to the new pass phrase in <sharedKey>\\n sNewXML = reKeyM.sub('<{0}>{1}</{0}>'.format( 'keyMaterial', pass_phrase), sNewXML, 1)\\n # rename <name2> back to <name>\\n sNewXML = reProf2.sub('<{0}>{1}</{0}>'.format( 'name',newProfile), sNewXML, 1) \\n #print sNewXML\\n if saveNewProfile is not None:\\n open(saveNewProfile,'w').write(sNewXML)\\n # set the new profile\\n self.wlanSetProfile(iface, sNewXML)\",\n \"def save(self, *args, **kwargs):\\r\\n\\r\\n if not self.trackerid:\\r\\n self.trackerid = generate_trackerid()\\r\\n super(Profile, self).save(*args, **kwargs)\",\n \"def save(wn, change_list=None):\\n wordnet_yaml.save(wn, change_list)\\n save_all_xml(wn, change_list)\\n with codecs.open(\\\"wn.xml\\\", \\\"w\\\", \\\"utf-8\\\") as outp:\\n wn.to_xml(outp, True)\\n pickle.dump(wn, open(\\\"wn.pickle\\\", \\\"wb\\\"))\",\n \"def SaveWirelessNetworkProfile(self, id):\\n def write_script_ent(prof, conf, script):\\n if not conf.has_option(prof, script):\\n conf.set(prof, script, None)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wireless_conf)\\n cur_network = self.LastScan[id]\\n bssid_key = cur_network[\\\"bssid\\\"]\\n essid_key = \\\"essid:\\\" + cur_network[\\\"essid\\\"]\\n if config.has_section(bssid_key):\\n config.remove_section(bssid_key)\\n config.add_section(bssid_key)\\n if cur_network[\\\"use_settings_globally\\\"]:\\n if config.has_section(essid_key):\\n config.remove_section(essid_key)\\n config.add_section(essid_key)\\n for x in cur_network:\\n config.set(bssid_key, x, cur_network[x])\\n if cur_network[\\\"use_settings_globally\\\"]:\\n config.set(essid_key, x, cur_network[x])\\n write_script_ent(bssid_key, config, \\\"beforescript\\\")\\n write_script_ent(bssid_key, config, \\\"afterscript\\\")\\n write_script_ent(bssid_key, config, \\\"disconnect\\\")\\n if cur_network[\\\"use_settings_globally\\\"]:\\n write_script_ent(essid_key, config, \\\"beforescript\\\")\\n write_script_ent(essid_key, config, \\\"afterscript\\\")\\n write_script_ent(essid_key, config, \\\"disconnect\\\")\\n config.write(open(self.wireless_conf, \\\"w\\\"))\",\n \"def save(self):\\n self.click(\\\".action-save\\\")\\n self.page.wait_for_ajax()\",\n \"def save(self,filename): \\n with open(filename, 'wb') as f:\\n pickle.dump(self,f)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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db."},"document":{"kind":"string","value":"def main():\n \n option_parser = optparse.OptionParser(usage='usage: %prog [options] filename')\n option_parser.add_option('-d', '--debug',\n dest='debug',\n default=False,\n action='store_true', \n help='Enable debugging messages')\n option_parser.add_option('-n', '--no-store', \n dest='store', \n default=True, \n action='store_false', \n help='Test but do not store the data') \n option_parser.add_option('-v', '--verbose', \n dest='verbose', \n default=False, \n action='store_true', \n help='Produce verbose output') \n (options, args) = option_parser.parse_args()\n \n if not args:\n raise RuntimeError('Please specify at least one filename to load.')\n\n if options.debug:\n level = logging.DEBUG\n else:\n level = logging.INFO\n logging.basicConfig(level=level,\n format='%(levelname)-8s %(message)s',\n )\n\n # Establish our django set-up so we can import models\n # to work with the database.\n import settings\n setup_environ(settings)\n\n # Now we can import the other modules we need that depend on django\n from vtr_loader import load_data_from_stream, ErrorKeeper\n \n errors = ErrorKeeper()\n num_new = 0\n num_existing = 0\n for filename in args:\n logging.info('Processing %s', filename)\n f = open(filename, 'rt')\n try:\n counts = load_data_from_stream(\n f, \n store=options.store, \n verbose=options.verbose, \n message_stream=sys.stdout, \n errorcb=errors,\n )\n finally:\n f.close()\n \n num_existing += counts[0]\n num_new += counts[1]\n\n for error in errors.errors:\n error['file'] = filename\n\n if options.verbose:\n print\n print 'Found %d new records' % num_new\n print 'Found %d existing records' % num_existing\n print 'Found %d errors' % len(errors.errors)\n\n for error in errors.errors:\n print\n error['file'] = filename\n print 'ERROR in %(file)s: %(start)d - %(end)d (%(error)s)' % error\n for line in error['lines']:\n print '\\t%s' % line\n \n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main():\n\n\tgdl = TwitterDataLoader()\t\n\tgdl.load_twitter_data_to_db(truncate_table=False, skip_loaded_files=True)","def populate_db_command():\n print(\"Populating DB with sample data.\")\n populate_db()\n print \"Done\"","def main():\r\n\r\n # delete the database file if it already exists\r\n db_path = Path('../../data/db.sqlite')\r\n db_path.unlink(missing_ok=True)\r\n\r\n # create the database\r\n with sqlite3.connect(db_path) as connection:\r\n create_database(connection)","def main():\n\n conn = psycopg2.connect(\n \"host=127.0.0.1 dbname=sparkifydb user=student password=student\")\n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n\n conn.close()","def main():\n\n database_file = Path(__file__).parent.parent / \"src\" / \"galois\" / \"_databases\" / \"irreducible_polys.db\"\n conn, cursor = create_database(database_file)\n\n _add_hpl_1998(conn, cursor)\n\n conn.close()","def main():\r\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb2 user=postgres password=261998\")\r\n cur = conn.cursor()\r\n\r\n process_data(cur, conn, filepath='C:/Users/AG/Downloads/Data-engineering-nanodegree-master/Data-engineering-nanodegree-master/1_dend_data_modeling/P1_Postgres_Data_Modeling_and_ETL/data/song_data', func=process_song_file)\r\n process_data(cur, conn, filepath='C:/Users/AG/Downloads/Data-engineering-nanodegree-master/Data-engineering-nanodegree-master/1_dend_data_modeling/P1_Postgres_Data_Modeling_and_ETL/data/log_data', func=process_log_file)\r\n\r\n conn.close()","def main():\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=hallo user=hallo password=hallo\")\n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n\n conn.close()","def main():\n database = dbdir + \"pyflangun.db\"\n sql_create_airports_table = \"\"\"CREATE TABLE IF NOT EXISTS airports (\n id integer PRIMARY KEY,\n name text NOT NULL,\n icao text\n );\"\"\"\n sql_create_weather_table = \"\"\"CREATE TABLE IF NOT EXISTS weather (\n id integer PRIMARY KEY,\n icao text NOT NULL,\n temp_f integer NOT NULL,\n dew_pt_f integer NOT NULL,\n status integer NOT NULL,\n FOREIGN KEY (icao) REFERENCES airports (icao)\n );\"\"\"\n # create a database connection\n conn = create_connection(database)\n # create tables\n if conn is not None:\n create_table(conn, sql_create_airports_table)\n create_table(conn, sql_create_weather_table)\n else:\n print(\"Error! cannot create the database connection.\")","def main():\n conn = psycopg2.connect('host=127.0.0.1 dbname=sparkifydb user=student password=student')\n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', function=process_song_file)\n process_data(cur, conn, filepath='data/log_data', function=process_log_file)\n\n conn.close()","def main():\n if len(sys.argv) == 4:\n\n messages_path, categories_path, database_path = sys.argv[1:]\n\n print('Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}'\n .format(messages_path, categories_path))\n df = load_data(messages_path, categories_path)\n\n print('Cleaning data...')\n df = clean_data(df)\n\n print('Saving data...\\n DATABASE: {}'.format(database_path))\n save_data(df, database_path)\n\n print('Cleaned data saved to database!')\n\n else:\n print('Please provide the filepaths of the messages and categories '\n 'datasets as the first and second argument respectively, as '\n 'well as the filepath of the database to save the cleaned data '\n 'to as the third argument. \\n\\nExample: python process_data.py '\n 'disaster_messages.csv disaster_categories.csv '\n 'DisasterResponse.db')","def importData():\n #importChallengeDataToDB()\n importTrendingDataToDB()","def main() -> None:\n if len(sys.argv) == 4:\n\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n print(\n \"Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}\".format(\n messages_filepath, categories_filepath\n )\n )\n df = load_data(messages_filepath, categories_filepath)\n\n print(\"Cleaning data...\")\n df = clean_data(df)\n\n print(\"Saving data...\\n DATABASE: {}\".format(database_filepath))\n save_data(df, database_filepath)\n\n print(\"Cleaned data saved to database!\")\n\n else:\n print(\n \"Please provide the filepaths of the messages and categories \"\n \"datasets as the first and second argument respectively, as \"\n \"well as the filepath of the database to save the cleaned data \"\n \"to as the third argument. \\n\\nExample: python process_data.py \"\n \"disaster_messages.csv disaster_categories.csv \"\n \"DisasterResponse.db\"\n )","def main():\n langs = []\n\n with open(\"sql/07_populate.sql\", 'w', encoding='utf8') as sql:\n sql.write(\"--this file is generated from csv files in data folder\\n\\n\")\n\n langs = write_lang_city(sql)\n write_groups_diets(sql, langs)\n\n with open(\"sql/10_populate_test_data.sql\", 'w', encoding='utf8') as sql:\n sql.write(\"--this file is generated from csv files in moc_data folder\\n\\n\")\n write_test_data(sql)\n # This command sets postgis coordinates based on latitude and longitude\n sql.write(\"UPDATE restaurant SET geo_location = ST_POINT(latitude, longitude);\\n\")\n sql.close()","def populate_db():\n stdout.write('Emptying the tables...\\n')\n empty_tables()\n stdout.write('Populating Language records...\\n')\n populate_language()\n stdout.write('Populating Lemma, Wordform, and Definition records...\\n')\n populate_lexical()\n stdout.write('Populating ProperName records...\\n')\n populate_proper_names()","def main():\n\n if len(sys.argv) == 4:\n\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n print('Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}'\n .format(messages_filepath, categories_filepath))\n df = load_data(messages_filepath, categories_filepath)\n\n print('Cleaning data...')\n df = clean_data(df)\n \n print('Saving data...\\n DATABASE: {}'.format(database_filepath))\n save_data(df, database_filepath)\n \n print('Cleaned data saved to database!')\n \n else:\n print('Please provide the filepaths of the messages and categories '\\\n 'datasets as the first and second argument respectively, as '\\\n 'well as the filepath of the database to save the cleaned data '\\\n 'to as the third argument. \\n\\nExample: python process_data.py '\\\n 'disaster_messages.csv disaster_categories.csv '\\\n 'DisasterResponse.db')","def main():\n # Create a connection to database\n try:\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb user=student password=student\")\n except psycopg2.Error as e:\n print(\"Error: Unable to make connection to Postgres Database {}\".format(\"sparkifydb\"))\n print(e)\n \n # Get cursor\n try:\n cur = conn.cursor()\n except psycopg2.Error as e:\n print(\"Error: Unable to get cursor to the Database\")\n print(e)\n\n # set the autocommit to true to avoid conn.commit() after each command\n conn.set_session(autocommit=True)\n\n # use the process_data function to get all log and song data files contents inserted in tables\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n\n # Close the connection to the database\n conn.close()","def main():\n if len(sys.argv) == 4:\n\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n print('Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}'\n .format(messages_filepath, categories_filepath))\n df = load_data(messages_filepath, categories_filepath)\n\n print('Cleaning data...')\n df = clean_data(df)\n \n print('Saving data...\\n DATABASE: {}'.format(database_filepath))\n save_data(df, database_filepath)\n \n print('Cleaned data saved to database!')\n \n else:\n print('Please provide the filepaths of the messages and categories '\\\n 'datasets as the first and second argument respectively, as '\\\n 'well as the filepath of the database to save the cleaned data '\\\n 'to as the third argument. \\n\\nExample: python process_data.py '\\\n 'disaster_messages.csv disaster_categories.csv '\\\n 'DisasterResponse.db')","def main():\n \n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb user=student password=student\")\n \n cur = conn.cursor()\n process_data(cur, conn, filepath='data/song_data',\n func=process_song_file) \n \n process_data(cur, conn, filepath='data/log_data',\n func=process_log_file)\n \n conn.close()","def main():\n db = _db.Database(experiment.ORACLE_PATH)\n db.populate_kernel_names_table()\n db.commit()","def main():\n \n if len(sys.argv) == 4:\n\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n print('Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}'\n .format(messages_filepath, categories_filepath))\n df = load_data(messages_filepath, categories_filepath)\n\n print('Cleaning data...')\n df = clean_data(df)\n \n print('Saving data...\\n DATABASE: {}'.format(database_filepath))\n save_data(df, database_filepath)\n \n print('Cleaned data saved to database!')\n \n else:\n print('Please provide the filepaths of the messages and categories '\\\n 'datasets as the first and second argument respectively, as '\\\n 'well as the filepath of the database to save the cleaned data '\\\n 'to as the third argument. \\n\\nExample: python process_data.py '\\\n 'disaster_messages.csv disaster_categories.csv '\\\n 'DisasterResponse.db')","def main():\r\n if len(sys.argv)==4:\r\n\r\n # files path\r\n m_file_path,c_file_path,database = sys.argv[1:]\r\n\r\n # first, read the data\r\n print('Reading the data...')\r\n df = read_data(m_file_path,c_file_path)\r\n print('OK!')\r\n print(' ')\r\n \r\n # clean it\r\n print('Cleaning the data...')\r\n df = clean_data(df)\r\n print('OK!')\r\n print(' ')\r\n \r\n # save it\r\n print('Saving data...')\r\n save_data(df,database)\r\n print(' ')\r\n \r\n # when it's done\r\n print(f'Cleaned data is stored in {database[:-3]} database') \r\n\r\n else:\r\n print('Please provide the filepaths of the messages and categories '\\\r\n 'datasets as the first and second argument respectively, as '\\\r\n 'well as the filepath of the database to save the cleaned data '\\\r\n 'to as the third argument. \\n\\nExample: python process_data.py '\\\r\n 'disaster_messages.csv disaster_categories.csv '\\\r\n 'DisasterResponse.db')","def main():\n conn = psycopg2.connect(f\"host=127.0.0.1 dbname=sparkifydb user={username} password={password}\")\n cur = conn.cursor()\n conn.set_session(autocommit=True)\n\n artists_data, songs_data = process_song_file()\n songplays_help_df, time_data, users_data = process_log_file()\n songplays_data = process_songplays_data(artists_data, songs_data, songplays_help_df)\n\n data_list = [songplays_data, users_data, songs_data, artists_data, time_data]\n for idx, (data, query) in enumerate(zip(data_list, insert_table_queries), start=1):\n print(f\"inserting file {idx}/{len(data_list)}\")\n for row in data:\n try:\n cur.execute(query, row)\n except psycopg2.Error as error:\n print(f\"Psychog2 error @ file {idx} row {row}: {error} NOTE: this file will not be inserted.\")\n\n conn.close()","def main():\n db.connect()\n db.execute_sql('PRAGMA foreign_keys = ON;')\n db.create_tables([\n Job,\n Person\n ])\n\n people = [\n ('Andrew', 'Sumner', 'Andy'),\n ('Peter', 'Seattle', None),\n ('Susan', 'Boston', 'Beannie'),\n ('Steven', 'Colchester', None),\n ('Peter', 'Seattle', None),\n ]\n\n for person in people:\n try:\n with db.transaction():\n new_person = Person.create(\n person_name = person[0],\n lives_in_town = person[1],\n nickname = person[2],)\n new_person.save()\n\n except Exception as e:\n logger.info(f'Error creating person = {person[0]}')\n logger.info(e)\n logger.info('See how the datbase protects our data')\n\n for person in Person:\n person.show()\n\n jobs = [\n ('Analyst', '2017-02-01', '2019-07-31', 34.999, 'Andrew'),\n ('Developer', '2017-02-01', '2019-07-31', 34.999, 'Fred'),\n ]\n\n for job in jobs:\n try:\n with db.transaction():\n new_job = Job.create(\n job_name = job[0],\n start_date = job[1],\n end_date = job[2],\n salary = job[3],\n person_employed = job[4],\n )\n new_job.save()\n\n except Exception as e:\n logger.info(f'Error creating job = {job[0]}')\n logger.info(e)\n logger.info('See how the datbase protects data across tables')\n\n logger.info(\"don't forget - but can you find a better way?\")\n db.close()","def import_db(import_file):\n import_data(import_file)","def connect_db_and_load_data(cls):\n db.connect()\n db.create_tables([Product], safe=True)\n load_data(transform_data('./inventory.csv'))","def main():\n\t\n\ttry:\n\t conn = mysql.connector.connect(user = 'root', password = 'love',\n\t host = '127.0.0.1',\n\t database = 'promap')\n\texcept mysql.connector.Error as err:\n\t if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:\n\t print(\"password and name pair incorrect\")\n\t elif err.errno == errorcode.ER_BAD_DB_ERROR:\n\t print(\"no such database\")\n\t else:\n\t print(err)\n\t exit(1)\n\n\t# obtain connection cursor\n\tcursor = conn.cursor()\n\ti = 0\n\t# walk through all the files\n\trootDirectory = 'C:/Users/Zhou/Downloads/ProMap_Data/articles.O-Z'\n\tfor aDir, theSubdirs, theFiles in os.walk(rootDirectory):\n\t\t\n\t\tfor aFile in theFiles:\n\t\t\tfilename = os.path.join(aDir, aFile)\n\t\t\tprint(aFile)\n\t\t\tdata = NXMLParser.parse(filename)\n\t\t\tif (data[0] == -1): \n\t\t\t\ti = i + 1\n\t\t\t\tprint(i)\n\t\t\t\tcontinue\n\t\t\tDatabaseUpdater.update(cursor, data)\n\t\t\tconn.commit()\n\n\tcursor.close()\n\tconn.close()","def load_DB(self):\n\t\tprint 'Loadind Data Base...'\n\t\tstream = open(self.DB_file)\n\t\tself.DB = cPickle.load(stream)\n\t\tstream.close()\n\t\tprint \"Number of documents in the Data Base: \", self.DB.nb_doc_total\n\t\tprint 'Loading completed'\n\t\treturn","def main():\n program = None\n try:\n program = DataUploader()\n program.clear_db()\n program.upload_data()\n except Exception as e:\n traceback.print_exc()\n print(\"ERROR: Failed to use database:\", e)\n finally:\n if program:\n program.connection.close_connection()","def main():\n cur, conn = connect('dwh.cfg')\n \n set_schema = schema_queries[1]\n cur.execute(set_schema)\n \n print('Loading Staging Tables.')\n load_staging_tables(cur, conn)\n \n print('Inserting Rows.')\n insert_tables(cur, conn)\n\n \n conn.close()","def load_file():\n global list_of_table, data_base, new_data\n open_name = askopenfilename()\n\n if Path(open_name).suffix == '.db':\n data_base = open_name\n data_base = str(data_base)\n new_data_base = parse(data_base)\n new_data = update_list_tables(new_data_base)\n new_data.clear()\n\n else:\n mistake_db_file()","def main():\n\n populate('monsters')\n populate('spells')\n populate('conditions')\n populate('encounters')\n populate('characters')\n populate('actions')","def os_start_db( self, ):\r\n pass","def main():\r\n db = connect_database()\r\n with db:\r\n if sys.argv[1] == \"-s\":\r\n select_all(db, sys.argv[2])\r\n elif sys.argv[1] == \"-i\":\r\n cus_data = []\r\n for i in range(2, len(sys.argv)):\r\n cus_data.append(sys.argv[i])\r\n insert_customer(db, cus_data)\r\n elif sys.argv[1] == \"-c\":\r\n create_tables()\r\n elif sys.argv[1] == \"-pw\":\r\n pop_waiting(db, sys.argv[2])\r\n elif sys.argv[1] == \"-ph\":\r\n pop_help(db, sys.argv[2])\r\n elif sys.argv[1] == \"-r\":\r\n refresh_tables(db)\r\n elif sys.argv[1] == \"-e\":\r\n export_helped_table(db)\r\n else:\r\n print errorArgument\r\n db.close()","def main():\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb user=student password=student\")\n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n print('song file processing is complete')\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n print('log file processing is complete')\n conn.close()","def build_input_db():\n build_input_database()","def load_data():\n\tscores = pd.read_csv('../data/user_assessment_scores.csv')\n\tviews = pd.read_csv('../data/user_course_views.csv')\n\ttags = pd.read_csv('../data/course_tags.csv')\n\tinterests = pd.read_csv('../data/user_interests.csv')\n\n\tdb_file = '../db/usersim.sqlite'\n\ttry:\n\t\tengine = sqlite3.connect(db_file, timeout=10)\n\t\tscores.to_sql('scores', engine, if_exists='replace', index=False, index_label='user_handle')\n\t\tviews.to_sql('views', engine, if_exists='replace', index=False, index_label='user_handle')\n\t\ttags.to_sql('tags', engine, if_exists='replace', index=False, index_label='course_id')\n\t\tinterests.to_sql('interests', engine, if_exists='replace', index=False, index_label='user_handle')\n\texcept:\n\t\tprint('Error occured while inserting into database')\n\tfinally:\n\t\tif engine:\n\t\t\tengine.close()\n\treturn scores, views, tags, interests","def main() :\n\n global dbMan\n dbMan = DBManager( 'CMS_HCL_APPUSER_R/HCAL_Reader_55@localhost:1521/cms_omds_lb.cern.ch' )","def main(csvfile, dbfile, verbose=False):\n CONN = sqlite3.connect(dbfile)\n cursor = CONN.cursor()\n create_schema(cursor)\n process_data(cursor, csvfile, verbose=verbose)\n CONN.commit()\n CONN.close()","def main() -> None:\n\n # TODO - Gather this from whatever mechanism is needed\n data = {\n \"family_members\": \"\",\n \"social_status\": \"\",\n \"gender\": \"\",\n \"date_birth\": \"\",\n \"id_number\": 0,\n \"mail\": \"\",\n \"name\": \"\",\n \"id\": \"\",\n }\n\n # Grab an active cursor object\n cursor = db_connection.cursor()\n\n # We know the DB is the part that can break, so we'll wrap this first\n try:\n update(cursor, data)\n print(\"Yay this worked\") # Check the DB and give some user feedback\n except pymysql.DatabaseError as e:\n print(str(e)) # Prints the basic error you encountered\n # If you hit this section, get rid of the try/except or use pdb to walk the error\n # There are multiple ways to print the traceback\n # IMHO the easiest is to just not catch the exception\n\n # This should only really be terminated when the application is done\n db_connection.close()","def main(passed_arguments):\n\n # use real data as default\n scripts_path = os.path.abspath(os.path.join(PYTHON_PATH, 'scripts'))\n meta_path = os.path.abspath(os.path.join(scripts_path, 'meta.json'))\n manifest_path = os.path.abspath(os.path.join(scripts_path, 'manifest.csv'))\n\n # Locally, we can optionally have sample data\n if passed_arguments.sample and passed_arguments.database != 'remote':\n meta_path = os.path.abspath(os.path.join(scripts_path,\n 'meta_sample.json'))\n manifest_path = os.path.abspath(\n os.path.join(scripts_path, 'manifest_sample.csv'))\n\n # for case of more than one database choice default to the option with\n # the lowest risk if database is updated\n if passed_arguments.database == 'docker':\n database_choice = 'docker_database'\n drop_tables = True\n\n elif passed_arguments.database == 'docker_local':\n database_choice = 'docker_with_local_python'\n drop_tables = True\n\n elif passed_arguments.database == 'remote':\n database_choice = 'remote_database'\n drop_tables = False #TODO this is a hacky way to avoid dropping tables because it's not working with RDS...\n\n # Only users with additional admin privileges can rebuild the\n # remote database\n if not passed_arguments.update_only:\n database_choice = 'remote_database_master'\n\n # TODO: do we want to default to local or docker?\n elif passed_arguments.database == 'local':\n database_choice = 'local_database'\n drop_tables = True\n\n # universal defaults\n keep_temp_files = True\n\n # Instantiate and run the loader\n loader = LoadData(database_choice=database_choice, meta_path=meta_path,\n manifest_path=manifest_path,\n keep_temp_files=keep_temp_files,\n drop_tables=drop_tables)\n\n if passed_arguments.update_only:\n loader.update_database(passed_arguments.update_only)\n else:\n loader.rebuild()\n\n\n\n #TODO add in failures report here e.g. _failed_table_count","def main():\n args = sys.argv[1:]\n if len(args) != 2:\n print('Usage: sqlite.py db SQL', file=sys.stderr)\n return\n\n db, sql = args\n\n print('Opening {}'.format(args[0]))\n conn = connect(db)\n\n for q in sql.split(';'):\n print('Executing {}'.format(q))\n with conn:\n conn.execute(q)","def dbinit( *args, **kwargs ):","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n \n load_staging_tables(cur, conn)\n insert_tables(cur, conn)\n\n conn.close()","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n \n load_staging_tables(cur, conn)\n insert_tables(cur, conn)\n\n conn.close()","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(config['CLUSTER']['HOST'], config['CLUSTER']['DB_NAME'], config['CLUSTER']['DB_USER'], config['CLUSTER']['DB_PASSWORD'], config['CLUSTER']['DB_PORT']))\n cur = conn.cursor()\n \n load_staging_tables(cur, conn)\n insert_tables(cur, conn)\n\n conn.close()","def load_movies():\n print \"Movies\"\n\n # Delete all rows in table, so if we need to run this a second time,\n # we won't be trying to add duplicate users\n Movie.query.delete()\n\n # Read u.item file and insert data\n for row in open(\"seed_data/u.item\"):\n row =row.rstrip()\n\n movie_id, title_long, released_string, imdb_url = row.split(\"|\")[:4]\n #we modified the datetime format changed released_string into \n #new format by using datetim.strptime to convert it. \n print row\n if released_string: \n release_at = datetime.strptime(released_string, \"%d-%b-%Y\")\n else: \n release_at = None \n\n #here we stripped the title of the (xxxx) year and parenthesis\n #using the slice method. \n title = title_long[:-7]\n\n print movie_id, title_long, released_string, imdb_url\n\n #assign the return values from our for loop to a new variable\n movie = Movie(movie_id=movie_id, title=title, released_at=release_at,\n imdb_url=imdb_url)\n \n # We need to add to the session or it won't ever be stored\n db.session.add(movie)\n\n #Once we're done, we should commit our work\n db.session.commit()","def main():\n ensure_not_root()\n config.setup()\n model.init_db()\n manager.run()","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} \\\n port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n \n load_staging_tables(cur, conn)\n insert_tables(cur, conn)\n\n conn.close()","def run_db(args):\n # print(\"running chronqc_db\")\n chronqc_db.main(args)","def main():\r\n print(\"Initializing Database\")\r\n global conn\r\n conn = create_db()\r\n\r\n print(\"Inserting Raw Ticket Data\")\r\n create_raw_data()\r\n print(\"Creating Block Limits DataFrame\")\r\n\r\n global streetnamedict\r\n global streetintersections\r\n streetintersections, streetnamedict = create_block_limits()\r\n\r\n print(\"Starting Address Creation\")\r\n global single_address\r\n global double_address\r\n global addresses\r\n single_address, double_address, addresses = create_address_data()\r\n print(\"Finished Creating addresses\")\r\n\r\n global nhoodtype\r\n nhoodtype = pd.read_sql_query('Select nhood, violationdesc, count(*) tickets from raw_ticket_data t1 join single_address t2 '\r\n ' on t1.TickStreetNo = t2.number and t1.TickStreetName = t2.streetname group by nhood, violationdesc', conn)\r\n\r\n print(\"Processing Ticket Data\")\r\n process_ticket_data()\r\n print('Finished processing Ticket Data!')\r\n print('Starting Street Data Creation')\r\n streetsweeping, streetvolume = process_volume()\r\n print(' Finished Street Data Creation')\r\n\r\n print('Pairing Addresses with Street Data')\r\n pair_address(streetsweeping, streetvolume)\r\n print('Finished Pairing Addresses')\r\n\r\n print('Pairing Parking')\r\n pair_parking(streetvolume)\r\n print('Finished Pairing Parking')\r\n\r\n conn.close()\r\n\r\n print('Finished Creating Entire Database and updated Shapefiles at {}'.format( dt.datetime.now().strftime(\"%H:%M\")))","def read_db(self):\n with open(self.filename, 'r') as database:\n data = json.load(database)\n self.data = data","def structure_and_repopulate_db() -> None:\n with open('db.sql', encoding=\"utf-8\") as f:\n commands = f.read().strip().split(';')\n commands = [command.strip() for command in commands]\n for command in commands:\n my_cursor.execute(command)\n my_db.commit()\n print('Source structure created, data repopulated')","def main():\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n get_user_config()","def main():\n db = sqlite3.connect(DATABASE_NAME)\n create_tables(db)\n\n # Write your code below","def main():\n # Create the flow\n with Flow('pickle flow') as flow:\n db_table = create_table()\n weather_data = get_weather(LAT_NYC, LON_NYC, 2018)\n parsed_data = parse_weather(weather_data)\n populated_table = store_weather(parsed_data)\n populated_table.set_upstream(db_table)\n\n # Run the flow\n flow.run()","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n print('config file read! - now connecting...')\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n\n print('connection established! - now dropping tables...')\n drop_tables(cur, conn)\n\n print('tables dropped! - now creating tables...')\n create_tables(cur, conn)\n\n print('tables are now created! - closing connection...')\n conn.close()\n\n print('done!')","def main():\n \n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n \n load_staging_tables(cur, conn)\n insert_tables(cur, conn)\n\n conn.close()","def initdb():\n\tc, conn = connect()\n\tsql = []\n\twith open('data\\\\database.sql') as f:\n\t\tfor line in f:\n\t\t\tsql.append(line.strip())\n\t\n\tfor query in sql:\n\t\tc.execute(query)\n\tconn.commit()","def load():\n pr = PLAPIResource()\n pr.name = 'Programming Languages'\n pr.uri = 'programming-languages'\n db.session.add(pr)\n db.session.commit()\n\n pd = PLAPIResource()\n pd.name = 'Paradigms'\n pd.uri = 'paradigms'\n db.session.add(pd)\n db.session.commit()\n\n \"\"\"\n pt = PLAPIResource()\n pt.name = 'Tutorials'\n pt.uri = 'tutorials'\n db.session.add(pt)\n db.session.commit()\n \"\"\"\n\n\n pl = ProgrammingLanguageModel()\n pl.name = 'Python'\n pl.slug = 'python'\n pl.homepage_url = 'https://www.python.org/'\n pl.is_visible = True\n db.session.add(pl)\n db.session.commit()\n\n pl2 = ProgrammingLanguageModel()\n pl2.name = 'Swift'\n pl2.slug = 'swift'\n pl2.homepage_url = 'https://developer.apple.com/swift/'\n pl2.is_visible = True\n db.session.add(pl2)\n db.session.commit()\n\n pl3 = ProgrammingLanguageModel()\n pl3.name = 'Ruby'\n pl3.slug = 'ruby'\n pl3.homepage_url = 'https://www.ruby-lang.org/'\n pl3.is_visible = True\n db.session.add(pl3)\n db.session.commit()\n\n prdgm1 = ParadigmModel()\n prdgm1.name = 'Imperative'\n prdgm1.slug = 'imperative'\n prdgm1.is_visible = True\n db.session.add(prdgm1)\n db.session.commit()\n\n prdgm2 = ParadigmModel()\n prdgm2.name = 'Functional'\n prdgm2.slug = 'functional'\n prdgm2.is_visible = True\n db.session.add(prdgm2)\n db.session.commit()\n\n lib = LibraryModel()\n lib.name = 'Django'\n lib.slug = 'django'\n lib.is_visible = True\n lib.language = pl.id\n lib.homepage_url = 'https://www.djangoproject.com/'\n lib.source_code_url = 'https://github.com/django/django'\n db.session.add(lib)\n db.session.commit()\n\n lib2 = LibraryModel()\n lib2.name = 'Flask'\n lib2.slug = 'flask'\n lib2.is_visible = True\n lib2.language = pl.id\n lib2.homepage_url = 'http://flask.pocoo.org/'\n lib2.source_code_url = 'https://github.com/mitsuhiko/flask'\n db.session.add(lib)\n db.session.commit()\n\n tut = TutorialModel()\n tut.name = 'Choose Your Own Adventure Presentations'\n tut.slug = 'choose-your-own-adventure-presentations'\n tut.is_visible = True\n tut.language = pl.id\n tut.tutorial_url = 'https://www.twilio.com/blog/2014/11/choose-your-own-adventure-presentations-with-reveal-js-python-and-websockets.html'\n db.session.add(tut)\n db.session.commit()\n\n tut2 = TutorialModel()\n tut2.name = 'Full Stack Python'\n tut2.slug = 'full-stack-python'\n tut2.is_visible = True\n tut2.language = pl.id\n tut2.tutorial_url = 'http://www.fullstackpython.com/'\n db.session.add(tut2)\n db.session.commit()","def populate_db():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n\n logger.info('Working with Person class')\n logger.info('Note how I use constants and a list of tuples as a simple schema')\n logger.info('Normally you probably will have prompted for this from a user')\n\n PERSON_NAME = 0\n LIVES_IN_TOWN = 1\n NICKNAME = 2\n\n people = [\n ('Andrew', 'Sumner', 'Andy'),\n ('Peter', 'Seattle', None),\n ('Susan', 'Boston', 'Beannie'),\n ('Pam', 'Coventry', 'PJ'),\n ('Steven', 'Colchester', None),\n ]\n\n logger.info('Creating Person records: iterate through the list of tuples')\n logger.info('Prepare to explain any errors with exceptions')\n logger.info('and the transaction tells the database to fail on error')\n\n logger.info('Working with Job class')\n logger.info('Creating Job records. We use the foreign key')\n\n JOB_NAME = 0\n START_DATE = 1\n END_DATE = 2\n SALARY = 3\n PERSON_EMPLOYED = 4\n\n jobs = [\n ('Analyst', '2001-09-22', '2003-01-30',65500, 'Andrew'),\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew'),\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000, 'Andrew'),\n ('Admin supervisor', '2012-10-01', '2014-11,10', 45900, 'Peter'),\n ('Admin manager', '2014-11-14', '2018-01,05', 45900, 'Peter')\n ]\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n try:\n for person in people:\n with database.transaction():\n new_person = Person.create(\n person_name = person[PERSON_NAME],\n lives_in_town = person[LIVES_IN_TOWN],\n nickname = person[NICKNAME])\n new_person.save()\n logger.info('Database add successful')\n\n logger.info('Print the Person records we saved...')\n for saved_person in Person:\n logger.info(f'{saved_person.person_name} lives in {saved_person.lives_in_town} and likes to be known as {saved_person.nickname}')\n\n except Exception as e:\n logger.info(f'Error creating = {person[PERSON_NAME]}')\n logger.info(e)\n logger.info('See how the database protects our data')\n try:\n for job in jobs:\n with database.transaction():\n new_job = Job.create(\n job_name = job[JOB_NAME],\n start_date = job[START_DATE],\n end_date = job[END_DATE],\n salary = job[SALARY],\n person_employed = job[PERSON_EMPLOYED])\n new_job.save()\n\n logger.info('Reading and print all Job rows (note the value of person)...')\n for job in Job:\n logger.info(f'{job.job_name} : {job.start_date} to {job.end_date} for {job.person_employed}')\n\n except Exception as e:\n logger.info(f'Error creating = {job[JOB_NAME]}')\n logger.info(e)\n\n finally:\n logger.info('database closes')\n database.close()","def do_command(self, args):\n chk_arg_count(args, 0)\n dbops.init_database()","def main():\n cur, conn = create_database()\n \n drop_tables(cur, conn)\n create_tables(cur, conn)\n\n conn.close()","def load_data(self):","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['STAGE'].values()))\n cur = conn.cursor()\n \n #remove the existing tables\n drop_tables(cur, conn)\n \n #replace the tables with new ones\n create_tables(cur, conn)\n \n #add missing postcode value into table\n default_missing_values(cur, conn)\n \n conn.close()","def example_data():\n\n # In case this is run more than once, empty out existing data\n User.query.delete()\n Location.query.delete()\n\n # Add sample users and locations\n\n trinity = User(fname='Trinity', email='trin@unplugged.com',\n username='questionit', password='l0lagent')\n neo = User(fname='Neo', email='theone@unplugged.com',\n username='neo', password='l0lagent')\n\n tacorea = Location(yelp_id='tacorea-san-francisco', name='Tacorea',\n latitude='37.7749', longitude='122.3392',\n address='809 Bush St, San Francisco, CA 94108',\n yelp_url='tacorea@yelp.com', pic='pic')\n\n db.session.add_all([trinity, neo, tacorea])\n db.session.commit()","def init_db():\n # with current_app.open_resource(\"schema.sql\") as f:\n # db.executescript(f.read().decode(\"utf8\"))\n print(\"初始化数据库脚本文件！！！\")","def load(file_path = \"database.pkl\"):\n while True:\n doLoad = input(\"Database contents will be overwritten. Proceed? (y/n): \")\n if doLoad == 'y':\n break\n elif doLoad == 'n':\n return\n else:\n continue\n try:\n with open(file_path, 'rb') as f:\n global person_database\n person_database = pickle.load(f)\n print(\"Database loaded.\")\n except:\n print(\"Database not available.\")","def populate_db():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('../data/personjob.db') # navigate relative path to the db\n\n logger.info('Working with Department class')\n logger.info('Creating department records')\n\n department_number = 0\n department_name = 1\n department_manager = 2\n\n departments = [\n ('A111', 'Asset Management', 'Dave Sanders'),\n ('B222', 'Human Resources', 'Tammy Murray'),\n ('C333', 'Payroll', 'Daddy Warbucks'),\n ]\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for department in departments:\n with database.transaction():\n new_department = Department.create(\n department_number = department[department_number],\n department_name = department[department_name],\n deptartment_manager = department[department_manager]\n )\n new_department.save()\n logger.info('Department has been added to the database')\n\n logger.info('Reading and print all department data...')\n for saved_department in Department:\n logger.info(f'{saved_department.department_name} ' + \\\n f'Manager: {saved_department.department_manager}. ' + \\\n f'Department number: {saved_department.department_number}')\n\n except Exception as e:\n logger.info(f'Error creating = {department[department_number]}')\n logger.info(e)\n\n finally:\n logger.info('database closes')\n database.close()","def main():\n cur, conn = create_database()\n \n drop_tables(cur, conn)\n create_tables(cur, conn)\n\n cur.close();\n conn.close()","def read_database(db_path, db_file, *args):\n\n db_filepath = os.path.join(db_path, db_file)\n\n # list to store loaded data\n data_imported = []\n conn = sqlite3.connect(db_filepath)\n\n for data_name in args:\n\n\n info = f'Reading {data_name} from database................'\n print(info, end=\"\")\n data_name_in_db = conn.execute(\n f\"\"\"SELECT name FROM sqlite_master WHERE type='table' \n AND name='{data_name}'; \"\"\").fetchall()\n if data_name_in_db:\n df = pd.read_sql(f\"select * from {data_name}\", con=conn)\n substitute_names(df)\n # revert single column DataFrame to Series\n if 'index' in df.columns:\n df.set_index('index', inplace=True)\n df = df.squeeze('columns')\n data_imported.append(df)\n print('ok')\n else:\n data_imported.append(None)\n print('no data')\n conn.close()\n return data_imported #if len(data_imported)>1 else data_imported[0]","def load_data(db_handler):\n\n from random import seed\n from random import random\n \n seed(1)\n\n new_notes = []\n\n for i in range(1,10):\n\n new_notes.append({\n\n\n 'title': str(i) + str(random()),\n 'content': 'Lorem ipsum' + str(i),\n 'active': True,\n 'created_by':\"Cristhian\" + str(i),\n 'created_at': date.today(),\n 'edited_at':date.today(),\n \n })\n\n new_notes.append(\n {\n \"active\": False,\n \"content\": \"Jesenia\",\n \"edited_at\": \"2019-10-24\",\n \"title\": \"Jesenia La chica de al lado\",\n \"created_by\": \"Cristhian1\",\n \"created_at\": \"2019-10-24\"\n })\n\n new_notes.append(\n {\n \"active\": False,\n \"title\": \"La vida de los numeros\",\n \"content\": \"Lorem ipsum y los numeros de la muerte\",\n \"edited_at\": \"2019-10-25\",\n \"created_by\": \"Jesenia\",\n \"created_at\": \"2019-10-24\"\n })\n\n Note.insert_many(new_notes).execute()\n\n User(name=\"Cristhian\", email=\"test@gmail.com\",\n password=b'$2b$12$U/QjtHt/j0xRT4r8Hx3fOe93EssM6M0iiUaQJOrTd64RXbxvhw6Ii').save()","def gtfsdb_main(ctx, database):\n ctx.obj = dict()\n if not database and os.path.exists(DEFAULT_CONFIG_FILE):\n conf = json.load(open(DEFAULT_CONFIG_FILE, 'r'))\n database = conf['database']\n ctx.obj.update(dict(conf=conf))\n else:\n click.echo(\"No database selected!!\")\n sys.exit(1)\n ctx.obj.update(dict(database=Database(url=database), db_url=database))","def load_data(self):\n if self.debug:\n print(\"Loading data\")","def example_data():\n\n # In case this is run more than once, empty out existing data\n User.query.delete()\n Trip.query.delete()\n Entry.query.delete()\n Category.query.delete()\n Share.query.delete()\n\n # Add sample data\n user1 = User(email='user1@gmail.com', password=bcrypt.hashpw('user1'.encode('utf8'), bcrypt.gensalt(9)), name='One')\n user2 = User(email='user2@gmail.com', password=bcrypt.hashpw('user2'.encode('utf8'), bcrypt.gensalt(9)), name='Two')\n trip1 = Trip(location='Spain', date='08/09/2017', name='Abroad Trip', user_id=1)\n entry1 = Entry(trip_id=1, name='Tibidabo', address='08035 Barcelona, Spain', notes='Fun day trip!',\n type_id=1)\n category1 = Category(name='Attraction')\n share1 = Share(viewer_id=2, trip_id=1)\n\n db.session.add_all([user1, user2, trip1, entry1, category1, share1])\n db.session.commit()","def load_data(self) -> None:","def generate_db_data(data):\n # pipeline database default is mongodb://localhost:27017/example_db\n db = hybrid.db.init(dbtype, database=MONGODB_URI, view_files=[], push_views=False, create=True,\n delete_existing=True)\n\n # training database for classifier default is mongodb://localhost:27017/example_training_db\n train_db = hybrid.db.init(dbtype, database=MONGODB_TRAINING_URI, view_files=[], push_views=False, create=True,\n delete_existing=True)\n\n # start inserting data\n try:\n if arguments.add_training_data:\n train = simple_generator_function(data)\n for _ in range(TRAINING_SIZE):\n train_data = train.next()\n print \"%s\\n inserting training data into \\t::::: example_training_db :::::\" % train_data\n db.storeDataBlobArray([data_blob.dict2blob(train_data)])\n\n for iris in simple_generator_function(data):\n print \"%s\\n inserting data into \\t::::: example_db :::::\" % iris\n train_db.storeDataBlobArray([data_blob.dict2blob(iris)])\n time.sleep(TIME_BETWEEN_GUESSES)\n\n except (KeyboardInterrupt, SystemExit):\n print \"\\n\"\n print \"closing db connection to example_db and example_training_db\"\n db.close()\n train_db.close()\n print \"closed\"\n except:\n raise","def load_data(self):\n try:\n self.manager.load()\n except error:\n show_error_message(title='Initialization error!',\n message='File lords.sdb was not found!')\n else:\n self.update_widgets_values()","def main():\n\n db_config_params = config.get_db_config('psql')\n print(f'Connecting to {db_config_params[\"database\"]} database.')\n\n try:\n # Will commit and close the connection\n with psycopg2.connect(**db_config_params) as connection:\n with connection.cursor() as cursor:\n # Using the sql statements to modify the tables\n cursor.execute('CREATE TABLE If NOT EXISTS student(id SERIAL PRIMARY KEY, name VARCHAR);')\n # cursor.execute(\"INSERT INTO student(name) VALUES (%s);\", ('Jack Smith',))\n # connection.commit()\n cursor.execute('SELECT * FROM student;')\n print(*cursor.fetchall(), sep='\\n')\n\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)","def command_dbtool(self):\n dbtool.main(*self.args())","def startup(self):\n self.load_up_initial_db(TIMESTAMP_PARSE_DICT)\n self.add_numeric_cols()","def initialize_database():\n db = Database(database_name)\n i, m, u, p = db.fetch_needed_data()\n\n return i, m, u, p","def start ( r, w, args ):\n sqlLoginInfo = parseArgs ( args )\n sql = sqlLogin ( sqlLoginInfo )\n xml = importXml ( r )\n createTables ( sql )\n importDB ( sql, xml )\n exportXML = exportDB ( sql )\n sql.close ()\n exportXml ( w, exportXML )","def load():\r\n global database\r\n global ranks\r\n \r\n osPlatform = (\"Windows\" if os.name == \"nt\" else \"Linux\" if os.name == \"posix\" else os.name)\r\n debug.write('Log file started at %s' % time.strftime(\"%A %d %B %Y - %H:%M:%S\"), 0, False)\r\n debug.write('\\n*******************************************************', 0, False)\r\n debug.write('[SourceRPG]: Turning your Server into a Role Playing Game', 0, False)\r\n debug.write('[SourceRPG]: Current Version - %s' % info.version, 0, False)\r\n debug.write('[SourceRPG]: Made by %s' % info.author, 0, False)\r\n debug.write('\\nSystem Info:', 0, False)\r\n debug.write('\\tOS: %s' % osPlatform, 0, False)\r\n debug.write('\\tEventscripts Version: %s' % es.ServerVar('eventscripts_ver'), 0, False)\r\n debug.write('\\tCorelib Version: %s' % es.ServerVar('es_corelib_ver'), 0, False)\r\n debug.write('\\tEventscript Tools Version: %s' % es.ServerVar('est_version'), 0, False)\r\n debug.write('\\tEventscripts Noisy: %s' % es.ServerVar('eventscripts_noisy'), 0, False)\r\n debug.write('\\tPopuplib version: %s' % popuplib.info.version, 0, False) \r\n \r\n cmdlib.registerSayCommand(\"rpgmenu\", sayCommands.mainMenu, \"Opens the rpg main menu\")\r\n cmdlib.registerSayCommand(\"rpgupgrade\", sayCommands.upgradeMenu, \"Opens the upgrade menu\")\r\n cmdlib.registerSayCommand(\"rpgsell\", sayCommands.sellMenu, \"Opens the sell menu\")\r\n cmdlib.registerSayCommand(\"rpghelp\", sayCommands.helpMenu, \"Opens the help menu\")\r\n cmdlib.registerSayCommand(\"rpgstats\", sayCommands.stats, \"Opens the stats menu for the user or another player\")\r\n cmdlib.registerSayCommand(\"rpgrank\", sayCommands.rank, \"Tells the player their rank or another player's rank\")\r\n cmdlib.registerSayCommand(\"rpgpopup\", sayCommands.togglePopup, \"Tells the player their rank or another player's rank\")\r\n cmdlib.registerSayCommand(\"rpgtop10\", sayCommands.top10, \"Sends the player the last updated top 10 scores\")\r\n \r\n es.server.cmd(\"exec sourcerpg/skill_loader.cfg\")\r\n \r\n es.server.cmd(\"exec sourcerpg/addon_loader.cfg\")\r\n \r\n skillConfig.write(True)\r\n skillConfig.execute(True, True)\r\n\r\n debug.write('[SourceRPG] Starting the popup creation', 0, False)\r\n\r\n \"\"\" Create the default popups which aren't unique to players \"\"\"\r\n rpgmenu = popuplib.easymenu(\"sourcerpg_rpgmenu\", \"_popup_choice\", popups.rpgmenu)\r\n rpgmenu.settitle(\"=== %s Menu ===\" % prefix)\r\n rpgmenu.addoption(1, \"Upgrade Skills\")\r\n rpgmenu.addoption(2, \"Sell Skills\")\r\n rpgmenu.addoption(3, \"RPG Help\")\r\n rpgmenu.addoption(4, \"RPG Stats\")\r\n rpgmenu.addoption(5, \"Reset Skills\")\r\n \r\n helpMenu = popuplib.easymenu('sourcerpg_help', '_popup_choice', popups.helpmenu)\r\n helpMenu.settitle('=== %s Help ===' % prefix)\r\n helpMenu.addoption(1, 'About SourceRPG')\r\n helpMenu.addoption(2, 'List of Commands')\r\n helpMenu.addoption(3, 'About SourceRPG Skills')\r\n helpMenu.addoption(4, 'Credit')\r\n helpMenu.submenu(10, \"sourcerpg_rpgmenu\")\r\n \r\n confirmation = popuplib.easymenu('sourcerpg_confirm', '_popup_choice', popups.confirm)\r\n confirmation.settitle(\"=== %s Reset Stats ===\" % prefix)\r\n confirmation.setdescription(\"\"\"Are you sure you want to remove\r\nyour skills? There is no chance\r\nor recovering them again!\"\"\")\r\n confirmation.addoption(True, \"Yes\")\r\n confirmation.addoption(False, \"No\")\r\n \r\n about = popuplib.create('sourcerpg_about')\r\n about.addline('=== About %s ===' % prefix)\r\n about.addline('-' * 30)\r\n about.addline('SourceRPG is a python coded mod')\r\n about.addline('for EventScripts 2+. It enables')\r\n about.addline('players to gain Levels, by gaining')\r\n about.addline('XP from certain events, such as')\r\n about.addline('planting the bomb, or killing')\r\n about.addline('another player. Each level gives')\r\n about.addline('%s Credits, which allows you to' % creditsReceived)\r\n about.addline('buy certain skills which aid you')\r\n about.addline('in killing other players.')\r\n about.addline('-' * 30)\r\n about.addline('->8. Back')\r\n about.addline('0. Cancel')\r\n about.submenu(8, 'sourcerpg_help')\r\n \r\n commandspopup = popuplib.create('sourcerpg_commands')\r\n commandspopup.addline(\"=== %s Commands ===\" % prefix)\r\n commandspopup.addline(\"-\" * 30)\r\n commandspopup.addline(\"rpghelp - displays the help menu\")\r\n commandspopup.addline(\"rpgmenu - displays the main menu\")\r\n commandspopup.addline(\"rpgrank - displays your RPG rank\")\r\n commandspopup.addline(\"rpgpopup - toggles on / off automatic popup display\")\r\n commandspopup.addline(\"rpgupgrade - upgrade skills\")\r\n commandspopup.addline(\"rpgsell - sell skills\")\r\n commandspopup.addline(\"rpgstats - display your stats\")\r\n commandspopup.addline(\"-\" * 30)\r\n commandspopup.addline(\"->8. Back\")\r\n commandspopup.addline(\"0. Cancel\")\r\n commandspopup.submenu(8, 'sourcerpg_help')\r\n \r\n creditmenu = popuplib.create('sourcerpg_creditmenu') \r\n creditmenu.addline('=== %s Credits ===' % prefix)\r\n creditmenu.addline('-' * 30)\r\n creditmenu.addline(info.author)\r\n creditmenu.addline(' Script Creator')\r\n creditmenu.addline(' ')\r\n creditmenu.addline('SumGuy14 and Murphey')\r\n creditmenu.addline(' Letting me use their Long Jump code')\r\n creditmenu.addline(' ')\r\n creditmenu.addline('SuperDave')\r\n creditmenu.addline(' He turned my failing SmogNade code into')\r\n creditmenu.addline(' a working code! Thank him for that skill.')\r\n creditmenu.addline(' ')\r\n creditmenu.addline('JoeyT2008 (Jordan Thomas)')\r\n creditmenu.addline(' Awesome scripter who made the database conversion')\r\n creditmenu.addline(' ')\r\n creditmenu.addline('EventScripts Community')\r\n creditmenu.addline(' Help and support, and such a good plugin.')\r\n creditmenu.addline('-' * 30)\r\n creditmenu.addline('8. Back')\r\n creditmenu.addline('0. Cancel')\r\n creditmenu.submenu(8, 'sourcerpg_help')\r\n \r\n debug.write('[SourceRPG] Popups created', 0, False)\r\n \r\n \r\n if int(turboMode):\r\n database = DATABASE_STORAGE_METHOD(\":memory:\")\r\n else:\r\n database = DATABASE_STORAGE_METHOD(databasePath)\r\n \r\n ranks = RankManager()\r\n \r\n \"\"\" If the script is loaded late then make sure all players are inserted \"\"\"\r\n if es.getplayercount():\r\n for player in es.getUseridList():\r\n players.addPlayer( player )\r\n \r\n es.server.queuecmd('mp_restartgame 1')\r\n\r\n if str( es.ServerVar('eventscripts_currentmap')):\r\n es_map_start({})\r\n\r\n \"\"\" If we want to save by intervals then create a repeat to save the database \"\"\"\r\n if str( saveType ) == \"intervals\":\r\n gamethread.delayedname(float(saveLength), 'sourcerpg_databasesave', saveDatabase)\r\n \r\n debug.write('[SourceRPG]: Finished Loading... Enjoy your stay!', 0, False)\r\n debug.write('*******************************************************\\n', 0, False)","def initialize_database():\n # TODO: Refactor the funtime library\n this.db = Store(this.host).create_lib(this.store_name).get_store()","def load_database(db_session, fixture):\n # TODO: the fixture file path controls\n\n # load the fixture\n datas = pickle.loads(fixture)\n db_session.add_all(datas)\n db_session.commit()\n print \"load database ok\"","def setup_database() -> sqlite3.Cursor:\n conn = sqlite3.connect(':memory:')\n cursor = conn.cursor()\n\n insert_books(cursor)\n insert_lookups(cursor)\n\n return cursor","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n hostname = get_hostname(config)\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(\n hostname,\n config.get(\"CLUSTER\", \"DB_NAME\"),\n config.get(\"CLUSTER\", \"DB_USER\"),\n config.get(\"CLUSTER\", \"DB_PASSWORD\"),\n config.get(\"CLUSTER\", \"DB_PORT\"))\n )\n\n cur = conn.cursor()\n drop_tables(cur, conn)\n create_tables(cur, conn)\n\n conn.close()","def init_db(app, test_data=False, load_init_data=True):\r\n db.app = app\r\n db.init_app(app)\r\n db.create_all()","def add_data_db_command():\n click.echo(\"Adding data to the database.\")\n add_data_db()\n click.echo(\"Finished adding data to the database.\")","def main():\n args = parse_args()\n\n make_session = bootstrap(\n 'sqlite:///{}'.format(DB_NAME),\n )\n session = make_session()\n\n for vase in session.query(Vase).order_by(Vase.produced_start):\n for query_str in make_searches(vase):\n pass","def main():\n os.makedirs(PATH)\n fetch_data()\n convert_to_json(model_list, 'models.json', is_model=True)\n convert_to_json(backend_list, 'backends.json')\n convert_to_json(type_list, 'types.json')\n convert_to_json(featurizer_list, 'featurizers.json')","def load_fixtures(self, dbname, table, data):\n db = self.databases[dbname]['db']\n db.execute('BEGIN')\n for row in data:\n columns = row.keys()\n q = db.Insert(table, cols=columns)\n db.execute(q, row)\n db.execute('COMMIT')","def run(args):\n print(\"matdb Copyright (C) 2019 HALL LABS\")\n print(\"This program comes with ABSOLUTELY NO WARRANTY.\")\n print(\"This is free software, and you are welcome to redistribute it under \"\n \"certain conditions.\")\n if args is None:\n return\n\n #No matter what other options the user has chosen, we will have to create a\n #database controller for the specification they have given us.\n from matdb.database import Controller\n cdb = Controller(args[\"dbspec\"])\n\n if args[\"d\"]:\n _generic_find(cdb, \"Database Context Instances\", args[\"p\"])\n if args[\"t\"]:\n _generic_find(cdb.trainers, \"Fitter Context Instances\", args[\"p\"])","def read_database():\n file = tables.open_file(glob.datafile)\n table_d = file.root.VelibData.dynamic\n table_s = file.root.VelibData.static\n n_rows = len(table_d)\n print \"Nrows in dynamic table:\", n_rows\n print \"N stations:\", len(table_d[0][\"last_update\"])\n print \"Time of most recent sampling:\", \\\n time.asctime(time.localtime(recover_time(table_d[-1][\"sample_time\"])))\n print \"Nbikes available at most recent sampling:\", \\\n table_d[n_rows-1][\"available_bikes\"]\n print \"Time of last_update at most recent sampling:\", \\\n time.asctime(\n time.localtime(recover_time(table_d[n_rows-1][\"last_update\"][0])))\n print \"Number arr\", table_s[0][\"number\"]\n file.close()","def initdb_command():\n db.drop_all()\n db.create_all()\n if LOAD_DUMMY_DATA:\n setup_dummy_data()\n\n print('Initialized the database.')","def load_drugs():\n\n print(\"Drugs\")\n\n Drug.query.delete()\n\n with open(\"seed_data/drug_seed.psv\") as drugs:\n for row in drugs:\n name = row.strip()\n\n drug = Drug(generic_name=name)\n\n db.session.add(drug)\n\n db.session.commit()","def initialize():\n\n db.connect() # Se conecta\n db.create_tables([Entry], safe=True) # Crea las tablas\n # safe=true evita crear modelos ya creados","def main():\n\n from argparse import ArgumentParser\n parser = ArgumentParser()\n parser.add_argument('messages_filepath', help='The location of the csv file containing your messages.')\n parser.add_argument('categories_filepath', help='The location of the csv file containing your message categories.')\n parser.add_argument('database_filepath', help='The location to which you wish to save the cleaned messages database file.')\n \n # parse args or raise expection on failure\n try:\n args = parser.parse_args()\n except:\n print('Please provide the filepaths of the messages and categories '\\\n 'datasets as the first and second argument respectively, as '\\\n 'well as the filepath of the database to save the cleaned data '\\\n 'to as the third argument. \\n\\nExample: python process_data.py '\\\n 'disaster_messages.csv disaster_categories.csv '\\\n 'DisasterResponse.db')\n raise\n\n # assign command line args to vars\n messages_filepath = args.messages_filepath\n categories_filepath = args.categories_filepath\n database_filepath = args.database_filepath\n\n print('Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}'\n .format(messages_filepath, categories_filepath))\n df = load_data(messages_filepath, categories_filepath)\n\n print('Cleaning data...')\n df = clean_data(df)\n \n print('Saving data...\\n DATABASE: {}'.format(database_filepath))\n save_data(df, database_filepath)\n \n print('Cleaned data saved to database!')","def insert_db():\n populate_tables()","def setUpTestData(cls):\n call_command('loaddata', 'db.json', verbosity=0)","def main():\n\n dbase = sqlite3.connect('database.db')\n with dbase:\n cursor = dbase.cursor()\n try:\n cursor.execute(\"DROP TABLE Graph;\")\n except sqlite3.OperationalError:\n pass\n\n try:\n cursor.execute(\"CREATE TABLE Graph (name TEXT, json TEXT);\")\n except sqlite3.OperationalError:\n pass\n\n for i, item in enumerate(SOME_JSONS):\n cursor.execute(\"insert into Graph (name, json) values\"\n \"(?, ?);\",\n ('example_' + str(i + 1), json.dumps(item)))"],"string":"[\n \"def main():\\n\\n\\tgdl = TwitterDataLoader()\\t\\n\\tgdl.load_twitter_data_to_db(truncate_table=False, skip_loaded_files=True)\",\n \"def populate_db_command():\\n print(\\\"Populating DB with sample data.\\\")\\n populate_db()\\n print \\\"Done\\\"\",\n \"def main():\\r\\n\\r\\n # delete the database file if it already exists\\r\\n db_path = Path('../../data/db.sqlite')\\r\\n db_path.unlink(missing_ok=True)\\r\\n\\r\\n # create the database\\r\\n with sqlite3.connect(db_path) as connection:\\r\\n create_database(connection)\",\n \"def main():\\n\\n conn = psycopg2.connect(\\n \\\"host=127.0.0.1 dbname=sparkifydb user=student password=student\\\")\\n cur = conn.cursor()\\n\\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\\n\\n conn.close()\",\n \"def main():\\n\\n database_file = Path(__file__).parent.parent / \\\"src\\\" / \\\"galois\\\" / \\\"_databases\\\" / \\\"irreducible_polys.db\\\"\\n conn, cursor = create_database(database_file)\\n\\n _add_hpl_1998(conn, cursor)\\n\\n conn.close()\",\n \"def main():\\r\\n conn = psycopg2.connect(\\\"host=127.0.0.1 dbname=sparkifydb2 user=postgres password=261998\\\")\\r\\n cur = conn.cursor()\\r\\n\\r\\n process_data(cur, conn, filepath='C:/Users/AG/Downloads/Data-engineering-nanodegree-master/Data-engineering-nanodegree-master/1_dend_data_modeling/P1_Postgres_Data_Modeling_and_ETL/data/song_data', func=process_song_file)\\r\\n process_data(cur, conn, filepath='C:/Users/AG/Downloads/Data-engineering-nanodegree-master/Data-engineering-nanodegree-master/1_dend_data_modeling/P1_Postgres_Data_Modeling_and_ETL/data/log_data', func=process_log_file)\\r\\n\\r\\n conn.close()\",\n \"def main():\\n conn = psycopg2.connect(\\\"host=127.0.0.1 dbname=hallo user=hallo password=hallo\\\")\\n cur = conn.cursor()\\n\\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\\n\\n conn.close()\",\n \"def main():\\n database = dbdir + \\\"pyflangun.db\\\"\\n sql_create_airports_table = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS airports (\\n id integer PRIMARY KEY,\\n name text NOT NULL,\\n icao text\\n );\\\"\\\"\\\"\\n sql_create_weather_table = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS weather (\\n id integer PRIMARY KEY,\\n icao text NOT NULL,\\n temp_f integer NOT NULL,\\n dew_pt_f integer NOT NULL,\\n status integer NOT NULL,\\n FOREIGN KEY (icao) REFERENCES airports (icao)\\n );\\\"\\\"\\\"\\n # create a database connection\\n conn = create_connection(database)\\n # create tables\\n if conn is not None:\\n create_table(conn, sql_create_airports_table)\\n create_table(conn, sql_create_weather_table)\\n else:\\n print(\\\"Error! cannot create the database connection.\\\")\",\n \"def main():\\n conn = psycopg2.connect('host=127.0.0.1 dbname=sparkifydb user=student password=student')\\n cur = conn.cursor()\\n\\n process_data(cur, conn, filepath='data/song_data', function=process_song_file)\\n process_data(cur, conn, filepath='data/log_data', function=process_log_file)\\n\\n conn.close()\",\n \"def main():\\n if len(sys.argv) == 4:\\n\\n messages_path, categories_path, database_path = sys.argv[1:]\\n\\n print('Loading data...\\\\n MESSAGES: {}\\\\n CATEGORIES: {}'\\n .format(messages_path, categories_path))\\n df = load_data(messages_path, categories_path)\\n\\n print('Cleaning data...')\\n df = clean_data(df)\\n\\n print('Saving data...\\\\n DATABASE: {}'.format(database_path))\\n save_data(df, database_path)\\n\\n print('Cleaned data saved to database!')\\n\\n else:\\n print('Please provide the filepaths of the messages and categories '\\n 'datasets as the first and second argument respectively, as '\\n 'well as the filepath of the database to save the cleaned data '\\n 'to as the third argument. \\\\n\\\\nExample: python process_data.py '\\n 'disaster_messages.csv disaster_categories.csv '\\n 'DisasterResponse.db')\",\n \"def importData():\\n #importChallengeDataToDB()\\n importTrendingDataToDB()\",\n \"def main() -> None:\\n if len(sys.argv) == 4:\\n\\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\\n\\n print(\\n \\\"Loading data...\\\\n MESSAGES: {}\\\\n CATEGORIES: {}\\\".format(\\n messages_filepath, categories_filepath\\n )\\n )\\n df = load_data(messages_filepath, categories_filepath)\\n\\n print(\\\"Cleaning data...\\\")\\n df = clean_data(df)\\n\\n print(\\\"Saving data...\\\\n DATABASE: {}\\\".format(database_filepath))\\n save_data(df, database_filepath)\\n\\n print(\\\"Cleaned data saved to database!\\\")\\n\\n else:\\n print(\\n \\\"Please provide the filepaths of the messages and categories \\\"\\n \\\"datasets as the first and second argument respectively, as \\\"\\n \\\"well as the filepath of the database to save the cleaned data \\\"\\n \\\"to as the third argument. \\\\n\\\\nExample: python process_data.py \\\"\\n \\\"disaster_messages.csv disaster_categories.csv \\\"\\n \\\"DisasterResponse.db\\\"\\n )\",\n \"def main():\\n langs = []\\n\\n with open(\\\"sql/07_populate.sql\\\", 'w', encoding='utf8') as sql:\\n sql.write(\\\"--this file is generated from csv files in data folder\\\\n\\\\n\\\")\\n\\n langs = write_lang_city(sql)\\n write_groups_diets(sql, langs)\\n\\n with open(\\\"sql/10_populate_test_data.sql\\\", 'w', encoding='utf8') as sql:\\n sql.write(\\\"--this file is generated from csv files in moc_data folder\\\\n\\\\n\\\")\\n write_test_data(sql)\\n # This command sets postgis coordinates based on latitude and longitude\\n sql.write(\\\"UPDATE restaurant SET geo_location = ST_POINT(latitude, longitude);\\\\n\\\")\\n sql.close()\",\n \"def populate_db():\\n stdout.write('Emptying the tables...\\\\n')\\n empty_tables()\\n stdout.write('Populating Language records...\\\\n')\\n populate_language()\\n stdout.write('Populating Lemma, Wordform, and Definition records...\\\\n')\\n populate_lexical()\\n stdout.write('Populating ProperName records...\\\\n')\\n populate_proper_names()\",\n \"def main():\\n\\n if len(sys.argv) == 4:\\n\\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\\n\\n print('Loading data...\\\\n MESSAGES: {}\\\\n CATEGORIES: {}'\\n .format(messages_filepath, categories_filepath))\\n df = load_data(messages_filepath, categories_filepath)\\n\\n print('Cleaning data...')\\n df = clean_data(df)\\n \\n print('Saving data...\\\\n DATABASE: {}'.format(database_filepath))\\n save_data(df, database_filepath)\\n \\n print('Cleaned data saved to database!')\\n \\n else:\\n print('Please provide the filepaths of the messages and categories '\\\\\\n 'datasets as the first and second argument respectively, as '\\\\\\n 'well as the filepath of the database to save the cleaned data '\\\\\\n 'to as the third argument. \\\\n\\\\nExample: python process_data.py '\\\\\\n 'disaster_messages.csv disaster_categories.csv '\\\\\\n 'DisasterResponse.db')\",\n \"def main():\\n # Create a connection to database\\n try:\\n conn = psycopg2.connect(\\\"host=127.0.0.1 dbname=sparkifydb user=student password=student\\\")\\n except psycopg2.Error as e:\\n print(\\\"Error: Unable to make connection to Postgres Database {}\\\".format(\\\"sparkifydb\\\"))\\n print(e)\\n \\n # Get cursor\\n try:\\n cur = conn.cursor()\\n except psycopg2.Error as e:\\n print(\\\"Error: Unable to get cursor to the Database\\\")\\n print(e)\\n\\n # set the autocommit to true to avoid conn.commit() after each command\\n conn.set_session(autocommit=True)\\n\\n # use the process_data function to get all log and song data files contents inserted in tables\\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\\n\\n # Close the connection to the database\\n conn.close()\",\n \"def main():\\n if len(sys.argv) == 4:\\n\\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\\n\\n print('Loading data...\\\\n MESSAGES: {}\\\\n CATEGORIES: {}'\\n .format(messages_filepath, categories_filepath))\\n df = load_data(messages_filepath, categories_filepath)\\n\\n print('Cleaning data...')\\n df = clean_data(df)\\n \\n print('Saving data...\\\\n DATABASE: {}'.format(database_filepath))\\n save_data(df, database_filepath)\\n \\n print('Cleaned data saved to database!')\\n \\n else:\\n print('Please provide the filepaths of the messages and categories '\\\\\\n 'datasets as the first and second argument respectively, as '\\\\\\n 'well as the filepath of the database to save the cleaned data '\\\\\\n 'to as the third argument. \\\\n\\\\nExample: python process_data.py '\\\\\\n 'disaster_messages.csv disaster_categories.csv '\\\\\\n 'DisasterResponse.db')\",\n \"def main():\\n \\n conn = psycopg2.connect(\\\"host=127.0.0.1 dbname=sparkifydb user=student password=student\\\")\\n \\n cur = conn.cursor()\\n process_data(cur, conn, filepath='data/song_data',\\n func=process_song_file) \\n \\n process_data(cur, conn, filepath='data/log_data',\\n func=process_log_file)\\n \\n conn.close()\",\n \"def main():\\n db = _db.Database(experiment.ORACLE_PATH)\\n db.populate_kernel_names_table()\\n db.commit()\",\n \"def main():\\n \\n if len(sys.argv) == 4:\\n\\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\\n\\n print('Loading data...\\\\n MESSAGES: {}\\\\n CATEGORIES: {}'\\n .format(messages_filepath, categories_filepath))\\n df = load_data(messages_filepath, categories_filepath)\\n\\n print('Cleaning data...')\\n df = clean_data(df)\\n \\n print('Saving data...\\\\n DATABASE: {}'.format(database_filepath))\\n save_data(df, database_filepath)\\n \\n print('Cleaned data saved to database!')\\n \\n else:\\n print('Please provide the filepaths of the messages and categories '\\\\\\n 'datasets as the first and second argument respectively, as '\\\\\\n 'well as the filepath of the database to save the cleaned data '\\\\\\n 'to as the third argument. \\\\n\\\\nExample: python process_data.py '\\\\\\n 'disaster_messages.csv disaster_categories.csv '\\\\\\n 'DisasterResponse.db')\",\n \"def main():\\r\\n if len(sys.argv)==4:\\r\\n\\r\\n # files path\\r\\n m_file_path,c_file_path,database = sys.argv[1:]\\r\\n\\r\\n # first, read the data\\r\\n print('Reading the data...')\\r\\n df = read_data(m_file_path,c_file_path)\\r\\n print('OK!')\\r\\n print(' ')\\r\\n \\r\\n # clean it\\r\\n print('Cleaning the data...')\\r\\n df = clean_data(df)\\r\\n print('OK!')\\r\\n print(' ')\\r\\n \\r\\n # save it\\r\\n print('Saving data...')\\r\\n save_data(df,database)\\r\\n print(' ')\\r\\n \\r\\n # when it's done\\r\\n print(f'Cleaned data is stored in {database[:-3]} database') \\r\\n\\r\\n else:\\r\\n print('Please provide the filepaths of the messages and categories '\\\\\\r\\n 'datasets as the first and second argument respectively, as '\\\\\\r\\n 'well as the filepath of the database to save the cleaned data '\\\\\\r\\n 'to as the third argument. \\\\n\\\\nExample: python process_data.py '\\\\\\r\\n 'disaster_messages.csv disaster_categories.csv '\\\\\\r\\n 'DisasterResponse.db')\",\n \"def main():\\n conn = psycopg2.connect(f\\\"host=127.0.0.1 dbname=sparkifydb user={username} password={password}\\\")\\n cur = conn.cursor()\\n conn.set_session(autocommit=True)\\n\\n artists_data, songs_data = process_song_file()\\n songplays_help_df, time_data, users_data = process_log_file()\\n songplays_data = process_songplays_data(artists_data, songs_data, songplays_help_df)\\n\\n data_list = [songplays_data, users_data, songs_data, artists_data, time_data]\\n for idx, (data, query) in enumerate(zip(data_list, insert_table_queries), start=1):\\n print(f\\\"inserting file {idx}/{len(data_list)}\\\")\\n for row in data:\\n try:\\n cur.execute(query, row)\\n except psycopg2.Error as error:\\n print(f\\\"Psychog2 error @ file {idx} row {row}: {error} NOTE: this file will not be inserted.\\\")\\n\\n conn.close()\",\n \"def main():\\n db.connect()\\n db.execute_sql('PRAGMA foreign_keys = ON;')\\n db.create_tables([\\n Job,\\n Person\\n ])\\n\\n people = [\\n ('Andrew', 'Sumner', 'Andy'),\\n ('Peter', 'Seattle', None),\\n ('Susan', 'Boston', 'Beannie'),\\n ('Steven', 'Colchester', None),\\n ('Peter', 'Seattle', None),\\n ]\\n\\n for person in people:\\n try:\\n with db.transaction():\\n new_person = Person.create(\\n person_name = person[0],\\n lives_in_town = person[1],\\n nickname = person[2],)\\n new_person.save()\\n\\n except Exception as e:\\n logger.info(f'Error creating person = {person[0]}')\\n logger.info(e)\\n logger.info('See how the datbase protects our data')\\n\\n for person in Person:\\n person.show()\\n\\n jobs = [\\n ('Analyst', '2017-02-01', '2019-07-31', 34.999, 'Andrew'),\\n ('Developer', '2017-02-01', '2019-07-31', 34.999, 'Fred'),\\n ]\\n\\n for job in jobs:\\n try:\\n with db.transaction():\\n new_job = Job.create(\\n job_name = job[0],\\n start_date = job[1],\\n end_date = job[2],\\n salary = job[3],\\n person_employed = job[4],\\n )\\n new_job.save()\\n\\n except Exception as e:\\n logger.info(f'Error creating job = {job[0]}')\\n logger.info(e)\\n logger.info('See how the datbase protects data across tables')\\n\\n logger.info(\\\"don't forget - but can you find a better way?\\\")\\n db.close()\",\n \"def import_db(import_file):\\n import_data(import_file)\",\n \"def connect_db_and_load_data(cls):\\n db.connect()\\n db.create_tables([Product], safe=True)\\n load_data(transform_data('./inventory.csv'))\",\n \"def main():\\n\\t\\n\\ttry:\\n\\t conn = mysql.connector.connect(user = 'root', password = 'love',\\n\\t host = '127.0.0.1',\\n\\t database = 'promap')\\n\\texcept mysql.connector.Error as err:\\n\\t if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:\\n\\t print(\\\"password and name pair incorrect\\\")\\n\\t elif err.errno == errorcode.ER_BAD_DB_ERROR:\\n\\t print(\\\"no such database\\\")\\n\\t else:\\n\\t print(err)\\n\\t exit(1)\\n\\n\\t# obtain connection cursor\\n\\tcursor = conn.cursor()\\n\\ti = 0\\n\\t# walk through all the files\\n\\trootDirectory = 'C:/Users/Zhou/Downloads/ProMap_Data/articles.O-Z'\\n\\tfor aDir, theSubdirs, theFiles in os.walk(rootDirectory):\\n\\t\\t\\n\\t\\tfor aFile in theFiles:\\n\\t\\t\\tfilename = os.path.join(aDir, aFile)\\n\\t\\t\\tprint(aFile)\\n\\t\\t\\tdata = NXMLParser.parse(filename)\\n\\t\\t\\tif (data[0] == -1): \\n\\t\\t\\t\\ti = i + 1\\n\\t\\t\\t\\tprint(i)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tDatabaseUpdater.update(cursor, data)\\n\\t\\t\\tconn.commit()\\n\\n\\tcursor.close()\\n\\tconn.close()\",\n \"def load_DB(self):\\n\\t\\tprint 'Loadind Data Base...'\\n\\t\\tstream = open(self.DB_file)\\n\\t\\tself.DB = cPickle.load(stream)\\n\\t\\tstream.close()\\n\\t\\tprint \\\"Number of documents in the Data Base: \\\", self.DB.nb_doc_total\\n\\t\\tprint 'Loading completed'\\n\\t\\treturn\",\n \"def main():\\n program = None\\n try:\\n program = DataUploader()\\n program.clear_db()\\n program.upload_data()\\n except Exception as e:\\n traceback.print_exc()\\n print(\\\"ERROR: Failed to use database:\\\", e)\\n finally:\\n if program:\\n program.connection.close_connection()\",\n \"def main():\\n cur, conn = connect('dwh.cfg')\\n \\n set_schema = schema_queries[1]\\n cur.execute(set_schema)\\n \\n print('Loading Staging Tables.')\\n load_staging_tables(cur, conn)\\n \\n print('Inserting Rows.')\\n insert_tables(cur, conn)\\n\\n \\n conn.close()\",\n \"def load_file():\\n global list_of_table, data_base, new_data\\n open_name = askopenfilename()\\n\\n if Path(open_name).suffix == '.db':\\n data_base = open_name\\n data_base = str(data_base)\\n new_data_base = parse(data_base)\\n new_data = update_list_tables(new_data_base)\\n new_data.clear()\\n\\n else:\\n mistake_db_file()\",\n \"def main():\\n\\n populate('monsters')\\n populate('spells')\\n populate('conditions')\\n populate('encounters')\\n populate('characters')\\n populate('actions')\",\n \"def os_start_db( self, ):\\r\\n pass\",\n \"def main():\\r\\n db = connect_database()\\r\\n with db:\\r\\n if sys.argv[1] == \\\"-s\\\":\\r\\n select_all(db, sys.argv[2])\\r\\n elif sys.argv[1] == \\\"-i\\\":\\r\\n cus_data = []\\r\\n for i in range(2, len(sys.argv)):\\r\\n cus_data.append(sys.argv[i])\\r\\n insert_customer(db, cus_data)\\r\\n elif sys.argv[1] == \\\"-c\\\":\\r\\n create_tables()\\r\\n elif sys.argv[1] == \\\"-pw\\\":\\r\\n pop_waiting(db, sys.argv[2])\\r\\n elif sys.argv[1] == \\\"-ph\\\":\\r\\n pop_help(db, sys.argv[2])\\r\\n elif sys.argv[1] == \\\"-r\\\":\\r\\n refresh_tables(db)\\r\\n elif sys.argv[1] == \\\"-e\\\":\\r\\n export_helped_table(db)\\r\\n else:\\r\\n print errorArgument\\r\\n db.close()\",\n \"def main():\\n conn = psycopg2.connect(\\\"host=127.0.0.1 dbname=sparkifydb user=student password=student\\\")\\n cur = conn.cursor()\\n\\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\\n print('song file processing is complete')\\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\\n print('log file processing is complete')\\n conn.close()\",\n \"def build_input_db():\\n build_input_database()\",\n \"def load_data():\\n\\tscores = pd.read_csv('../data/user_assessment_scores.csv')\\n\\tviews = pd.read_csv('../data/user_course_views.csv')\\n\\ttags = pd.read_csv('../data/course_tags.csv')\\n\\tinterests = pd.read_csv('../data/user_interests.csv')\\n\\n\\tdb_file = '../db/usersim.sqlite'\\n\\ttry:\\n\\t\\tengine = sqlite3.connect(db_file, timeout=10)\\n\\t\\tscores.to_sql('scores', engine, if_exists='replace', index=False, index_label='user_handle')\\n\\t\\tviews.to_sql('views', engine, if_exists='replace', index=False, index_label='user_handle')\\n\\t\\ttags.to_sql('tags', engine, if_exists='replace', index=False, index_label='course_id')\\n\\t\\tinterests.to_sql('interests', engine, if_exists='replace', index=False, index_label='user_handle')\\n\\texcept:\\n\\t\\tprint('Error occured while inserting into database')\\n\\tfinally:\\n\\t\\tif engine:\\n\\t\\t\\tengine.close()\\n\\treturn scores, views, tags, interests\",\n \"def main() :\\n\\n global dbMan\\n dbMan = DBManager( 'CMS_HCL_APPUSER_R/HCAL_Reader_55@localhost:1521/cms_omds_lb.cern.ch' )\",\n \"def main(csvfile, dbfile, verbose=False):\\n CONN = sqlite3.connect(dbfile)\\n cursor = CONN.cursor()\\n create_schema(cursor)\\n process_data(cursor, csvfile, verbose=verbose)\\n CONN.commit()\\n CONN.close()\",\n \"def main() -> None:\\n\\n # TODO - Gather this from whatever mechanism is needed\\n data = {\\n \\\"family_members\\\": \\\"\\\",\\n \\\"social_status\\\": \\\"\\\",\\n \\\"gender\\\": \\\"\\\",\\n \\\"date_birth\\\": \\\"\\\",\\n \\\"id_number\\\": 0,\\n \\\"mail\\\": \\\"\\\",\\n \\\"name\\\": \\\"\\\",\\n \\\"id\\\": \\\"\\\",\\n }\\n\\n # Grab an active cursor object\\n cursor = db_connection.cursor()\\n\\n # We know the DB is the part that can break, so we'll wrap this first\\n try:\\n update(cursor, data)\\n print(\\\"Yay this worked\\\") # Check the DB and give some user feedback\\n except pymysql.DatabaseError as e:\\n print(str(e)) # Prints the basic error you encountered\\n # If you hit this section, get rid of the try/except or use pdb to walk the error\\n # There are multiple ways to print the traceback\\n # IMHO the easiest is to just not catch the exception\\n\\n # This should only really be terminated when the application is done\\n db_connection.close()\",\n \"def main(passed_arguments):\\n\\n # use real data as default\\n scripts_path = os.path.abspath(os.path.join(PYTHON_PATH, 'scripts'))\\n meta_path = os.path.abspath(os.path.join(scripts_path, 'meta.json'))\\n manifest_path = os.path.abspath(os.path.join(scripts_path, 'manifest.csv'))\\n\\n # Locally, we can optionally have sample data\\n if passed_arguments.sample and passed_arguments.database != 'remote':\\n meta_path = os.path.abspath(os.path.join(scripts_path,\\n 'meta_sample.json'))\\n manifest_path = os.path.abspath(\\n os.path.join(scripts_path, 'manifest_sample.csv'))\\n\\n # for case of more than one database choice default to the option with\\n # the lowest risk if database is updated\\n if passed_arguments.database == 'docker':\\n database_choice = 'docker_database'\\n drop_tables = True\\n\\n elif passed_arguments.database == 'docker_local':\\n database_choice = 'docker_with_local_python'\\n drop_tables = True\\n\\n elif passed_arguments.database == 'remote':\\n database_choice = 'remote_database'\\n drop_tables = False #TODO this is a hacky way to avoid dropping tables because it's not working with RDS...\\n\\n # Only users with additional admin privileges can rebuild the\\n # remote database\\n if not passed_arguments.update_only:\\n database_choice = 'remote_database_master'\\n\\n # TODO: do we want to default to local or docker?\\n elif passed_arguments.database == 'local':\\n database_choice = 'local_database'\\n drop_tables = True\\n\\n # universal defaults\\n keep_temp_files = True\\n\\n # Instantiate and run the loader\\n loader = LoadData(database_choice=database_choice, meta_path=meta_path,\\n manifest_path=manifest_path,\\n keep_temp_files=keep_temp_files,\\n drop_tables=drop_tables)\\n\\n if passed_arguments.update_only:\\n loader.update_database(passed_arguments.update_only)\\n else:\\n loader.rebuild()\\n\\n\\n\\n #TODO add in failures report here e.g. _failed_table_count\",\n \"def main():\\n args = sys.argv[1:]\\n if len(args) != 2:\\n print('Usage: sqlite.py db SQL', file=sys.stderr)\\n return\\n\\n db, sql = args\\n\\n print('Opening {}'.format(args[0]))\\n conn = connect(db)\\n\\n for q in sql.split(';'):\\n print('Executing {}'.format(q))\\n with conn:\\n conn.execute(q)\",\n \"def dbinit( *args, **kwargs ):\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(*config['CLUSTER'].values()))\\n cur = conn.cursor()\\n \\n load_staging_tables(cur, conn)\\n insert_tables(cur, conn)\\n\\n conn.close()\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(*config['CLUSTER'].values()))\\n cur = conn.cursor()\\n \\n load_staging_tables(cur, conn)\\n insert_tables(cur, conn)\\n\\n conn.close()\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(config['CLUSTER']['HOST'], config['CLUSTER']['DB_NAME'], config['CLUSTER']['DB_USER'], config['CLUSTER']['DB_PASSWORD'], config['CLUSTER']['DB_PORT']))\\n cur = conn.cursor()\\n \\n load_staging_tables(cur, conn)\\n insert_tables(cur, conn)\\n\\n conn.close()\",\n \"def load_movies():\\n print \\\"Movies\\\"\\n\\n # Delete all rows in table, so if we need to run this a second time,\\n # we won't be trying to add duplicate users\\n Movie.query.delete()\\n\\n # Read u.item file and insert data\\n for row in open(\\\"seed_data/u.item\\\"):\\n row =row.rstrip()\\n\\n movie_id, title_long, released_string, imdb_url = row.split(\\\"|\\\")[:4]\\n #we modified the datetime format changed released_string into \\n #new format by using datetim.strptime to convert it. \\n print row\\n if released_string: \\n release_at = datetime.strptime(released_string, \\\"%d-%b-%Y\\\")\\n else: \\n release_at = None \\n\\n #here we stripped the title of the (xxxx) year and parenthesis\\n #using the slice method. \\n title = title_long[:-7]\\n\\n print movie_id, title_long, released_string, imdb_url\\n\\n #assign the return values from our for loop to a new variable\\n movie = Movie(movie_id=movie_id, title=title, released_at=release_at,\\n imdb_url=imdb_url)\\n \\n # We need to add to the session or it won't ever be stored\\n db.session.add(movie)\\n\\n #Once we're done, we should commit our work\\n db.session.commit()\",\n \"def main():\\n ensure_not_root()\\n config.setup()\\n model.init_db()\\n manager.run()\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} \\\\\\n port={}\\\".format(*config['CLUSTER'].values()))\\n cur = conn.cursor()\\n \\n load_staging_tables(cur, conn)\\n insert_tables(cur, conn)\\n\\n conn.close()\",\n \"def run_db(args):\\n # print(\\\"running chronqc_db\\\")\\n chronqc_db.main(args)\",\n \"def main():\\r\\n print(\\\"Initializing Database\\\")\\r\\n global conn\\r\\n conn = create_db()\\r\\n\\r\\n print(\\\"Inserting Raw Ticket Data\\\")\\r\\n create_raw_data()\\r\\n print(\\\"Creating Block Limits DataFrame\\\")\\r\\n\\r\\n global streetnamedict\\r\\n global streetintersections\\r\\n streetintersections, streetnamedict = create_block_limits()\\r\\n\\r\\n print(\\\"Starting Address Creation\\\")\\r\\n global single_address\\r\\n global double_address\\r\\n global addresses\\r\\n single_address, double_address, addresses = create_address_data()\\r\\n print(\\\"Finished Creating addresses\\\")\\r\\n\\r\\n global nhoodtype\\r\\n nhoodtype = pd.read_sql_query('Select nhood, violationdesc, count(*) tickets from raw_ticket_data t1 join single_address t2 '\\r\\n ' on t1.TickStreetNo = t2.number and t1.TickStreetName = t2.streetname group by nhood, violationdesc', conn)\\r\\n\\r\\n print(\\\"Processing Ticket Data\\\")\\r\\n process_ticket_data()\\r\\n print('Finished processing Ticket Data!')\\r\\n print('Starting Street Data Creation')\\r\\n streetsweeping, streetvolume = process_volume()\\r\\n print(' Finished Street Data Creation')\\r\\n\\r\\n print('Pairing Addresses with Street Data')\\r\\n pair_address(streetsweeping, streetvolume)\\r\\n print('Finished Pairing Addresses')\\r\\n\\r\\n print('Pairing Parking')\\r\\n pair_parking(streetvolume)\\r\\n print('Finished Pairing Parking')\\r\\n\\r\\n conn.close()\\r\\n\\r\\n print('Finished Creating Entire Database and updated Shapefiles at {}'.format( dt.datetime.now().strftime(\\\"%H:%M\\\")))\",\n \"def read_db(self):\\n with open(self.filename, 'r') as database:\\n data = json.load(database)\\n self.data = data\",\n \"def structure_and_repopulate_db() -> None:\\n with open('db.sql', encoding=\\\"utf-8\\\") as f:\\n commands = f.read().strip().split(';')\\n commands = [command.strip() for command in commands]\\n for command in commands:\\n my_cursor.execute(command)\\n my_db.commit()\\n print('Source structure created, data repopulated')\",\n \"def main():\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n get_user_config()\",\n \"def main():\\n db = sqlite3.connect(DATABASE_NAME)\\n create_tables(db)\\n\\n # Write your code below\",\n \"def main():\\n # Create the flow\\n with Flow('pickle flow') as flow:\\n db_table = create_table()\\n weather_data = get_weather(LAT_NYC, LON_NYC, 2018)\\n parsed_data = parse_weather(weather_data)\\n populated_table = store_weather(parsed_data)\\n populated_table.set_upstream(db_table)\\n\\n # Run the flow\\n flow.run()\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n print('config file read! - now connecting...')\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(*config['CLUSTER'].values()))\\n cur = conn.cursor()\\n\\n print('connection established! - now dropping tables...')\\n drop_tables(cur, conn)\\n\\n print('tables dropped! - now creating tables...')\\n create_tables(cur, conn)\\n\\n print('tables are now created! - closing connection...')\\n conn.close()\\n\\n print('done!')\",\n \"def main():\\n \\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(*config['CLUSTER'].values()))\\n cur = conn.cursor()\\n \\n load_staging_tables(cur, conn)\\n insert_tables(cur, conn)\\n\\n conn.close()\",\n \"def initdb():\\n\\tc, conn = connect()\\n\\tsql = []\\n\\twith open('data\\\\\\\\database.sql') as f:\\n\\t\\tfor line in f:\\n\\t\\t\\tsql.append(line.strip())\\n\\t\\n\\tfor query in sql:\\n\\t\\tc.execute(query)\\n\\tconn.commit()\",\n \"def load():\\n pr = PLAPIResource()\\n pr.name = 'Programming Languages'\\n pr.uri = 'programming-languages'\\n db.session.add(pr)\\n db.session.commit()\\n\\n pd = PLAPIResource()\\n pd.name = 'Paradigms'\\n pd.uri = 'paradigms'\\n db.session.add(pd)\\n db.session.commit()\\n\\n \\\"\\\"\\\"\\n pt = PLAPIResource()\\n pt.name = 'Tutorials'\\n pt.uri = 'tutorials'\\n db.session.add(pt)\\n db.session.commit()\\n \\\"\\\"\\\"\\n\\n\\n pl = ProgrammingLanguageModel()\\n pl.name = 'Python'\\n pl.slug = 'python'\\n pl.homepage_url = 'https://www.python.org/'\\n pl.is_visible = True\\n db.session.add(pl)\\n db.session.commit()\\n\\n pl2 = ProgrammingLanguageModel()\\n pl2.name = 'Swift'\\n pl2.slug = 'swift'\\n pl2.homepage_url = 'https://developer.apple.com/swift/'\\n pl2.is_visible = True\\n db.session.add(pl2)\\n db.session.commit()\\n\\n pl3 = ProgrammingLanguageModel()\\n pl3.name = 'Ruby'\\n pl3.slug = 'ruby'\\n pl3.homepage_url = 'https://www.ruby-lang.org/'\\n pl3.is_visible = True\\n db.session.add(pl3)\\n db.session.commit()\\n\\n prdgm1 = ParadigmModel()\\n prdgm1.name = 'Imperative'\\n prdgm1.slug = 'imperative'\\n prdgm1.is_visible = True\\n db.session.add(prdgm1)\\n db.session.commit()\\n\\n prdgm2 = ParadigmModel()\\n prdgm2.name = 'Functional'\\n prdgm2.slug = 'functional'\\n prdgm2.is_visible = True\\n db.session.add(prdgm2)\\n db.session.commit()\\n\\n lib = LibraryModel()\\n lib.name = 'Django'\\n lib.slug = 'django'\\n lib.is_visible = True\\n lib.language = pl.id\\n lib.homepage_url = 'https://www.djangoproject.com/'\\n lib.source_code_url = 'https://github.com/django/django'\\n db.session.add(lib)\\n db.session.commit()\\n\\n lib2 = LibraryModel()\\n lib2.name = 'Flask'\\n lib2.slug = 'flask'\\n lib2.is_visible = True\\n lib2.language = pl.id\\n lib2.homepage_url = 'http://flask.pocoo.org/'\\n lib2.source_code_url = 'https://github.com/mitsuhiko/flask'\\n db.session.add(lib)\\n db.session.commit()\\n\\n tut = TutorialModel()\\n tut.name = 'Choose Your Own Adventure Presentations'\\n tut.slug = 'choose-your-own-adventure-presentations'\\n tut.is_visible = True\\n tut.language = pl.id\\n tut.tutorial_url = 'https://www.twilio.com/blog/2014/11/choose-your-own-adventure-presentations-with-reveal-js-python-and-websockets.html'\\n db.session.add(tut)\\n db.session.commit()\\n\\n tut2 = TutorialModel()\\n tut2.name = 'Full Stack Python'\\n tut2.slug = 'full-stack-python'\\n tut2.is_visible = True\\n tut2.language = pl.id\\n tut2.tutorial_url = 'http://www.fullstackpython.com/'\\n db.session.add(tut2)\\n db.session.commit()\",\n \"def populate_db():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n\\n logger.info('Working with Person class')\\n logger.info('Note how I use constants and a list of tuples as a simple schema')\\n logger.info('Normally you probably will have prompted for this from a user')\\n\\n PERSON_NAME = 0\\n LIVES_IN_TOWN = 1\\n NICKNAME = 2\\n\\n people = [\\n ('Andrew', 'Sumner', 'Andy'),\\n ('Peter', 'Seattle', None),\\n ('Susan', 'Boston', 'Beannie'),\\n ('Pam', 'Coventry', 'PJ'),\\n ('Steven', 'Colchester', None),\\n ]\\n\\n logger.info('Creating Person records: iterate through the list of tuples')\\n logger.info('Prepare to explain any errors with exceptions')\\n logger.info('and the transaction tells the database to fail on error')\\n\\n logger.info('Working with Job class')\\n logger.info('Creating Job records. We use the foreign key')\\n\\n JOB_NAME = 0\\n START_DATE = 1\\n END_DATE = 2\\n SALARY = 3\\n PERSON_EMPLOYED = 4\\n\\n jobs = [\\n ('Analyst', '2001-09-22', '2003-01-30',65500, 'Andrew'),\\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew'),\\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000, 'Andrew'),\\n ('Admin supervisor', '2012-10-01', '2014-11,10', 45900, 'Peter'),\\n ('Admin manager', '2014-11-14', '2018-01,05', 45900, 'Peter')\\n ]\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n try:\\n for person in people:\\n with database.transaction():\\n new_person = Person.create(\\n person_name = person[PERSON_NAME],\\n lives_in_town = person[LIVES_IN_TOWN],\\n nickname = person[NICKNAME])\\n new_person.save()\\n logger.info('Database add successful')\\n\\n logger.info('Print the Person records we saved...')\\n for saved_person in Person:\\n logger.info(f'{saved_person.person_name} lives in {saved_person.lives_in_town} and likes to be known as {saved_person.nickname}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {person[PERSON_NAME]}')\\n logger.info(e)\\n logger.info('See how the database protects our data')\\n try:\\n for job in jobs:\\n with database.transaction():\\n new_job = Job.create(\\n job_name = job[JOB_NAME],\\n start_date = job[START_DATE],\\n end_date = job[END_DATE],\\n salary = job[SALARY],\\n person_employed = job[PERSON_EMPLOYED])\\n new_job.save()\\n\\n logger.info('Reading and print all Job rows (note the value of person)...')\\n for job in Job:\\n logger.info(f'{job.job_name} : {job.start_date} to {job.end_date} for {job.person_employed}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {job[JOB_NAME]}')\\n logger.info(e)\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"def do_command(self, args):\\n chk_arg_count(args, 0)\\n dbops.init_database()\",\n \"def main():\\n cur, conn = create_database()\\n \\n drop_tables(cur, conn)\\n create_tables(cur, conn)\\n\\n conn.close()\",\n \"def load_data(self):\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(*config['STAGE'].values()))\\n cur = conn.cursor()\\n \\n #remove the existing tables\\n drop_tables(cur, conn)\\n \\n #replace the tables with new ones\\n create_tables(cur, conn)\\n \\n #add missing postcode value into table\\n default_missing_values(cur, conn)\\n \\n conn.close()\",\n \"def example_data():\\n\\n # In case this is run more than once, empty out existing data\\n User.query.delete()\\n Location.query.delete()\\n\\n # Add sample users and locations\\n\\n trinity = User(fname='Trinity', email='trin@unplugged.com',\\n username='questionit', password='l0lagent')\\n neo = User(fname='Neo', email='theone@unplugged.com',\\n username='neo', password='l0lagent')\\n\\n tacorea = Location(yelp_id='tacorea-san-francisco', name='Tacorea',\\n latitude='37.7749', longitude='122.3392',\\n address='809 Bush St, San Francisco, CA 94108',\\n yelp_url='tacorea@yelp.com', pic='pic')\\n\\n db.session.add_all([trinity, neo, tacorea])\\n db.session.commit()\",\n \"def init_db():\\n # with current_app.open_resource(\\\"schema.sql\\\") as f:\\n # db.executescript(f.read().decode(\\\"utf8\\\"))\\n print(\\\"初始化数据库脚本文件！！！\\\")\",\n \"def load(file_path = \\\"database.pkl\\\"):\\n while True:\\n doLoad = input(\\\"Database contents will be overwritten. Proceed? (y/n): \\\")\\n if doLoad == 'y':\\n break\\n elif doLoad == 'n':\\n return\\n else:\\n continue\\n try:\\n with open(file_path, 'rb') as f:\\n global person_database\\n person_database = pickle.load(f)\\n print(\\\"Database loaded.\\\")\\n except:\\n print(\\\"Database not available.\\\")\",\n \"def populate_db():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('../data/personjob.db') # navigate relative path to the db\\n\\n logger.info('Working with Department class')\\n logger.info('Creating department records')\\n\\n department_number = 0\\n department_name = 1\\n department_manager = 2\\n\\n departments = [\\n ('A111', 'Asset Management', 'Dave Sanders'),\\n ('B222', 'Human Resources', 'Tammy Murray'),\\n ('C333', 'Payroll', 'Daddy Warbucks'),\\n ]\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for department in departments:\\n with database.transaction():\\n new_department = Department.create(\\n department_number = department[department_number],\\n department_name = department[department_name],\\n deptartment_manager = department[department_manager]\\n )\\n new_department.save()\\n logger.info('Department has been added to the database')\\n\\n logger.info('Reading and print all department data...')\\n for saved_department in Department:\\n logger.info(f'{saved_department.department_name} ' + \\\\\\n f'Manager: {saved_department.department_manager}. ' + \\\\\\n f'Department number: {saved_department.department_number}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {department[department_number]}')\\n logger.info(e)\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"def main():\\n cur, conn = create_database()\\n \\n drop_tables(cur, conn)\\n create_tables(cur, conn)\\n\\n cur.close();\\n conn.close()\",\n \"def read_database(db_path, db_file, *args):\\n\\n db_filepath = os.path.join(db_path, db_file)\\n\\n # list to store loaded data\\n data_imported = []\\n conn = sqlite3.connect(db_filepath)\\n\\n for data_name in args:\\n\\n\\n info = f'Reading {data_name} from database................'\\n print(info, end=\\\"\\\")\\n data_name_in_db = conn.execute(\\n f\\\"\\\"\\\"SELECT name FROM sqlite_master WHERE type='table' \\n AND name='{data_name}'; \\\"\\\"\\\").fetchall()\\n if data_name_in_db:\\n df = pd.read_sql(f\\\"select * from {data_name}\\\", con=conn)\\n substitute_names(df)\\n # revert single column DataFrame to Series\\n if 'index' in df.columns:\\n df.set_index('index', inplace=True)\\n df = df.squeeze('columns')\\n data_imported.append(df)\\n print('ok')\\n else:\\n data_imported.append(None)\\n print('no data')\\n conn.close()\\n return data_imported #if len(data_imported)>1 else data_imported[0]\",\n \"def load_data(db_handler):\\n\\n from random import seed\\n from random import random\\n \\n seed(1)\\n\\n new_notes = []\\n\\n for i in range(1,10):\\n\\n new_notes.append({\\n\\n\\n 'title': str(i) + str(random()),\\n 'content': 'Lorem ipsum' + str(i),\\n 'active': True,\\n 'created_by':\\\"Cristhian\\\" + str(i),\\n 'created_at': date.today(),\\n 'edited_at':date.today(),\\n \\n })\\n\\n new_notes.append(\\n {\\n \\\"active\\\": False,\\n \\\"content\\\": \\\"Jesenia\\\",\\n \\\"edited_at\\\": \\\"2019-10-24\\\",\\n \\\"title\\\": \\\"Jesenia La chica de al lado\\\",\\n \\\"created_by\\\": \\\"Cristhian1\\\",\\n \\\"created_at\\\": \\\"2019-10-24\\\"\\n })\\n\\n new_notes.append(\\n {\\n \\\"active\\\": False,\\n \\\"title\\\": \\\"La vida de los numeros\\\",\\n \\\"content\\\": \\\"Lorem ipsum y los numeros de la muerte\\\",\\n \\\"edited_at\\\": \\\"2019-10-25\\\",\\n \\\"created_by\\\": \\\"Jesenia\\\",\\n \\\"created_at\\\": \\\"2019-10-24\\\"\\n })\\n\\n Note.insert_many(new_notes).execute()\\n\\n User(name=\\\"Cristhian\\\", email=\\\"test@gmail.com\\\",\\n password=b'$2b$12$U/QjtHt/j0xRT4r8Hx3fOe93EssM6M0iiUaQJOrTd64RXbxvhw6Ii').save()\",\n \"def gtfsdb_main(ctx, database):\\n ctx.obj = dict()\\n if not database and os.path.exists(DEFAULT_CONFIG_FILE):\\n conf = json.load(open(DEFAULT_CONFIG_FILE, 'r'))\\n database = conf['database']\\n ctx.obj.update(dict(conf=conf))\\n else:\\n click.echo(\\\"No database selected!!\\\")\\n sys.exit(1)\\n ctx.obj.update(dict(database=Database(url=database), db_url=database))\",\n \"def load_data(self):\\n if self.debug:\\n print(\\\"Loading data\\\")\",\n \"def example_data():\\n\\n # In case this is run more than once, empty out existing data\\n User.query.delete()\\n Trip.query.delete()\\n Entry.query.delete()\\n Category.query.delete()\\n Share.query.delete()\\n\\n # Add sample data\\n user1 = User(email='user1@gmail.com', password=bcrypt.hashpw('user1'.encode('utf8'), bcrypt.gensalt(9)), name='One')\\n user2 = User(email='user2@gmail.com', password=bcrypt.hashpw('user2'.encode('utf8'), bcrypt.gensalt(9)), name='Two')\\n trip1 = Trip(location='Spain', date='08/09/2017', name='Abroad Trip', user_id=1)\\n entry1 = Entry(trip_id=1, name='Tibidabo', address='08035 Barcelona, Spain', notes='Fun day trip!',\\n type_id=1)\\n category1 = Category(name='Attraction')\\n share1 = Share(viewer_id=2, trip_id=1)\\n\\n db.session.add_all([user1, user2, trip1, entry1, category1, share1])\\n db.session.commit()\",\n \"def load_data(self) -> None:\",\n \"def generate_db_data(data):\\n # pipeline database default is mongodb://localhost:27017/example_db\\n db = hybrid.db.init(dbtype, database=MONGODB_URI, view_files=[], push_views=False, create=True,\\n delete_existing=True)\\n\\n # training database for classifier default is mongodb://localhost:27017/example_training_db\\n train_db = hybrid.db.init(dbtype, database=MONGODB_TRAINING_URI, view_files=[], push_views=False, create=True,\\n delete_existing=True)\\n\\n # start inserting data\\n try:\\n if arguments.add_training_data:\\n train = simple_generator_function(data)\\n for _ in range(TRAINING_SIZE):\\n train_data = train.next()\\n print \\\"%s\\\\n inserting training data into \\\\t::::: example_training_db :::::\\\" % train_data\\n db.storeDataBlobArray([data_blob.dict2blob(train_data)])\\n\\n for iris in simple_generator_function(data):\\n print \\\"%s\\\\n inserting data into \\\\t::::: example_db :::::\\\" % iris\\n train_db.storeDataBlobArray([data_blob.dict2blob(iris)])\\n time.sleep(TIME_BETWEEN_GUESSES)\\n\\n except (KeyboardInterrupt, SystemExit):\\n print \\\"\\\\n\\\"\\n print \\\"closing db connection to example_db and example_training_db\\\"\\n db.close()\\n train_db.close()\\n print \\\"closed\\\"\\n except:\\n raise\",\n \"def load_data(self):\\n try:\\n self.manager.load()\\n except error:\\n show_error_message(title='Initialization error!',\\n message='File lords.sdb was not found!')\\n else:\\n self.update_widgets_values()\",\n \"def main():\\n\\n db_config_params = config.get_db_config('psql')\\n print(f'Connecting to {db_config_params[\\\"database\\\"]} database.')\\n\\n try:\\n # Will commit and close the connection\\n with psycopg2.connect(**db_config_params) as connection:\\n with connection.cursor() as cursor:\\n # Using the sql statements to modify the tables\\n cursor.execute('CREATE TABLE If NOT EXISTS student(id SERIAL PRIMARY KEY, name VARCHAR);')\\n # cursor.execute(\\\"INSERT INTO student(name) VALUES (%s);\\\", ('Jack Smith',))\\n # connection.commit()\\n cursor.execute('SELECT * FROM student;')\\n print(*cursor.fetchall(), sep='\\\\n')\\n\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\",\n \"def command_dbtool(self):\\n dbtool.main(*self.args())\",\n \"def startup(self):\\n self.load_up_initial_db(TIMESTAMP_PARSE_DICT)\\n self.add_numeric_cols()\",\n \"def initialize_database():\\n db = Database(database_name)\\n i, m, u, p = db.fetch_needed_data()\\n\\n return i, m, u, p\",\n \"def start ( r, w, args ):\\n sqlLoginInfo = parseArgs ( args )\\n sql = sqlLogin ( sqlLoginInfo )\\n xml = importXml ( r )\\n createTables ( sql )\\n importDB ( sql, xml )\\n exportXML = exportDB ( sql )\\n sql.close ()\\n exportXml ( w, exportXML )\",\n \"def load():\\r\\n global database\\r\\n global ranks\\r\\n \\r\\n osPlatform = (\\\"Windows\\\" if os.name == \\\"nt\\\" else \\\"Linux\\\" if os.name == \\\"posix\\\" else os.name)\\r\\n debug.write('Log file started at %s' % time.strftime(\\\"%A %d %B %Y - %H:%M:%S\\\"), 0, False)\\r\\n debug.write('\\\\n*******************************************************', 0, False)\\r\\n debug.write('[SourceRPG]: Turning your Server into a Role Playing Game', 0, False)\\r\\n debug.write('[SourceRPG]: Current Version - %s' % info.version, 0, False)\\r\\n debug.write('[SourceRPG]: Made by %s' % info.author, 0, False)\\r\\n debug.write('\\\\nSystem Info:', 0, False)\\r\\n debug.write('\\\\tOS: %s' % osPlatform, 0, False)\\r\\n debug.write('\\\\tEventscripts Version: %s' % es.ServerVar('eventscripts_ver'), 0, False)\\r\\n debug.write('\\\\tCorelib Version: %s' % es.ServerVar('es_corelib_ver'), 0, False)\\r\\n debug.write('\\\\tEventscript Tools Version: %s' % es.ServerVar('est_version'), 0, False)\\r\\n debug.write('\\\\tEventscripts Noisy: %s' % es.ServerVar('eventscripts_noisy'), 0, False)\\r\\n debug.write('\\\\tPopuplib version: %s' % popuplib.info.version, 0, False) \\r\\n \\r\\n cmdlib.registerSayCommand(\\\"rpgmenu\\\", sayCommands.mainMenu, \\\"Opens the rpg main menu\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpgupgrade\\\", sayCommands.upgradeMenu, \\\"Opens the upgrade menu\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpgsell\\\", sayCommands.sellMenu, \\\"Opens the sell menu\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpghelp\\\", sayCommands.helpMenu, \\\"Opens the help menu\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpgstats\\\", sayCommands.stats, \\\"Opens the stats menu for the user or another player\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpgrank\\\", sayCommands.rank, \\\"Tells the player their rank or another player's rank\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpgpopup\\\", sayCommands.togglePopup, \\\"Tells the player their rank or another player's rank\\\")\\r\\n cmdlib.registerSayCommand(\\\"rpgtop10\\\", sayCommands.top10, \\\"Sends the player the last updated top 10 scores\\\")\\r\\n \\r\\n es.server.cmd(\\\"exec sourcerpg/skill_loader.cfg\\\")\\r\\n \\r\\n es.server.cmd(\\\"exec sourcerpg/addon_loader.cfg\\\")\\r\\n \\r\\n skillConfig.write(True)\\r\\n skillConfig.execute(True, True)\\r\\n\\r\\n debug.write('[SourceRPG] Starting the popup creation', 0, False)\\r\\n\\r\\n \\\"\\\"\\\" Create the default popups which aren't unique to players \\\"\\\"\\\"\\r\\n rpgmenu = popuplib.easymenu(\\\"sourcerpg_rpgmenu\\\", \\\"_popup_choice\\\", popups.rpgmenu)\\r\\n rpgmenu.settitle(\\\"=== %s Menu ===\\\" % prefix)\\r\\n rpgmenu.addoption(1, \\\"Upgrade Skills\\\")\\r\\n rpgmenu.addoption(2, \\\"Sell Skills\\\")\\r\\n rpgmenu.addoption(3, \\\"RPG Help\\\")\\r\\n rpgmenu.addoption(4, \\\"RPG Stats\\\")\\r\\n rpgmenu.addoption(5, \\\"Reset Skills\\\")\\r\\n \\r\\n helpMenu = popuplib.easymenu('sourcerpg_help', '_popup_choice', popups.helpmenu)\\r\\n helpMenu.settitle('=== %s Help ===' % prefix)\\r\\n helpMenu.addoption(1, 'About SourceRPG')\\r\\n helpMenu.addoption(2, 'List of Commands')\\r\\n helpMenu.addoption(3, 'About SourceRPG Skills')\\r\\n helpMenu.addoption(4, 'Credit')\\r\\n helpMenu.submenu(10, \\\"sourcerpg_rpgmenu\\\")\\r\\n \\r\\n confirmation = popuplib.easymenu('sourcerpg_confirm', '_popup_choice', popups.confirm)\\r\\n confirmation.settitle(\\\"=== %s Reset Stats ===\\\" % prefix)\\r\\n confirmation.setdescription(\\\"\\\"\\\"Are you sure you want to remove\\r\\nyour skills? There is no chance\\r\\nor recovering them again!\\\"\\\"\\\")\\r\\n confirmation.addoption(True, \\\"Yes\\\")\\r\\n confirmation.addoption(False, \\\"No\\\")\\r\\n \\r\\n about = popuplib.create('sourcerpg_about')\\r\\n about.addline('=== About %s ===' % prefix)\\r\\n about.addline('-' * 30)\\r\\n about.addline('SourceRPG is a python coded mod')\\r\\n about.addline('for EventScripts 2+. It enables')\\r\\n about.addline('players to gain Levels, by gaining')\\r\\n about.addline('XP from certain events, such as')\\r\\n about.addline('planting the bomb, or killing')\\r\\n about.addline('another player. Each level gives')\\r\\n about.addline('%s Credits, which allows you to' % creditsReceived)\\r\\n about.addline('buy certain skills which aid you')\\r\\n about.addline('in killing other players.')\\r\\n about.addline('-' * 30)\\r\\n about.addline('->8. Back')\\r\\n about.addline('0. Cancel')\\r\\n about.submenu(8, 'sourcerpg_help')\\r\\n \\r\\n commandspopup = popuplib.create('sourcerpg_commands')\\r\\n commandspopup.addline(\\\"=== %s Commands ===\\\" % prefix)\\r\\n commandspopup.addline(\\\"-\\\" * 30)\\r\\n commandspopup.addline(\\\"rpghelp - displays the help menu\\\")\\r\\n commandspopup.addline(\\\"rpgmenu - displays the main menu\\\")\\r\\n commandspopup.addline(\\\"rpgrank - displays your RPG rank\\\")\\r\\n commandspopup.addline(\\\"rpgpopup - toggles on / off automatic popup display\\\")\\r\\n commandspopup.addline(\\\"rpgupgrade - upgrade skills\\\")\\r\\n commandspopup.addline(\\\"rpgsell - sell skills\\\")\\r\\n commandspopup.addline(\\\"rpgstats - display your stats\\\")\\r\\n commandspopup.addline(\\\"-\\\" * 30)\\r\\n commandspopup.addline(\\\"->8. Back\\\")\\r\\n commandspopup.addline(\\\"0. Cancel\\\")\\r\\n commandspopup.submenu(8, 'sourcerpg_help')\\r\\n \\r\\n creditmenu = popuplib.create('sourcerpg_creditmenu') \\r\\n creditmenu.addline('=== %s Credits ===' % prefix)\\r\\n creditmenu.addline('-' * 30)\\r\\n creditmenu.addline(info.author)\\r\\n creditmenu.addline(' Script Creator')\\r\\n creditmenu.addline(' ')\\r\\n creditmenu.addline('SumGuy14 and Murphey')\\r\\n creditmenu.addline(' Letting me use their Long Jump code')\\r\\n creditmenu.addline(' ')\\r\\n creditmenu.addline('SuperDave')\\r\\n creditmenu.addline(' He turned my failing SmogNade code into')\\r\\n creditmenu.addline(' a working code! Thank him for that skill.')\\r\\n creditmenu.addline(' ')\\r\\n creditmenu.addline('JoeyT2008 (Jordan Thomas)')\\r\\n creditmenu.addline(' Awesome scripter who made the database conversion')\\r\\n creditmenu.addline(' ')\\r\\n creditmenu.addline('EventScripts Community')\\r\\n creditmenu.addline(' Help and support, and such a good plugin.')\\r\\n creditmenu.addline('-' * 30)\\r\\n creditmenu.addline('8. Back')\\r\\n creditmenu.addline('0. Cancel')\\r\\n creditmenu.submenu(8, 'sourcerpg_help')\\r\\n \\r\\n debug.write('[SourceRPG] Popups created', 0, False)\\r\\n \\r\\n \\r\\n if int(turboMode):\\r\\n database = DATABASE_STORAGE_METHOD(\\\":memory:\\\")\\r\\n else:\\r\\n database = DATABASE_STORAGE_METHOD(databasePath)\\r\\n \\r\\n ranks = RankManager()\\r\\n \\r\\n \\\"\\\"\\\" If the script is loaded late then make sure all players are inserted \\\"\\\"\\\"\\r\\n if es.getplayercount():\\r\\n for player in es.getUseridList():\\r\\n players.addPlayer( player )\\r\\n \\r\\n es.server.queuecmd('mp_restartgame 1')\\r\\n\\r\\n if str( es.ServerVar('eventscripts_currentmap')):\\r\\n es_map_start({})\\r\\n\\r\\n \\\"\\\"\\\" If we want to save by intervals then create a repeat to save the database \\\"\\\"\\\"\\r\\n if str( saveType ) == \\\"intervals\\\":\\r\\n gamethread.delayedname(float(saveLength), 'sourcerpg_databasesave', saveDatabase)\\r\\n \\r\\n debug.write('[SourceRPG]: Finished Loading... Enjoy your stay!', 0, False)\\r\\n debug.write('*******************************************************\\\\n', 0, False)\",\n \"def initialize_database():\\n # TODO: Refactor the funtime library\\n this.db = Store(this.host).create_lib(this.store_name).get_store()\",\n \"def load_database(db_session, fixture):\\n # TODO: the fixture file path controls\\n\\n # load the fixture\\n datas = pickle.loads(fixture)\\n db_session.add_all(datas)\\n db_session.commit()\\n print \\\"load database ok\\\"\",\n \"def setup_database() -> sqlite3.Cursor:\\n conn = sqlite3.connect(':memory:')\\n cursor = conn.cursor()\\n\\n insert_books(cursor)\\n insert_lookups(cursor)\\n\\n return cursor\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n hostname = get_hostname(config)\\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(\\n hostname,\\n config.get(\\\"CLUSTER\\\", \\\"DB_NAME\\\"),\\n config.get(\\\"CLUSTER\\\", \\\"DB_USER\\\"),\\n config.get(\\\"CLUSTER\\\", \\\"DB_PASSWORD\\\"),\\n config.get(\\\"CLUSTER\\\", \\\"DB_PORT\\\"))\\n )\\n\\n cur = conn.cursor()\\n drop_tables(cur, conn)\\n create_tables(cur, conn)\\n\\n conn.close()\",\n \"def init_db(app, test_data=False, load_init_data=True):\\r\\n db.app = app\\r\\n db.init_app(app)\\r\\n db.create_all()\",\n \"def add_data_db_command():\\n click.echo(\\\"Adding data to the database.\\\")\\n add_data_db()\\n click.echo(\\\"Finished adding data to the database.\\\")\",\n \"def main():\\n args = parse_args()\\n\\n make_session = bootstrap(\\n 'sqlite:///{}'.format(DB_NAME),\\n )\\n session = make_session()\\n\\n for vase in session.query(Vase).order_by(Vase.produced_start):\\n for query_str in make_searches(vase):\\n pass\",\n \"def main():\\n os.makedirs(PATH)\\n fetch_data()\\n convert_to_json(model_list, 'models.json', is_model=True)\\n convert_to_json(backend_list, 'backends.json')\\n convert_to_json(type_list, 'types.json')\\n convert_to_json(featurizer_list, 'featurizers.json')\",\n \"def load_fixtures(self, dbname, table, data):\\n db = self.databases[dbname]['db']\\n db.execute('BEGIN')\\n for row in data:\\n columns = row.keys()\\n q = db.Insert(table, cols=columns)\\n db.execute(q, row)\\n db.execute('COMMIT')\",\n \"def run(args):\\n print(\\\"matdb Copyright (C) 2019 HALL LABS\\\")\\n print(\\\"This program comes with ABSOLUTELY NO WARRANTY.\\\")\\n print(\\\"This is free software, and you are welcome to redistribute it under \\\"\\n \\\"certain conditions.\\\")\\n if args is None:\\n return\\n\\n #No matter what other options the user has chosen, we will have to create a\\n #database controller for the specification they have given us.\\n from matdb.database import Controller\\n cdb = Controller(args[\\\"dbspec\\\"])\\n\\n if args[\\\"d\\\"]:\\n _generic_find(cdb, \\\"Database Context Instances\\\", args[\\\"p\\\"])\\n if args[\\\"t\\\"]:\\n _generic_find(cdb.trainers, \\\"Fitter Context Instances\\\", args[\\\"p\\\"])\",\n \"def read_database():\\n file = tables.open_file(glob.datafile)\\n table_d = file.root.VelibData.dynamic\\n table_s = file.root.VelibData.static\\n n_rows = len(table_d)\\n print \\\"Nrows in dynamic table:\\\", n_rows\\n print \\\"N stations:\\\", len(table_d[0][\\\"last_update\\\"])\\n print \\\"Time of most recent sampling:\\\", \\\\\\n time.asctime(time.localtime(recover_time(table_d[-1][\\\"sample_time\\\"])))\\n print \\\"Nbikes available at most recent sampling:\\\", \\\\\\n table_d[n_rows-1][\\\"available_bikes\\\"]\\n print \\\"Time of last_update at most recent sampling:\\\", \\\\\\n time.asctime(\\n time.localtime(recover_time(table_d[n_rows-1][\\\"last_update\\\"][0])))\\n print \\\"Number arr\\\", table_s[0][\\\"number\\\"]\\n file.close()\",\n \"def initdb_command():\\n db.drop_all()\\n db.create_all()\\n if LOAD_DUMMY_DATA:\\n setup_dummy_data()\\n\\n print('Initialized the database.')\",\n \"def load_drugs():\\n\\n print(\\\"Drugs\\\")\\n\\n Drug.query.delete()\\n\\n with open(\\\"seed_data/drug_seed.psv\\\") as drugs:\\n for row in drugs:\\n name = row.strip()\\n\\n drug = Drug(generic_name=name)\\n\\n db.session.add(drug)\\n\\n db.session.commit()\",\n \"def initialize():\\n\\n db.connect() # Se conecta\\n db.create_tables([Entry], safe=True) # Crea las tablas\\n # safe=true evita crear modelos ya creados\",\n \"def main():\\n\\n from argparse import ArgumentParser\\n parser = ArgumentParser()\\n parser.add_argument('messages_filepath', help='The location of the csv file containing your messages.')\\n parser.add_argument('categories_filepath', help='The location of the csv file containing your message categories.')\\n parser.add_argument('database_filepath', help='The location to which you wish to save the cleaned messages database file.')\\n \\n # parse args or raise expection on failure\\n try:\\n args = parser.parse_args()\\n except:\\n print('Please provide the filepaths of the messages and categories '\\\\\\n 'datasets as the first and second argument respectively, as '\\\\\\n 'well as the filepath of the database to save the cleaned data '\\\\\\n 'to as the third argument. \\\\n\\\\nExample: python process_data.py '\\\\\\n 'disaster_messages.csv disaster_categories.csv '\\\\\\n 'DisasterResponse.db')\\n raise\\n\\n # assign command line args to vars\\n messages_filepath = args.messages_filepath\\n categories_filepath = args.categories_filepath\\n database_filepath = args.database_filepath\\n\\n print('Loading data...\\\\n MESSAGES: {}\\\\n CATEGORIES: {}'\\n .format(messages_filepath, categories_filepath))\\n df = load_data(messages_filepath, categories_filepath)\\n\\n print('Cleaning data...')\\n df = clean_data(df)\\n \\n print('Saving data...\\\\n DATABASE: {}'.format(database_filepath))\\n save_data(df, database_filepath)\\n \\n print('Cleaned data saved to database!')\",\n \"def insert_db():\\n populate_tables()\",\n \"def setUpTestData(cls):\\n call_command('loaddata', 'db.json', verbosity=0)\",\n \"def main():\\n\\n dbase = sqlite3.connect('database.db')\\n with dbase:\\n cursor = dbase.cursor()\\n try:\\n cursor.execute(\\\"DROP TABLE Graph;\\\")\\n except sqlite3.OperationalError:\\n pass\\n\\n try:\\n cursor.execute(\\\"CREATE TABLE Graph (name TEXT, json TEXT);\\\")\\n except sqlite3.OperationalError:\\n pass\\n\\n for i, item in enumerate(SOME_JSONS):\\n cursor.execute(\\\"insert into Graph (name, json) values\\\"\\n \\\"(?, ?);\\\",\\n ('example_' + str(i + 1), json.dumps(item)))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7243006","0.69598734","0.6931684","0.68877065","0.68784547","0.6848689","0.6814924","0.6806881","0.67778087","0.6770427","0.6755996","0.6755282","0.6713464","0.6702432","0.6670045","0.6666217","0.6662877","0.6647903","0.6627729","0.6620025","0.6592809","0.6570457","0.65518343","0.6550008","0.6536957","0.6536753","0.6533149","0.6525545","0.6493914","0.6464384","0.645183","0.6428631","0.64223146","0.6417683","0.6415037","0.6411355","0.6383784","0.6380349","0.6368073","0.63644135","0.6363437","0.6360109","0.63436794","0.63436794","0.63430715","0.6336994","0.6331351","0.63312525","0.62962747","0.6291371","0.6290989","0.6286776","0.62863845","0.6281653","0.62793446","0.627137","0.62687206","0.62635386","0.6263296","0.62295294","0.6222218","0.6221942","0.6221171","0.6219531","0.62030977","0.61967635","0.61814356","0.61781895","0.61714315","0.6166489","0.614876","0.61459416","0.61445713","0.61302006","0.61296445","0.6128509","0.61134547","0.6105852","0.61027","0.61023414","0.6102017","0.60936505","0.60689104","0.605616","0.6053607","0.6048622","0.60459816","0.6042767","0.6041586","0.6031737","0.6019871","0.6016102","0.601372","0.6009654","0.600901","0.6007375","0.6005807","0.6002795","0.5997123","0.5996953","0.5993135"],"string":"[\n \"0.7243006\",\n \"0.69598734\",\n \"0.6931684\",\n \"0.68877065\",\n \"0.68784547\",\n \"0.6848689\",\n \"0.6814924\",\n \"0.6806881\",\n \"0.67778087\",\n \"0.6770427\",\n \"0.6755996\",\n \"0.6755282\",\n \"0.6713464\",\n \"0.6702432\",\n \"0.6670045\",\n \"0.6666217\",\n \"0.6662877\",\n \"0.6647903\",\n \"0.6627729\",\n \"0.6620025\",\n \"0.6592809\",\n \"0.6570457\",\n \"0.65518343\",\n \"0.6550008\",\n \"0.6536957\",\n \"0.6536753\",\n \"0.6533149\",\n \"0.6525545\",\n \"0.6493914\",\n \"0.6464384\",\n \"0.645183\",\n \"0.6428631\",\n \"0.64223146\",\n \"0.6417683\",\n \"0.6415037\",\n \"0.6411355\",\n \"0.6383784\",\n \"0.6380349\",\n \"0.6368073\",\n \"0.63644135\",\n \"0.6363437\",\n \"0.6360109\",\n \"0.63436794\",\n \"0.63436794\",\n \"0.63430715\",\n \"0.6336994\",\n \"0.6331351\",\n \"0.63312525\",\n \"0.62962747\",\n \"0.6291371\",\n \"0.6290989\",\n \"0.6286776\",\n \"0.62863845\",\n \"0.6281653\",\n \"0.62793446\",\n \"0.627137\",\n \"0.62687206\",\n \"0.62635386\",\n \"0.6263296\",\n \"0.62295294\",\n \"0.6222218\",\n \"0.6221942\",\n \"0.6221171\",\n \"0.6219531\",\n \"0.62030977\",\n \"0.61967635\",\n \"0.61814356\",\n \"0.61781895\",\n \"0.61714315\",\n \"0.6166489\",\n \"0.614876\",\n \"0.61459416\",\n \"0.61445713\",\n \"0.61302006\",\n \"0.61296445\",\n \"0.6128509\",\n \"0.61134547\",\n \"0.6105852\",\n \"0.61027\",\n \"0.61023414\",\n \"0.6102017\",\n \"0.60936505\",\n \"0.60689104\",\n \"0.605616\",\n \"0.6053607\",\n \"0.6048622\",\n \"0.60459816\",\n \"0.6042767\",\n \"0.6041586\",\n \"0.6031737\",\n \"0.6019871\",\n \"0.6016102\",\n \"0.601372\",\n \"0.6009654\",\n \"0.600901\",\n \"0.6007375\",\n \"0.6005807\",\n \"0.6002795\",\n \"0.5997123\",\n \"0.5996953\",\n \"0.5993135\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11595,"cells":{"query":{"kind":"string","value":"This function returns the softmax value for the given input"},"document":{"kind":"string","value":"def softmax(X):\n if len(X) > 1:\n expo = np.exp(X)\n expo_sum = np.sum(np.exp(X))\n ret = expo/expo_sum\n print(f'The softmax result on the given input {ret}')\n return ret\n else:\n raise ValueError(\"Sorry, lenght of the list should be more than one\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def softmax(x):\n #pass # TODO: Compute and return softmax(x)\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n \"\"\"\"\"\"\n return exp(x) / sum(exp(x), axis=0)","def softmax(x): \n e_x = np.exp(x - np.max(x)) \n return e_x / e_x.sum()","def softmax(x):\n pass # TODO: Compute and return softmax(x)\n\n exp_x = np.exp(x)\n sum_x = np.sum(exp_x, axis=0)\n softmax = exp_x/sum_x\n \n return softmax","def softmax(x):\n x_exp = (x - x.max(1)[0].view(-1, 1)).exp()\n return x_exp / x_exp.sum(1).view(-1, 1)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n # https://stackoverflow.com/questions/34968722/softmax-function-python\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def my_softmax(x):\n x = x - np.max(x)\n exp_x = np.exp(x)\n softmax_x = exp_x / np.sum(exp_x)\n return softmax_x","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum()","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum()","def _softmax(self,x):\n e_x = np.exp(x - np.max(x))\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))","def softmax(x):\n npX = np.array(x)\n expX = np.exp(x)\n\n return expX/sum(expX)","def softmax(x):\n\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n x_exp = np.exp(x)\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n s = x_exp / x_sum\n \n return s","def softmax(x): \n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\r\n output = np.exp(x)\r\n return output / np.sum(output, axis=1, keepdims=True)","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)\n # return ( x / np.sum(x, axis=0) )","def softmax(inputs):\n return np.exp(inputs) / float(sum(np.exp(inputs)))","def softmax(inputs):\n return np.exp(inputs) / float(sum(np.exp(inputs)))","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\r\n exps = np.exp(x)\r\n return exps / np.sum(exps)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=1)","def softmax(x):\r\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\r\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\r","def softmax(x):\n\n x_exp = np.exp(x)\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n s = x_exp / x_sum\n\n return s","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(X):\n num = np.exp(X)\n den = np.sum(np.exp(X))\n return num / den","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\r\n sum_c = np.sum(np.exp(x), axis=1)\r\n sum_c = np.expand_dims(sum_c, axis=1)\r\n pred_x = np.divide(np.exp(x), sum_c)\r\n return pred_x","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1)","def _softmax(self, x):\n return np.exp(x - np.max(x)) / np.sum(np.exp(x - np.max(x)))","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return old_div(e_x, e_x.sum())","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)","def softmax(x):\n scoreMatExp = np.exp(np.asarray(x))\n return scoreMatExp / scoreMatExp.sum(0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax_my(x):\r\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp((x.transpose()-x.max(axis=1)).transpose())\n return e_x / np.sum(e_x,axis=1)[:,None]","def softmax(self, x):\n x_max = np.max(x, axis=-1, keepdims=True)\n e_x = np.exp(x - x_max)\n return e_x / e_x.sum(axis=-1, keepdims=True)","def softmax(x):\n e_yi = np.exp(x)\n suma_e_yj = sum(e_yi)\n soft = e_yi/suma_e_yj\n return soft","def softmax(self, x):\n if x.ndim == 1:\n x = x.reshape((1, -1))\n max_x = np.max(x, axis=1).reshape((-1, 1))\n exp_x = np.exp(x - max_x)\n return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum(axis=0) # only difference\r","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))\n return e_x / np.sum(e_x, axis=1).reshape(-1, 1)","def softmax(x):\n num = np.exp(x)\n den = np.sum(np.exp(x), axis=1)\n output = (num.T / den).T\n return output","def softmax(x):\n exps = np.exp(x - np.max(x, axis=0))\n return exps / exps.sum(axis=0)","def softmax(x):\n x_exp = np.exp(x)\n\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n\n s = x_exp/x_sum\n\n return s","def softmax(x):\n e_x = np.exp(x - np.max(x))\n temp=e_x / e_x.sum(axis=0) # only difference\n \n if np.isnan(temp).any()==True:\n return [0.0,1.0,0.0]\n else:\n return temp","def _softmax(x):\n e = K.exp(x - K.max(x, axis=-1, keepdims=True))\n s = K.sum(e, axis=-1, keepdims=True)\n return e / s","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def softmax(x):\n shape = x.shape\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\n return probs","def softmax(self, x):\n e_x = np.exp(x)\n return e_x / e_x.sum(axis=1, keepdims=True) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n x = e_x / e_x.sum()\n # Your code should be fast, so use a vectorized implementation using numpy,\n # don't use any loops.\n # With a vectorized implementation, the code should be no more than 2 lines.\n #\n # For numeric stability, use the identify you proved in Ex 2 Q1.\n return x","def softmax(x: npt.NDArray) -> npt.NDArray:\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\n exp_x = np.exp(x - row_wise_max)\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)","def softmax(x):\n \"\"\" Meaning: avoid very large exponents by reducing the largest to zero\n and everything else to less than that, so they go to zero instead of infinity an Nan\"\"\"\n ex = np.exp(x - np.max(x))\n return ex / ex.sum(axis=1, keepdims = True)","def softmax(X):\n _X = X - np.max(X, axis=1).reshape(-1, 1)\n ep = np.exp(_X)\n return ep / np.sum(ep, axis=1).reshape(-1, 1)","def softmax(x):\n xx = x\n x = x.reshape((-1, x.shape[-1]))\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\n return res.reshape(xx.shape)","def stablesoftmax(x):\r\n shiftx = x - np.max(x)\r\n exps = np.exp(shiftx)\r\n return exps / np.sum(exps)","def softmax(x):\n e_x = np.exp(x)# - np.max(x))\n print(e_x.sum())\n print(e_x)\n print(e_x / e_x.sum())\n return np.log(e_x / e_x.sum())","def softmax(input):\n list_value = []\n len_compute = input.shape[-1]\n shape_input = input.shape\n for x in input.reshape(-1, len_compute):\n # print(x)\n e_x = np.exp(x - np.max(x))\n res = e_x / e_x.sum(axis=0)\n list_value.append(res)\n\n return np.array(list_value).reshape(shape_input)","def softmax(x):\n max_val = np.amax(x)\n numerator = np.exp(x-max_val)\n denominator = np.sum(numerator, axis=1).reshape((numerator.shape[0],1))\n return numerator/denominator\n\n #raise NotImplementedError(\"Softmax not implemented\")","def softmax(self, x):\n \"\"\" Meaning: avoid very large exponents by reducing the largest to zero\n and everything else to less than that\"\"\"\n ex = np.exp(x - np.max(x))\n return ex / np.sum(ex, axis = 1, keepdims=True) #Sums over rows np.sum([[0, 1], [0, 5]], axis=1) = array([1, 5])"],"string":"[\n \"def softmax(x):\\n #pass # TODO: Compute and return softmax(x)\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n return exp(x) / sum(exp(x), axis=0)\",\n \"def softmax(x): \\n e_x = np.exp(x - np.max(x)) \\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n pass # TODO: Compute and return softmax(x)\\n\\n exp_x = np.exp(x)\\n sum_x = np.sum(exp_x, axis=0)\\n softmax = exp_x/sum_x\\n \\n return softmax\",\n \"def softmax(x):\\n x_exp = (x - x.max(1)[0].view(-1, 1)).exp()\\n return x_exp / x_exp.sum(1).view(-1, 1)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n # https://stackoverflow.com/questions/34968722/softmax-function-python\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def my_softmax(x):\\n x = x - np.max(x)\\n exp_x = np.exp(x)\\n softmax_x = exp_x / np.sum(exp_x)\\n return softmax_x\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum()\",\n \"def _softmax(self,x):\\n e_x = np.exp(x - np.max(x))\\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))\",\n \"def softmax(x):\\n npX = np.array(x)\\n expX = np.exp(x)\\n\\n return expX/sum(expX)\",\n \"def softmax(x):\\n\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n x_exp = np.exp(x)\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n s = x_exp / x_sum\\n \\n return s\",\n \"def softmax(x): \\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\r\\n output = np.exp(x)\\r\\n return output / np.sum(output, axis=1, keepdims=True)\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\\n # return ( x / np.sum(x, axis=0) )\",\n \"def softmax(inputs):\\n return np.exp(inputs) / float(sum(np.exp(inputs)))\",\n \"def softmax(inputs):\\n return np.exp(inputs) / float(sum(np.exp(inputs)))\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\r\\n exps = np.exp(x)\\r\\n return exps / np.sum(exps)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=1)\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\\r\\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\\r\",\n \"def softmax(x):\\n\\n x_exp = np.exp(x)\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n s = x_exp / x_sum\\n\\n return s\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(X):\\n num = np.exp(X)\\n den = np.sum(np.exp(X))\\n return num / den\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\r\\n sum_c = np.sum(np.exp(x), axis=1)\\r\\n sum_c = np.expand_dims(sum_c, axis=1)\\r\\n pred_x = np.divide(np.exp(x), sum_c)\\r\\n return pred_x\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1)\",\n \"def _softmax(self, x):\\n return np.exp(x - np.max(x)) / np.sum(np.exp(x - np.max(x)))\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return old_div(e_x, e_x.sum())\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)\",\n \"def softmax(x):\\n scoreMatExp = np.exp(np.asarray(x))\\n return scoreMatExp / scoreMatExp.sum(0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax_my(x):\\r\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp((x.transpose()-x.max(axis=1)).transpose())\\n return e_x / np.sum(e_x,axis=1)[:,None]\",\n \"def softmax(self, x):\\n x_max = np.max(x, axis=-1, keepdims=True)\\n e_x = np.exp(x - x_max)\\n return e_x / e_x.sum(axis=-1, keepdims=True)\",\n \"def softmax(x):\\n e_yi = np.exp(x)\\n suma_e_yj = sum(e_yi)\\n soft = e_yi/suma_e_yj\\n return soft\",\n \"def softmax(self, x):\\n if x.ndim == 1:\\n x = x.reshape((1, -1))\\n max_x = np.max(x, axis=1).reshape((-1, 1))\\n exp_x = np.exp(x - max_x)\\n return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum(axis=0) # only difference\\r\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))\\n return e_x / np.sum(e_x, axis=1).reshape(-1, 1)\",\n \"def softmax(x):\\n num = np.exp(x)\\n den = np.sum(np.exp(x), axis=1)\\n output = (num.T / den).T\\n return output\",\n \"def softmax(x):\\n exps = np.exp(x - np.max(x, axis=0))\\n return exps / exps.sum(axis=0)\",\n \"def softmax(x):\\n x_exp = np.exp(x)\\n\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n\\n s = x_exp/x_sum\\n\\n return s\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n temp=e_x / e_x.sum(axis=0) # only difference\\n \\n if np.isnan(temp).any()==True:\\n return [0.0,1.0,0.0]\\n else:\\n return temp\",\n \"def _softmax(x):\\n e = K.exp(x - K.max(x, axis=-1, keepdims=True))\\n s = K.sum(e, axis=-1, keepdims=True)\\n return e / s\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def softmax(x):\\n shape = x.shape\\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\\n return probs\",\n \"def softmax(self, x):\\n e_x = np.exp(x)\\n return e_x / e_x.sum(axis=1, keepdims=True) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n x = e_x / e_x.sum()\\n # Your code should be fast, so use a vectorized implementation using numpy,\\n # don't use any loops.\\n # With a vectorized implementation, the code should be no more than 2 lines.\\n #\\n # For numeric stability, use the identify you proved in Ex 2 Q1.\\n return x\",\n \"def softmax(x: npt.NDArray) -> npt.NDArray:\\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\\n exp_x = np.exp(x - row_wise_max)\\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)\",\n \"def softmax(x):\\n \\\"\\\"\\\" Meaning: avoid very large exponents by reducing the largest to zero\\n and everything else to less than that, so they go to zero instead of infinity an Nan\\\"\\\"\\\"\\n ex = np.exp(x - np.max(x))\\n return ex / ex.sum(axis=1, keepdims = True)\",\n \"def softmax(X):\\n _X = X - np.max(X, axis=1).reshape(-1, 1)\\n ep = np.exp(_X)\\n return ep / np.sum(ep, axis=1).reshape(-1, 1)\",\n \"def softmax(x):\\n xx = x\\n x = x.reshape((-1, x.shape[-1]))\\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\\n return res.reshape(xx.shape)\",\n \"def stablesoftmax(x):\\r\\n shiftx = x - np.max(x)\\r\\n exps = np.exp(shiftx)\\r\\n return exps / np.sum(exps)\",\n \"def softmax(x):\\n e_x = np.exp(x)# - np.max(x))\\n print(e_x.sum())\\n print(e_x)\\n print(e_x / e_x.sum())\\n return np.log(e_x / e_x.sum())\",\n \"def softmax(input):\\n list_value = []\\n len_compute = input.shape[-1]\\n shape_input = input.shape\\n for x in input.reshape(-1, len_compute):\\n # print(x)\\n e_x = np.exp(x - np.max(x))\\n res = e_x / e_x.sum(axis=0)\\n list_value.append(res)\\n\\n return np.array(list_value).reshape(shape_input)\",\n \"def softmax(x):\\n max_val = np.amax(x)\\n numerator = np.exp(x-max_val)\\n denominator = np.sum(numerator, axis=1).reshape((numerator.shape[0],1))\\n return numerator/denominator\\n\\n #raise NotImplementedError(\\\"Softmax not implemented\\\")\",\n \"def softmax(self, x):\\n \\\"\\\"\\\" Meaning: avoid very large exponents by reducing the largest to zero\\n and everything else to less than that\\\"\\\"\\\"\\n ex = np.exp(x - np.max(x))\\n return ex / np.sum(ex, axis = 1, keepdims=True) #Sums over rows np.sum([[0, 1], [0, 5]], axis=1) = array([1, 5])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85517555","0.85172","0.85039085","0.85021806","0.8486158","0.8471101","0.84642345","0.84226495","0.84178317","0.84178317","0.84136945","0.84076935","0.83944535","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.8392644","0.83897847","0.83752906","0.8365994","0.83634675","0.8357776","0.8357776","0.8356889","0.8354413","0.8354413","0.83538663","0.83399","0.83379465","0.83379465","0.83379465","0.83379465","0.83379465","0.8335344","0.8331758","0.83289814","0.8328141","0.8322312","0.8322312","0.8322312","0.8322312","0.8322312","0.8322312","0.8322312","0.8322312","0.8318206","0.8311299","0.8300747","0.8300599","0.8299869","0.8286783","0.8282569","0.8275875","0.8275557","0.82707405","0.82707405","0.82707405","0.82707405","0.826897","0.82652897","0.82627475","0.8258849","0.825673","0.8254562","0.82432204","0.824049","0.8238424","0.8217989","0.82102567","0.8206441","0.8200261","0.81579727","0.81579727","0.81488377","0.81279606","0.811834","0.8098703","0.8075605","0.8074609","0.80729926","0.8065096","0.80616236","0.8047404","0.80393916","0.80215424"],"string":"[\n \"0.85517555\",\n \"0.85172\",\n \"0.85039085\",\n \"0.85021806\",\n \"0.8486158\",\n \"0.8471101\",\n \"0.84642345\",\n \"0.84226495\",\n \"0.84178317\",\n \"0.84178317\",\n \"0.84136945\",\n \"0.84076935\",\n \"0.83944535\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.8392644\",\n \"0.83897847\",\n \"0.83752906\",\n \"0.8365994\",\n \"0.83634675\",\n \"0.8357776\",\n \"0.8357776\",\n \"0.8356889\",\n \"0.8354413\",\n \"0.8354413\",\n \"0.83538663\",\n \"0.83399\",\n \"0.83379465\",\n \"0.83379465\",\n \"0.83379465\",\n \"0.83379465\",\n \"0.83379465\",\n \"0.8335344\",\n \"0.8331758\",\n \"0.83289814\",\n \"0.8328141\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8322312\",\n \"0.8318206\",\n \"0.8311299\",\n \"0.8300747\",\n \"0.8300599\",\n \"0.8299869\",\n \"0.8286783\",\n \"0.8282569\",\n \"0.8275875\",\n \"0.8275557\",\n \"0.82707405\",\n \"0.82707405\",\n \"0.82707405\",\n \"0.82707405\",\n \"0.826897\",\n \"0.82652897\",\n \"0.82627475\",\n \"0.8258849\",\n \"0.825673\",\n \"0.8254562\",\n \"0.82432204\",\n \"0.824049\",\n \"0.8238424\",\n \"0.8217989\",\n \"0.82102567\",\n \"0.8206441\",\n \"0.8200261\",\n \"0.81579727\",\n \"0.81579727\",\n \"0.81488377\",\n \"0.81279606\",\n \"0.811834\",\n \"0.8098703\",\n \"0.8075605\",\n \"0.8074609\",\n \"0.80729926\",\n \"0.8065096\",\n \"0.80616236\",\n \"0.8047404\",\n \"0.80393916\",\n \"0.80215424\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11596,"cells":{"query":{"kind":"string","value":"This function returns the softmax derivative value for the given input"},"document":{"kind":"string","value":"def softmax_derivative(x):\n der = derivative(softmax,x,dx=1e-9)\n return der"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def softmax_derivative(Z):\n\treturn None","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum(axis=0) # only difference\r","def softmax(x):\r\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\r\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\r","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n \"\"\"\"\"\"\n return exp(x) / sum(exp(x), axis=0)","def softmax(x): \n e_x = np.exp(x - np.max(x)) \n return e_x / e_x.sum()","def softmax(x):\n #pass # TODO: Compute and return softmax(x)\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(X):\n num = np.exp(X)\n den = np.sum(np.exp(X))\n return num / den","def softmax(x):\n x_exp = (x - x.max(1)[0].view(-1, 1)).exp()\n return x_exp / x_exp.sum(1).view(-1, 1)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)\n # return ( x / np.sum(x, axis=0) )","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return old_div(e_x, e_x.sum())","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum()","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(x):\n pass # TODO: Compute and return softmax(x)\n\n exp_x = np.exp(x)\n sum_x = np.sum(exp_x, axis=0)\n softmax = exp_x/sum_x\n \n return softmax","def softmax_gradient(softmax_result):\r\n\r\n s = softmax_result.reshape(-1, 1)\r\n return np.diagflat(s) - np.dot(s, s.T)","def softmax(x):\n num = np.exp(x)\n den = np.sum(np.exp(x), axis=1)\n output = (num.T / den).T\n return output","def softmax(x):\n\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x): \n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n # https://stackoverflow.com/questions/34968722/softmax-function-python\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp((x.transpose()-x.max(axis=1)).transpose())\n return e_x / np.sum(e_x,axis=1)[:,None]","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=1)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n temp=e_x / e_x.sum(axis=0) # only difference\n \n if np.isnan(temp).any()==True:\n return [0.0,1.0,0.0]\n else:\n return temp","def softmax(x):\r\n exps = np.exp(x)\r\n return exps / np.sum(exps)","def softmax(x):\n npX = np.array(x)\n expX = np.exp(x)\n\n return expX/sum(expX)","def softmax(self, x):\n e_x = np.exp(x)\n return e_x / e_x.sum(axis=1, keepdims=True) # only difference","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(X):\n _X = X - np.max(X, axis=1).reshape(-1, 1)\n ep = np.exp(_X)\n return ep / np.sum(ep, axis=1).reshape(-1, 1)","def _softmax(self,x):\n e_x = np.exp(x - np.max(x))\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))","def softmax(inputs):\n return np.exp(inputs) / float(sum(np.exp(inputs)))","def softmax(inputs):\n return np.exp(inputs) / float(sum(np.exp(inputs)))","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1)","def softmax(x):\n e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))\n return e_x / np.sum(e_x, axis=1).reshape(-1, 1)","def softmax(self, x):\n x_max = np.max(x, axis=-1, keepdims=True)\n e_x = np.exp(x - x_max)\n return e_x / e_x.sum(axis=-1, keepdims=True)","def softmax(x):\r\n output = np.exp(x)\r\n return output / np.sum(output, axis=1, keepdims=True)","def softmax(x):\n max_val = np.amax(x)\n numerator = np.exp(x-max_val)\n denominator = np.sum(numerator, axis=1).reshape((numerator.shape[0],1))\n return numerator/denominator\n\n #raise NotImplementedError(\"Softmax not implemented\")","def softmax(x):\n e_yi = np.exp(x)\n suma_e_yj = sum(e_yi)\n soft = e_yi/suma_e_yj\n return soft","def softmax(x):\n x_exp = np.exp(x)\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n s = x_exp / x_sum\n \n return s","def softmax(self, x):\n if x.ndim == 1:\n x = x.reshape((1, -1))\n max_x = np.max(x, axis=1).reshape((-1, 1))\n exp_x = np.exp(x - max_x)\n return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))","def _softmax(self, x):\n return np.exp(x - np.max(x)) / np.sum(np.exp(x - np.max(x)))","def softmax(x):\n exps = np.exp(x - np.max(x, axis=0))\n return exps / exps.sum(axis=0)","def softmax(x):\n e_x = np.exp(x)# - np.max(x))\n print(e_x.sum())\n print(e_x)\n print(e_x / e_x.sum())\n return np.log(e_x / e_x.sum())","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)","def softmax_my(x):\r\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)","def my_softmax(x):\n x = x - np.max(x)\n exp_x = np.exp(x)\n softmax_x = exp_x / np.sum(exp_x)\n return softmax_x","def softmax(x):\n\n x_exp = np.exp(x)\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n s = x_exp / x_sum\n\n return s","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def softmax(x):\n e_x = np.exp(x - np.max(x))\n x = e_x / e_x.sum()\n # Your code should be fast, so use a vectorized implementation using numpy,\n # don't use any loops.\n # With a vectorized implementation, the code should be no more than 2 lines.\n #\n # For numeric stability, use the identify you proved in Ex 2 Q1.\n return x","def softmax(x):\n x_exp = np.exp(x)\n\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n\n s = x_exp/x_sum\n\n return s","def softmax(x):\n xx = x\n x = x.reshape((-1, x.shape[-1]))\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\n return res.reshape(xx.shape)","def softmax(x):\r\n sum_c = np.sum(np.exp(x), axis=1)\r\n sum_c = np.expand_dims(sum_c, axis=1)\r\n pred_x = np.divide(np.exp(x), sum_c)\r\n return pred_x","def softmax(self, x):\n\n out = np.zeros(x.shape)\n for i in range(x.shape[0]):\n max_x = x[i] - np.max(x[i])\n out[i] = np.exp(max_x) / np.sum(np.exp(max_x), axis=0)\n\n return out","def softmax(x):\n \"\"\" Meaning: avoid very large exponents by reducing the largest to zero\n and everything else to less than that, so they go to zero instead of infinity an Nan\"\"\"\n ex = np.exp(x - np.max(x))\n return ex / ex.sum(axis=1, keepdims = True)","def softmax(x):\n shape = x.shape\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\n return probs","def softmax(x: npt.NDArray) -> npt.NDArray:\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\n exp_x = np.exp(x - row_wise_max)\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)","def softmax(self, x):\n \"\"\" Meaning: avoid very large exponents by reducing the largest to zero\n and everything else to less than that\"\"\"\n ex = np.exp(x - np.max(x))\n return ex / np.sum(ex, axis = 1, keepdims=True) #Sums over rows np.sum([[0, 1], [0, 5]], axis=1) = array([1, 5])"],"string":"[\n \"def softmax_derivative(Z):\\n\\treturn None\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum(axis=0) # only difference\\r\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\\r\\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\\r\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n return exp(x) / sum(exp(x), axis=0)\",\n \"def softmax(x): \\n e_x = np.exp(x - np.max(x)) \\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n #pass # TODO: Compute and return softmax(x)\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(X):\\n num = np.exp(X)\\n den = np.sum(np.exp(X))\\n return num / den\",\n \"def softmax(x):\\n x_exp = (x - x.max(1)[0].view(-1, 1)).exp()\\n return x_exp / x_exp.sum(1).view(-1, 1)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\\n # return ( x / np.sum(x, axis=0) )\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return old_div(e_x, e_x.sum())\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(x):\\n pass # TODO: Compute and return softmax(x)\\n\\n exp_x = np.exp(x)\\n sum_x = np.sum(exp_x, axis=0)\\n softmax = exp_x/sum_x\\n \\n return softmax\",\n \"def softmax_gradient(softmax_result):\\r\\n\\r\\n s = softmax_result.reshape(-1, 1)\\r\\n return np.diagflat(s) - np.dot(s, s.T)\",\n \"def softmax(x):\\n num = np.exp(x)\\n den = np.sum(np.exp(x), axis=1)\\n output = (num.T / den).T\\n return output\",\n \"def softmax(x):\\n\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x): \\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n # https://stackoverflow.com/questions/34968722/softmax-function-python\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp((x.transpose()-x.max(axis=1)).transpose())\\n return e_x / np.sum(e_x,axis=1)[:,None]\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=1)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n temp=e_x / e_x.sum(axis=0) # only difference\\n \\n if np.isnan(temp).any()==True:\\n return [0.0,1.0,0.0]\\n else:\\n return temp\",\n \"def softmax(x):\\r\\n exps = np.exp(x)\\r\\n return exps / np.sum(exps)\",\n \"def softmax(x):\\n npX = np.array(x)\\n expX = np.exp(x)\\n\\n return expX/sum(expX)\",\n \"def softmax(self, x):\\n e_x = np.exp(x)\\n return e_x / e_x.sum(axis=1, keepdims=True) # only difference\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(X):\\n _X = X - np.max(X, axis=1).reshape(-1, 1)\\n ep = np.exp(_X)\\n return ep / np.sum(ep, axis=1).reshape(-1, 1)\",\n \"def _softmax(self,x):\\n e_x = np.exp(x - np.max(x))\\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))\",\n \"def softmax(inputs):\\n return np.exp(inputs) / float(sum(np.exp(inputs)))\",\n \"def softmax(inputs):\\n return np.exp(inputs) / float(sum(np.exp(inputs)))\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))\\n return e_x / np.sum(e_x, axis=1).reshape(-1, 1)\",\n \"def softmax(self, x):\\n x_max = np.max(x, axis=-1, keepdims=True)\\n e_x = np.exp(x - x_max)\\n return e_x / e_x.sum(axis=-1, keepdims=True)\",\n \"def softmax(x):\\r\\n output = np.exp(x)\\r\\n return output / np.sum(output, axis=1, keepdims=True)\",\n \"def softmax(x):\\n max_val = np.amax(x)\\n numerator = np.exp(x-max_val)\\n denominator = np.sum(numerator, axis=1).reshape((numerator.shape[0],1))\\n return numerator/denominator\\n\\n #raise NotImplementedError(\\\"Softmax not implemented\\\")\",\n \"def softmax(x):\\n e_yi = np.exp(x)\\n suma_e_yj = sum(e_yi)\\n soft = e_yi/suma_e_yj\\n return soft\",\n \"def softmax(x):\\n x_exp = np.exp(x)\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n s = x_exp / x_sum\\n \\n return s\",\n \"def softmax(self, x):\\n if x.ndim == 1:\\n x = x.reshape((1, -1))\\n max_x = np.max(x, axis=1).reshape((-1, 1))\\n exp_x = np.exp(x - max_x)\\n return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))\",\n \"def _softmax(self, x):\\n return np.exp(x - np.max(x)) / np.sum(np.exp(x - np.max(x)))\",\n \"def softmax(x):\\n exps = np.exp(x - np.max(x, axis=0))\\n return exps / exps.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x)# - np.max(x))\\n print(e_x.sum())\\n print(e_x)\\n print(e_x / e_x.sum())\\n return np.log(e_x / e_x.sum())\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)\",\n \"def softmax_my(x):\\r\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)\",\n \"def my_softmax(x):\\n x = x - np.max(x)\\n exp_x = np.exp(x)\\n softmax_x = exp_x / np.sum(exp_x)\\n return softmax_x\",\n \"def softmax(x):\\n\\n x_exp = np.exp(x)\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n s = x_exp / x_sum\\n\\n return s\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n x = e_x / e_x.sum()\\n # Your code should be fast, so use a vectorized implementation using numpy,\\n # don't use any loops.\\n # With a vectorized implementation, the code should be no more than 2 lines.\\n #\\n # For numeric stability, use the identify you proved in Ex 2 Q1.\\n return x\",\n \"def softmax(x):\\n x_exp = np.exp(x)\\n\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n\\n s = x_exp/x_sum\\n\\n return s\",\n \"def softmax(x):\\n xx = x\\n x = x.reshape((-1, x.shape[-1]))\\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\\n return res.reshape(xx.shape)\",\n \"def softmax(x):\\r\\n sum_c = np.sum(np.exp(x), axis=1)\\r\\n sum_c = np.expand_dims(sum_c, axis=1)\\r\\n pred_x = np.divide(np.exp(x), sum_c)\\r\\n return pred_x\",\n \"def softmax(self, x):\\n\\n out = np.zeros(x.shape)\\n for i in range(x.shape[0]):\\n max_x = x[i] - np.max(x[i])\\n out[i] = np.exp(max_x) / np.sum(np.exp(max_x), axis=0)\\n\\n return out\",\n \"def softmax(x):\\n \\\"\\\"\\\" Meaning: avoid very large exponents by reducing the largest to zero\\n and everything else to less than that, so they go to zero instead of infinity an Nan\\\"\\\"\\\"\\n ex = np.exp(x - np.max(x))\\n return ex / ex.sum(axis=1, keepdims = True)\",\n \"def softmax(x):\\n shape = x.shape\\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\\n return probs\",\n \"def softmax(x: npt.NDArray) -> npt.NDArray:\\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\\n exp_x = np.exp(x - row_wise_max)\\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)\",\n \"def softmax(self, x):\\n \\\"\\\"\\\" Meaning: avoid very large exponents by reducing the largest to zero\\n and everything else to less than that\\\"\\\"\\\"\\n ex = np.exp(x - np.max(x))\\n return ex / np.sum(ex, axis = 1, keepdims=True) #Sums over rows np.sum([[0, 1], [0, 5]], axis=1) = array([1, 5])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78823423","0.7861486","0.7818649","0.78063107","0.7801125","0.7801125","0.7801125","0.7801125","0.77747434","0.7771414","0.7755482","0.7754241","0.76962405","0.7687193","0.7682934","0.76776224","0.76399696","0.7632133","0.7632133","0.76276577","0.7626131","0.7624954","0.7624954","0.76234233","0.7621945","0.7616067","0.7615478","0.760707","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.759843","0.75928724","0.75928724","0.75928724","0.75928724","0.75928724","0.75876397","0.7586588","0.7584918","0.7580584","0.75711155","0.75585586","0.7558321","0.75490785","0.75488764","0.75488764","0.75488764","0.75488764","0.75488764","0.75488764","0.75488764","0.75488764","0.75431645","0.7537208","0.7532299","0.7532299","0.75237703","0.7514425","0.75137985","0.7484762","0.7478482","0.7477885","0.74767274","0.747595","0.7475125","0.74729675","0.7468099","0.74536616","0.7453428","0.74325246","0.74305123","0.7424964","0.74012166","0.74012166","0.7366469","0.7342287","0.7339124","0.7332307","0.73143166","0.731412","0.731208","0.72802097","0.7274535"],"string":"[\n \"0.78823423\",\n \"0.7861486\",\n \"0.7818649\",\n \"0.78063107\",\n \"0.7801125\",\n \"0.7801125\",\n \"0.7801125\",\n \"0.7801125\",\n \"0.77747434\",\n \"0.7771414\",\n \"0.7755482\",\n \"0.7754241\",\n \"0.76962405\",\n \"0.7687193\",\n \"0.7682934\",\n \"0.76776224\",\n \"0.76399696\",\n \"0.7632133\",\n \"0.7632133\",\n \"0.76276577\",\n \"0.7626131\",\n \"0.7624954\",\n \"0.7624954\",\n \"0.76234233\",\n \"0.7621945\",\n \"0.7616067\",\n \"0.7615478\",\n \"0.760707\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.759843\",\n \"0.75928724\",\n \"0.75928724\",\n \"0.75928724\",\n \"0.75928724\",\n \"0.75928724\",\n \"0.75876397\",\n \"0.7586588\",\n \"0.7584918\",\n \"0.7580584\",\n \"0.75711155\",\n \"0.75585586\",\n \"0.7558321\",\n \"0.75490785\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75488764\",\n \"0.75431645\",\n \"0.7537208\",\n \"0.7532299\",\n \"0.7532299\",\n \"0.75237703\",\n \"0.7514425\",\n \"0.75137985\",\n \"0.7484762\",\n \"0.7478482\",\n \"0.7477885\",\n \"0.74767274\",\n \"0.747595\",\n \"0.7475125\",\n \"0.74729675\",\n \"0.7468099\",\n \"0.74536616\",\n \"0.7453428\",\n \"0.74325246\",\n \"0.74305123\",\n \"0.7424964\",\n \"0.74012166\",\n \"0.74012166\",\n \"0.7366469\",\n \"0.7342287\",\n \"0.7339124\",\n \"0.7332307\",\n \"0.73143166\",\n \"0.731412\",\n \"0.731208\",\n \"0.72802097\",\n \"0.7274535\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8873611"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11597,"cells":{"query":{"kind":"string","value":"pos word id; i embedding dim id; d_model embedding dim"},"document":{"kind":"string","value":"def get_angles(pos, i, d_model):\n angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))\n return pos * angle_rates"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_model(allidx,MAX_LENGTH,onlyArg):\n wordidx, labelidx, featuresidx, extraidx=allidx\n posidx, neridx, depidx, distanceidx, chnkidx, wikineridx, dbpedianeridx, subneridx = featuresidx\n\n main_input = Input(shape=(MAX_LENGTH,), name='main_input', dtype='int32')\n inputNodes=[main_input]\n\n w2vmodel=\"../embeddings/Domain-Word2vec.model\"\n\n embedding_matrix,EMBEDDING_DIM,vocabulary_size=prepare.wv_embedded(wordidx,w2vmodel)\n \n x = Embedding(output_dim=EMBEDDING_DIM, weights=[embedding_matrix],input_dim=vocabulary_size, input_length=MAX_LENGTH, mask_zero=False)(main_input)\n numnode=int(EMBEDDING_DIM/2)\n\n # pos embedding\n inputNodes,pos_layer=layers.embedlayer(inputNodes,\"pos_input\",posidx,MAX_LENGTH)\n x=Concatenate()([x,pos_layer])\n numnode+=int(len(posidx)/2)\n\n # ner embedding\n inputNodes,ner_layer=layers.embedlayer(inputNodes,\"ner_input\",neridx,MAX_LENGTH)\n x=Concatenate()([x,ner_layer])\n numnode+=int(len(neridx)/2)\n\n inputNodes,wikiner_layer=layers.embedlayer(inputNodes,\"wikiner_input\",wikineridx,MAX_LENGTH)\n x=Concatenate()([x,wikiner_layer])\n numnode+=int(len(wikineridx)/2)\n\n inputNodes,dbpedianer_layer=layers.embedlayer(inputNodes,\"dbpedianer_input\",dbpedianeridx,MAX_LENGTH)\n x=Concatenate()([x,dbpedianer_layer])\n numnode+=int(len(dbpedianeridx)/2)\n\n # dep embedding\n inputNodes,dep0_layer=layers.embedlayer(inputNodes,\"dep0_input\",depidx,MAX_LENGTH)\n x=Concatenate()([x,dep0_layer])\n numnode+=int(len(depidx)/2)\n\n inputNodes,dep1_layer=layers.embedlayer(inputNodes,\"dep1_input\",depidx,MAX_LENGTH)\n x=Concatenate()([x,dep1_layer])\n numnode+=int(len(depidx)/2)\n\n inputNodes,dep2_layer=layers.embedlayer(inputNodes,\"dep2_input\",depidx,MAX_LENGTH)\n x=Concatenate()([x,dep2_layer])\n numnode+=int(len(depidx)/2)\n\n # chnk embedding\n inputNodes,lvl_layer=layers.embedlayer(inputNodes,\"lvl_input\",distanceidx,MAX_LENGTH)\n x=Concatenate()([x,lvl_layer])\n numnode+=int(len(distanceidx)/2)\n\n inputNodes,chnk_layer=layers.embedlayer(inputNodes,\"chnk_input\",chnkidx,MAX_LENGTH)\n x=Concatenate()([x,chnk_layer])\n numnode+=int(len(chnkidx)/2)\n\n # wikiclass embedding\n inputNodes,subner_layer=layers.embedlayer(inputNodes,\"subner_input\",subneridx,MAX_LENGTH)\n x=Concatenate()([x,subner_layer])\n numnode+=int(len(subneridx)/2)\n\n if onlyArg:\n neartrigger_input = Input(shape=(MAX_LENGTH,), name='neartrigger_input', dtype='int32')\n inputNodes.append(neartrigger_input)\n neartrigger_layer = Embedding(output_dim=EMBEDDING_DIM, weights=[embedding_matrix],input_dim=vocabulary_size, \\\n input_length=MAX_LENGTH, mask_zero=False)(neartrigger_input)\n x=Concatenate()([x,neartrigger_layer])\n numnode+=50\n inputNodes,x,numnode=layers.extralayer(inputNodes,x,numnode,extraidx,featuresidx,MAX_LENGTH)\n\n lstm_out = Bidirectional(LSTM(numnode, dropout=0.2,return_sequences=True))(x)\n numnode=int((numnode+len(labelidx))*2/3)\n\n if onlyArg:\n lstm_out = SeqSelfAttention(attention_activation='tanh', attention_width=5)(lstm_out)\n\n lstm_out = Dropout(0.2)(lstm_out)\n out = Dense(numnode)(lstm_out)\n\n crf = CRF(len(labelidx), sparse_target=False) # CRF layer\n main_output=crf(out)\n loss=crf_loss #crf.loss_function\n acc=[crf_accuracy]\n\n model = Model(inputs=inputNodes, outputs=main_output) \n model.compile(loss=loss,optimizer=Adam(0.001),metrics=acc)\n model.summary()\n\n return model","def pass_word(word, model, input_embedding, char2idx, device, use_head=True):\n inp = torch.LongTensor([char2idx['START']] + [char2idx[c] for c in word]).to(device)\n inp = pack([inp])\n out, hidden = model(input_embedding.unsqueeze(0), inp, use_head=use_head)\n return out, hidden","def build_bilstm(self, verbose=True):\r\n word_ids = Input(batch_shape=(None, None), dtype='int32', name='word_input')\r\n inputs = [word_ids]\r\n\r\n if self._params.use_pretrain_embedding:\r\n if verbose: logging.info(\"initial word embedding with pretrained embeddings\")\r\n if self._params.word_embedding_dim == 100:\r\n glove_file = self._params.data_dir + '/glove.6B.100d.txt'\r\n elif self._params.word_embedding_dim == 300:\r\n glove_file = self._params.data_dir + '/glove.42B.300d.txt'\r\n else:\r\n logging.error(\"we only support glove embedding with dimension 100 or 300\")\r\n raise ValueError(\"unmatch word dimension, we only support glove embedding with dimension 100 or 300\")\r\n glove_embedding_index = load_glove(glove_file, self._params.word_embedding_dim)\r\n word_vocab = self.input_processor.word_vocab.vocab\r\n glove_embeddings_matrix = np.zeros([len(word_vocab), self._params.word_embedding_dim])\r\n for word, i in word_vocab.items():\r\n vector = glove_embedding_index.get(word)\r\n if vector is not None:\r\n glove_embeddings_matrix[i] = vector\r\n \r\n word_embeddings = Embedding(input_dim=glove_embeddings_matrix.shape[0],\r\n output_dim=glove_embeddings_matrix.shape[1],\r\n trainable=False,\r\n mask_zero=True,\r\n weights=[glove_embeddings_matrix],\r\n name='word_embedding')(word_ids)\r\n else:\r\n word_embeddings = Embedding(input_dim=self._params.word_vocab_size,\r\n output_dim=self._params.word_embedding_dim,\r\n mask_zero=True,\r\n name='word_embedding')(word_ids)\r\n\r\n input_embeddings = [word_embeddings]\r\n if self._params.use_char:\r\n char_ids = Input(batch_shape=(None, None, None), dtype='int32', name='char_input')\r\n inputs.append(char_ids)\r\n if self._params.char_feature == \"lstm\":\r\n char_embeddings = Embedding(input_dim=self._params.char_vocab_size,\r\n output_dim=self._params.char_embedding_dim,\r\n mask_zero=True,\r\n name='char_embedding')(char_ids)\r\n if verbose: logging.info(\"using charcter level lstm features\")\r\n char_feas = TimeDistributed(Bidirectional(LSTM(self._params.char_lstm_size)), name=\"char_lstm\")(char_embeddings)\r\n elif self._params.char_feature == \"cnn\":\r\n # cnn do not support mask\r\n char_embeddings = Embedding(input_dim=self._params.char_vocab_size,\r\n output_dim=self._params.char_embedding_dim,\r\n name='char_embedding')(char_ids)\r\n if verbose: logging.info(\"using charcter level cnn features\")\r\n char_feas = char_cnn_encode(char_embeddings, self._params.n_gram_filter_sizes, self._params.n_gram_filter_nums)\r\n else:\r\n raise ValueError('char feature must be lstm or cnn')\r\n\r\n input_embeddings.append(char_feas)\r\n\r\n if self._params.use_pos:\r\n if verbose: logging.info(\"use pos tag features\")\r\n pos_ids = Input(batch_shape=(None, None), dtype='int32', name='pos_input')\r\n inputs.append(pos_ids)\r\n\r\n\r\n pos_embeddings = Embedding(input_dim=self._params.pos_vocab_size,\r\n output_dim=self._params.pos_embedding_dim,\r\n mask_zero=True,\r\n name='pos_embedding')(pos_ids)\r\n input_embeddings.append(pos_embeddings)\r\n\r\n if self._params.use_dict:\r\n if verbose: logging.info(\"use user dict features\")\r\n dict_ids = Input(batch_shape=(None, None), dtype='int32', name='dict_input')\r\n inputs.append(dict_ids)\r\n\r\n dict_embeddings = Embedding(input_dim=self._params.dict_vocab_size,\r\n output_dim=self._params.dict_embedding_dim,\r\n mask_zero=True,\r\n name='dict_embedding')(dict_ids)\r\n input_embeddings.append(dict_embeddings)\r\n\r\n input_embedding = Concatenate(name=\"input_embedding\")(input_embeddings) if len(input_embeddings)>1 else input_embeddings[0]\r\n input_embedding_ln = LayerNormalization(name='input_layer_normalization')(input_embedding)\r\n #input_embedding_bn = BatchNormalization()(input_embedding_ln)\r\n input_embedding_drop = Dropout(self._params.dropout, name=\"input_embedding_dropout\")(input_embedding_ln)\r\n\r\n z = Bidirectional(LSTM(units=self._params.main_lstm_size, return_sequences=True, dropout=0.2, recurrent_dropout=0.2),\r\n name=\"main_bilstm\")(input_embedding_drop)\r\n z = Dense(self._params.fc_dim, activation='tanh', name=\"fc_dense\")(z)\r\n\r\n if self._params.use_crf:\r\n if verbose: logging.info('use crf decode layer')\r\n crf = CRF(self._params.num_labels, sparse_target=False,\r\n learn_mode='marginal', test_mode='marginal', name='crf_out')\r\n loss = crf.loss_function\r\n pred = crf(z)\r\n else:\r\n loss = 'categorical_crossentropy'\r\n pred = Dense(self._params.num_labels, activation='softmax', name='softmax_out')(z)\r\n\r\n model = Model(inputs=inputs, outputs=pred)\r\n model.summary(print_fn=lambda x: logging.info(x + '\\n'))\r\n model.compile(loss=loss, optimizer=self._params.optimizer)\r\n\r\n self.model = model","def pretrained_embedding_layer(model,model2,model3, word_to_index,emb_dim_max):\n words_ignored = []\n vocab_len = len(word_to_index) + 1 \n emb_matrix = np.zeros([vocab_len,emb_dim_max])\n \n print(' Total words would be processed : '+str(vocab_len))\n for word, idx in word_to_index.items():\n if word in model:\n emb_matrix[idx,:200] = model[word]\n emb_matrix[idx,200:] = 0\n if word in model2:\n emb_matrix[idx, :100] = model2[word]\n emb_matrix[idx, 100:] = 0\n if word in model3.keys():\n emb_matrix[idx,:] = model3[word]\n else:\n words_ignored.append(word)\n print(str(len(words_ignored))+\" words ignored\")\n print(emb_matrix.shape) \n \n \n embedding_layer = Embedding(vocab_len,emb_dim_max,trainable = True)\n \n # Build the embedding layer, it is required before setting the weights of the embedding layer. \n embedding_layer.build((None,)) # Do not modify the \"None\". This line of code is complete as-is.\n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n embedding_layer.set_weights([emb_matrix])\n \n return embedding_layer,words_ignored","def text2vec(doc_tok, model, dim=300):\n doc_embedding = np.zeros(dim)\n valid_words = 0\n for word in doc_tok:\n if word in model:\n valid_words += 1\n doc_embedding += model.query(word)\n else:\n continue\n if valid_words > 0:\n return doc_embedding / valid_words\n else:\n return doc_embedding","def train_sentence_dm(model, sentence, lbls, alpha, work=None, neu1=None, train_words=True, train_lbls=True):\n lbl_indices = [lbl.index for lbl in lbls if lbl is not None]\n lbl_sum = np_sum(model.syn0[lbl_indices], axis=0)\n lbl_len = len(lbl_indices)\n neg_labels = []\n if model.negative:\n # precompute negative labels\n neg_labels = zeros(model.negative + 1)\n neg_labels[0] = 1.\n\n for pos, word in enumerate(sentence):\n if word is None:\n continue # OOV word in the input sentence => skip\n reduced_window = random.randint(model.window) # `b` in the original doc2vec code\n start = max(0, pos - model.window + reduced_window)\n window_pos = enumerate(sentence[start : pos + model.window + 1 - reduced_window], start)\n word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)]\n l1 = np_sum(model.syn0[word2_indices], axis=0) + lbl_sum # 1 x layer1_size\n if word2_indices and model.cbow_mean:\n l1 /= (len(word2_indices) + lbl_len)\n neu1e = train_cbow_pair(model, word, word2_indices, l1, alpha, neg_labels, train_words, train_words)\n if train_lbls:\n model.syn0[lbl_indices] += neu1e\n\n return len([word for word in sentence if word is not None])","def _get_embedding_layer(self, input_data, doc_input_data):\n opts = self._options\n word_embedding = tf.Variable(tf.random_uniform((self.vocab_size, opts.embed_dim), -1.0, 1.0))\n embed = []\n\n temp = tf.zeros([opts.batch_size, opts.embed_dim])\n embed_d = []\n for n in range(opts.sentence_sample):\n temp = tf.add(temp, tf.nn.embedding_lookup(word_embedding, doc_input_data[:, n]))\n embed_d.append(temp)\n\n if opts.concat == 'True':\n combined_embed_vector_length = opts.embed_dim * opts.window_size + opts.embed_dim\n for j in range(opts.window_size):\n embed_w = tf.nn.embedding_lookup(word_embedding, input_data[:, j])\n embed.append(embed_w)\n embed.append(embed_d)\n else:\n combined_embed_vector_length = opts.embed_dim\n embed_w = tf.zeros([opts.batch_size, opts.embed_dim])\n for j in range(opts.window_size):\n embed_w += tf.nn.embedding_lookup(word_embedding, input_data[:, j])\n embed_w += embed_d\n embed.append(embed_w)\n\n return tf.concat(embed, 1), word_embedding, combined_embed_vector_length","def forward(self, doc):\n out = torch.tensor([]).float().to(self.device)\n\n for i in range(len(doc)):\n sentences_raw = sentencesplit(cleantxt(doc[i]))\n sentences_ready = torch.tensor([]).float().to(self.device)\n for sentence in sentences_raw:\n sentence = sentence.split()\n if sentence == []:\n continue\n lookup_tensor = torch.tensor([]).long().to(self.device)\n for word in sentence:\n if word in self.embedd_dict:\n lookup_tensor = torch.cat((lookup_tensor,\n torch.LongTensor([self.embedd_dict[word]])), 0)\n else:\n lookup_tensor = torch.cat((lookup_tensor, torch.LongTensor([0])), 0)\n # Word embedding\n xw = self.word_embedding(lookup_tensor).view(1, -1, self.embedding_dim).to(self.device)\n # Word GRU\n self.hidden_gru_words = self.init_hidden_words()\n hw, self.hidden_gru_words = self.gru_word(xw, self.hidden_gru_words)\n # Word MLP\n uw = nn.Tanh()(self.MLP_word(hw)).to(self.device)\n # Word attention\n attention_score = torch.matmul(uw, self.attention_word).squeeze().to(self.device)\n attention_score = F.softmax(attention_score, dim=0).view(uw.size(0), uw.size(1), 1).to(self.device)\n scored_x = (hw * attention_score).to(self.device)\n s = torch.sum(scored_x, dim=1).to(self.device)\n #collecting sentences\n sentences_ready = torch.cat((sentences_ready, s), 0)\n # Sentence GRU\n if len(sentences_ready) == 0:\n out = torch.cat((out,\n torch.randn(1, self.number_cat).to(self.device)), 0).to(self.device)\n continue\n sentences_ready_gru = sentences_ready.view(1, -1, self.embedding_dim).to(self.device)\n self.hidden_gru_sentences = self.init_hidden_sentences()\n hs, self.hidden_gru_sentences = self.gru_sentence(torch.tensor(sentences_ready_gru), self.hidden_gru_sentences)\n # SENTENCE MLP\n us = nn.Tanh()(self.MLP_sentence(hs)).to(self.device)\n # Sentence attention\n attention_score = torch.matmul(us, self.attention_sentence).squeeze().to(self.device)\n attention_score = F.softmax(attention_score, dim=0).view(us.size(0), us.size(1), 1).to(self.device)\n scored_x = (hs * attention_score).to(self.device)\n v = torch.sum(scored_x, dim=1).to(self.device)\n # classification\n p = self.MLP_classification(v).to(self.device)\n out = torch.cat((out, p.float()), 0).float().to(self.device)\n return out","def get_embed(input_data, vocab_size, embed_dim):\n embedding = tf.Variable(tf.random_uniform((vocab_size,embed_dim), -1, 1))\n embed = tf.nn.embedding_lookup(embedding, input_data)\n #print (\"embed_dim: \",embed_dim) # 向量表达维度为 256\n #print (\"input_data.shape: \",input_data.shape) # (50, 5)\n #print (\"embed.shap: \", embed.shape) # word 的向量表达 ==特征 (50, 5, 256) ==(batch_size, num_step, embed_dim)\n return embed # 返回input的向量表达","def forward(self,input,mask,length):\r\n max_length = length.max()\r\n batch_size = input.shape[0]\r\n h,c = self.init_hidden_state(batch_size)\r\n embeddings = torch.zeros(batch_size,max_length,self.hidden_size).cuda()\r\n end_indx = length.unsqueeze(-1).long().cuda() - 1\r\n mask[:,0] = 0\r\n mask = mask.scatter_(1,end_indx,1)\r\n for t in range(max_length):\r\n pad_mask = mask[:,t].unsqueeze(-1).repeat(1,self.hidden_size)\r\n h = h*pad_mask[:h.shape[0],:]\r\n c = c*pad_mask[:h.shape[0],:]\r\n batch_size_t = sum([l >t for l in length])\r\n h,c = self.rnn(input[:batch_size_t,t,:], (h[:batch_size_t],c[:batch_size_t])\r\n )\r\n embeddings[:batch_size_t,t,:] = h\r\n \r\n word_nums = mask.sum(1)\r\n max_word_num = word_nums.max().int()\r\n i = 0\r\n\r\n # take the\r\n for num in word_nums:\r\n p = (num - word_nums.max()).int()\r\n # if mask[i,-1] != 0:\r\n # p = p-1\r\n # mask[i,p:] = 1\r\n if p != 0:\r\n mask[i,p:] = 1\r\n while mask[i].sum()<word_nums.max():\r\n p -= 1\r\n mask[i,p:] = 1\r\n i += 1\r\n index = mask.nonzero() # index[0,:] batch position, index[:,1] frame position\r\n index = index[:,0] * mask.shape[1] + index[:,1] - 1 # we take the frame of the boundary as the begaining of next word\r\n flat = embeddings.view(-1,embeddings.shape[-1])\r\n word_embeddings = flat[index.long()]\r\n output = word_embeddings.view(batch_size,max_word_num,-1)\r\n output = output.transpose(2,1)\r\n\r\n\r\n\r\n return output, word_nums","def prepare_word_emb_matrices(experiment):\r\n\r\n with open(\"public_data/stats/stats_train.pkl\", 'rb') as stats:\r\n stats = pickle.load(stats)\r\n vocab = stats[\"VOCAB\"]\r\n stops = [word.lower() for word in set(stopwords.words('english'))]\r\n vocab = vocab + stops\r\n\r\n if experiment == \"RANDOM\":\r\n word_embs = np.random.uniform(low=-1.0, high=1.0, size=(len(vocab), PARAMS[\"SIZE\"])).astype(\"float32\")\r\n\r\n else:\r\n word_embs = []\r\n count_unk = 0\r\n count_kn = 0\r\n\r\n if experiment == \"5\":\r\n emb_model = KeyedVectors.load_word2vec_format(\"public_data/models/experiment_5/embeddings_5.bin\",\r\n binary=True)\r\n elif experiment == \"6\":\r\n emb_model = Word2Vec.load(\"public_data/models/experiment_6/embeddings_6\")\r\n\r\n elif experiment in [\"7\", \"8\"]:\r\n emb_model = FastText.load_fasttext_format(\"public_data/models/experiment_%s/embeddings_%s.bin\"\r\n %(experiment, experiment))\r\n for word in vocab:\r\n if word in emb_model:\r\n word_embs.append(emb_model[word])\r\n count_kn += 1\r\n else:\r\n word_embs.append(np.random.uniform(low=-1.0, high=1.0, size=PARAMS[\"SIZE\"]))\r\n count_unk += 1\r\n\r\n word_embs = np.array(word_embs).astype(\"float32\")\r\n print(count_unk / (count_kn + count_unk))\r\n\r\n pad = np.zeros(shape=PARAMS[\"SIZE\"]).astype(\"float32\")\r\n unk = np.random.uniform(low=-1.0, high=1.0, size=PARAMS[\"SIZE\"]).astype(\"float32\")\r\n word_embs = np.insert(word_embs, 0, unk, axis=0) #id 1\r\n word_embs = np.insert(word_embs, 0, pad, axis=0) #id 0\r\n\r\n with open(\"public_data/embeddings/word_embeddings_%s.pkl\" %experiment, 'wb') as out:\r\n pickle.dump(word_embs, out, protocol=4)\r\n\r\n return word_embs","def create_embedding_matrix(self):\n self.id2word = dict([(self.vocab[word]['id'], word) for word in self.vocab])\n vocab_size = len(self.vocab)\n result = np.zeros((vocab_size, self.embed_dim))\n unknown_token_set = set()\n\n found_words = 0\n avg = np.zeros(self.embed_dim)\n for _ in range(1, vocab_size): # skip PAD embedding (initialize as zero embedding)\n try:\n result[_] = self.pretrained_embedding[self.id2word[_]]\n avg += result[_]\n found_words += 1\n except:\n unknown_token_set.add(_)\n\n avg /= found_words\n for _ in unknown_token_set:\n result[_] = avg\n self.embedding = result\n return found_words, len(self.id2word)","def __init__(self, embed_size, vocab):\n super(ModelEmbeddings, self).__init__()\n \n self.embed_size = embed_size\n self.char_embed_size = 50\n self.max_word_len = 21\n self.dropout_rate = 0.3\n self.vocab = vocab \n \n ## A4 code\n pad_token_idx = vocab.char2id['<pad>']\n self.embedding = nn.Embedding(num_embeddings =len(vocab.char2id),\n embedding_dim =self.char_embed_size,\n padding_idx =pad_token_idx,)\n \n self.CNN = CNN(char_embed_size=self.char_embed_size,\n num_filters=embed_size,\n max_word_length=self.max_word_len,)\n self.Highway = Highway(word_embed_size=self.embed_size)\n self.dropout = nn.Dropout(p=self.dropout_rate)\n ## End A4 code\n\n ### YOUR CODE HERE for part 1j\n\n\n ### END YOUR CODE","def construct_embedding(self):\n i = 0\n self.load_dicts()\n embedding_shape = (max(self.word2idx.values()) + 1,\n self.embedding_size)\n self.embedding = np.zeros(embedding_shape)\n\n with open(self.config.word_vec_fi_glove, 'r') as fi:\n for line in fi:\n word_vec = line.split(\" \")[1:]\n self.embedding[i, :] = np.array(word_vec, dtype=np.float32)\n i += 1\n\n self.write_embedding()","def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n \n vocab_size = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n any_word = list(word_to_vec_map.keys())[0]\n emb_dim = word_to_vec_map[any_word].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n \n ### START CODE HERE ###\n # Step 1\n # Initialize the embedding matrix as a numpy array of zeros.\n # See instructions above to choose the correct shape.\n emb_matrix = np.zeros((vocab_size, emb_dim))\n \n # Step 2\n # Set each row \"idx\" of the embedding matrix to be \n # the word vector representation of the idx'th word of the vocabulary\n for word, idx in word_to_index.items():\n emb_matrix[idx, :] = word_to_vec_map[word]\n\n # Step 3\n # Define Keras embedding layer with the correct input and output sizes\n # Make it non-trainable.\n embedding_layer = tensorflow.keras.layers.Embedding(input_dim = vocab_size, output_dim = emb_dim, trainable = False)\n ### END CODE HERE ###\n\n # Step 4 (already done for you; please do not modify)\n # Build the embedding layer, it is required before setting the weights of the embedding layer. \n embedding_layer.build((None,)) # Do not modify the \"None\". This line of code is complete as-is.\n \n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n embedding_layer.set_weights([emb_matrix])\n \n return embedding_layer","def set_glove_embedding(self,fpath,embedding_dim):\n\t\temb = np.random.randn(self._count,embedding_dim)\n#\ttf.logging.info(emb[0])\n\t\twith open(fpath) as f: #python 3.x support \n\t\t\tfor k,line in enumerate(f):\n\t\t\t\tfields = line.split()\n\t\t\t\tif len(fields) - 1 != embedding_dim:\n\t\t\t\t\t# Sometimes there are funny unicode parsing problems that lead to different\n\t\t\t\t\t# fields lengths (e.g., a word with a unicode space character that splits\n\t\t\t\t\t# into more than one colum n). We skip those lines. Note that if you have\n\t\t\t\t\t# some kind of long header, this could result in all of your lines getting\n\t\t\t\t\t# skipped. It's hard to check for that here; you just have to look in the\n\t\t\t\t\t# embedding_misses_file and at the model summary to make sure things look\n\t\t\t\t\t# like they are supposed to.\n\t\t\t\t\t#logger.warning(\"Found line with wrong number of dimensions (expected %d, was %d): %s\",\n\t\t\t\t\t\t\t# embedding_dim, len(fields) - 1, line)\n\t\t\t\t\traise Exception(\"Found line with wrong number of dimensions (expected %d, was %d): %s\",\n\t\t\t\t\t\t\t\t\t\t\t embedding_dim, len(fields) - 1, line)\n\t\t\t\t\tcontinue\n\t\t\t\tword = fields[0]\n\t\t\t\tif word in self._word_to_id:\n\t\t\t\t\tvector = np.asarray(fields[1:], dtype='float32')\n\t\t\t\t\temb[self._word_to_id[word]] = vector\n#\t\tif k%1000 == 0:\n#\t\t tf.logging.info('glove : %d',k)\n\t\tself.glove_emb = emb","def embeddings_layer(x, Wemb, dim_proj):\n\n n_words = x.shape[0]\n n_max_letters_in_word = x.shape[1]\n n_batch = x.shape[2]\n\n dist = Wemb[x.flatten()].reshape([n_words, n_max_letters_in_word, n_batch, dim_proj])\n return dist","def __getitem__(self, key):\n \"\"\"\n 原始batch的数据格式：\n sent=[\n word_id_list, # 包括ROOT_ID；0\n char_id_list, # 包括ROOT_ID；1\n upos_id_list, # 包括ROOT_ID；2\n pretrain_vocab_list, # 包括ROOT_ID；3\n graph_arc_list,\n ]\n graph_arc_list=[[head_id,red_id],...]\n \"\"\"\n if not isinstance(key, int):\n raise TypeError\n if key < 0 or key >= len(self.data):\n raise IndexError\n batch = self.data[key]\n batch_size = len(batch)\n\n batch = list(zip(*batch))\n\n assert len(batch) == 5 # word_idx_list, char_idx_list, pos_id_list, pretrain_list, arc_list\n\n # sort sentences by lens for easy RNN operations\n lens = [len(x) for x in batch[0]] # 统计所有句长\n batch, orig_idx = sort_all(batch, lens) # 按照句长排序（降序），易于RNN处理\n\n # sort words by lens for easy char-RNN operations\n # 对char排序，易于char-RNN处理\n batch_words = [w for sent in batch[1] for w in sent]\n word_lens = [len(x) for x in batch_words]\n\n batch_words, word_orig_idx = sort_all([batch_words], word_lens)\n batch_words = batch_words[0] # 去掉外层括号\n word_lens = [len(x) for x in batch_words]\n\n # convert to tensors and pad to max length\n words = batch[0]\n words = get_long_tensor(words, batch_size)\n words_mask = torch.eq(words, PAD_ID)\n\n wordchars = get_long_tensor(batch_words, len(word_lens))\n wordchars_mask = torch.eq(wordchars, PAD_ID)\n\n upos = get_long_tensor(batch[2], batch_size)\n pretrained = get_long_tensor(batch[3], batch_size)\n sentlens = [len(x) for x in batch[0]]\n arcs = batch[4]\n return words, words_mask, wordchars, wordchars_mask, upos, pretrained, arcs, orig_idx, word_orig_idx, sentlens, word_lens","def _embed(self):\n with tf.variable_scope('word_embedding'):\n self.word_embeddings = tf.get_variable(\n 'word_embeddings',\n shape=(self.term_vocab.size(), self.term_vocab.embed_dim),\n initializer=tf.constant_initializer(self.term_vocab.embeddings),\n trainable=True\n )\n self.p_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.p)\n self.q_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.q)\n\n with tf.variable_scope('char_embedding'):\n self.char_embeddings = tf.get_variable(\n 'char_embeddings',\n shape=(self.char_vocab.size(), self.char_vocab.embed_dim),\n initializer=tf.constant_initializer(self.char_vocab.embeddings),\n trainable=True\n )\n self.p_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.p_char) # [batch, seqlen, max_char_num, embedding_size]\n self.q_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.q_char)\n\n self.p_char_emb = self.cnn_emb(self.p_char_emb, \"p_emb\")\n self.q_char_emb = self.cnn_emb(self.q_char_emb, \"q_emb\")\n '''\n self.p_char_emb = tf.reshape(self.p_char_emb, [-1, self.max_char_num, self.emb_size])\n self.q_char_emb = tf.reshape(self.q_char_emb, [-1, self.max_char_num, self.emb_size])\n\n self.p_char_emb = cnn_layer.conv(self.p_char_emb, self.emb_size,\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\"char_conv\", reuse=None)\n self.q_char_emb = cnn_layer.conv(self.q_char_emb, self.emb_size,\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\"char_conv\", reuse=True)\n\n self.p_char_emb = tf.reduce_max(self.p_char_emb, axis=1) # [batch*seqlen, 1, emb_size]\n self.q_char_emb = tf.reduce_max(self.q_char_emb, axis=1)\n\n batch_size = tf.shape(self.p_word_emb)[0]\n self.p_char_emb = tf.reshape(self.p_char_emb, [batch_size, -1, self.emb_size])\n self.q_char_emb = tf.reshape(self.q_char_emb, [batch_size, -1, self.emb_size])\n\n self.p_char_emb = tf.nn.dropout(self.p_char_emb, 0.95)\n self.q_char_emb = tf.nn.dropout(self.q_char_emb, 0.95)\n '''\n self.p_emb = tf.concat([self.p_word_emb, self.p_char_emb], -1)\n self.q_emb = tf.concat([self.q_word_emb, self.q_char_emb], -1)","def word2vec2embed(word2vec, word2idx):\n\temb_dim = word2vec['the'].shape[0]\n\temb = torch.nn.Embedding(len(word2idx), emb_dim) \n\temb_matrix = []\n\tfor w, idx in word2idx.items():\n\t\tif w in word2vec:\n\t\t\temb_matrix.append(word2vec[w])\n\t\telse:\n\t\t\temb_matrix.append(np.zeros(emb_dim,))\n\temb.weight.data.copy_(torch.from_numpy(np.array(emb_matrix)))\n\treturn emb","def _use_embeddings(self, word):\n if word == \"@PAD@\":\n return torch.zeros(self.embeddings_dim)\n else:\n return self.embeddings[word]","def __init__(self, voc_size=8000, embed_size=100, hid_size=100, trunc=4,\n model=None):\n\n self.log = logging.getLogger(\"TEST.Embed\")\n self.log.setLevel(logging.INFO)\n\n self.unknown_token = \"UNKNOWN_TOKEN\"\n self.sentence_start_token = \"SENTENCE_START\"\n self.sentence_end_token = \"SENTENCE_END\"\n\n if model is None:\n self.log.info(\"Initializing RNN parameters and functions...\")\n\n self.vocabulary_size = voc_size\n self.embed_size = embed_size\n self.hidden_size = hid_size\n self.bptt_truncate = trunc\n\n # Instantiate the network weights\n # I feel like the first and third are switched for some reason...\n # but it's pretty consistent in the example code. Perhaps it's\n # backwards for a purpose\n # The weights going from the input layer to the word embedding\n # layer (E, in tutorial)\n weights_ie = np.random.uniform(-np.sqrt(1./voc_size),\n np.sqrt(1./voc_size),\n (embed_size, voc_size))\n\n # The weights going from input layer to hidden layer\n # (U, in tutorial)\n weights_eh = np.random.uniform(-np.sqrt(1./voc_size),\n np.sqrt(1./voc_size),\n (3, hid_size, embed_size))\n\n # The weights going from hidden layer to hidden layer\n # (W, in tutorial)\n weights_hh = np.random.uniform(-np.sqrt(1./voc_size),\n np.sqrt(1./voc_size),\n (3, hid_size, hid_size))\n\n # The weights going from hidden layer to output layer\n # (V, in tutorial)\n weights_ho = np.random.uniform(-np.sqrt(1./voc_size),\n np.sqrt(1./voc_size),\n (voc_size, hid_size))\n\n # The bias for the hidden units (no bias applied to embedding layer)\n bias = np.zeros((3, hid_size))\n\n # The bias for the output units\n out_bias = np.zeros(voc_size)\n\n self.weights_ie = theano.shared(\n name='weights_ie',\n value=weights_ie.astype(theano.config.floatX))\n\n self.weights_eh = theano.shared(\n name='weights_eh',\n value=weights_eh.astype(theano.config.floatX))\n\n self.weights_hh = theano.shared(\n name='weights_hh',\n value=weights_hh.astype(theano.config.floatX))\n\n self.weights_ho = theano.shared(\n name='weights_ho',\n value=weights_ho.astype(theano.config.floatX))\n\n self.bias = theano.shared(\n name='bias',\n value=bias.astype(theano.config.floatX))\n\n self.out_bias = theano.shared(\n name='out_bias',\n value=out_bias.astype(theano.config.floatX))\n\n self.cache_ie = theano.shared(\n name='cache_ie',\n value=np.zeros(weights_ie.shape).astype(theano.config.floatX))\n\n self.cache_eh = theano.shared(\n name='cache_eh',\n value=np.zeros(weights_eh.shape).astype(theano.config.floatX))\n\n self.cache_hh = theano.shared(\n name='cache_hh',\n value=np.zeros(weights_hh.shape).astype(theano.config.floatX))\n\n self.cache_ho = theano.shared(\n name='cache_ho',\n value=np.zeros(weights_ho.shape).astype(theano.config.floatX))\n\n self.cache_bias = theano.shared(\n name='cache_bias',\n value=np.zeros(bias.shape).astype(theano.config.floatX))\n\n self.cache_out_bias = theano.shared(\n name='cache_out_bias',\n value=np.zeros(out_bias.shape).astype(theano.config.floatX))\n\n self.vocabulary = []\n self.word_to_index = {}\n self.index_to_word = []\n else:\n self.log.info(\"Loading model parameters from saved model...\")\n\n with open(model, \"rb\") as modelFile:\n params = cPickle.load(modelFile)\n\n self.vocabulary_size = params[0]\n self.embed_size = params[1]\n self.hidden_size = params[2]\n self.bptt_truncate = params[3]\n\n self.weights_ie = params[4]\n self.weights_eh = params[5]\n self.weights_hh = params[6]\n self.weights_ho = params[7]\n\n self.vocabulary = params[8]\n if not self.vocabulary[-1] == self.unknown_token:\n self.log.info(\"Appending unknown token\")\n self.vocabulary[-1] = self.unknown_token\n self.index_to_word = params[9]\n self.word_to_index = params[10]\n\n self.bias = params[11]\n self.out_bias = params[12]\n\n self.cache_ie = params[13]\n self.cache_eh = params[14]\n self.cache_hh = params[15]\n self.cache_ho = params[16]\n self.cache_bias = params[17]\n self.cache_out_bias = params[18]\n # End of if statement\n\n # Symbolic representation of one input sentence\n input = T.ivector('sentence')\n\n # Symbolic representation of the one output sentence\n output = T.ivector('sentence')\n\n # Symbolic representation of the cache decay for RMSprop\n decay = T.scalar('decay')\n\n # Stochastic Gradient Descent step\n learning_rate = T.scalar('learning_rate')\n\n def forward_propagate(word, previous_state):\n \"\"\"\n Vertically propagates one of the words.\n\n :type word: int\n :param word: the index of the current input word\n\n :type previous_state: T.dvector()\n :param word: the output of the hidden layer from the previous\n horizontal layer\n \"\"\"\n # Embedding layer\n word_vector = self.weights_ie[:, word]\n\n # GRU layer\n update_gate = T.nnet.hard_sigmoid(\n self.weights_eh[0].dot(word_vector) +\n self.weights_hh[0].dot(previous_state) +\n self.bias[0]\n )\n\n reset_gate = T.nnet.hard_sigmoid(\n self.weights_eh[1].dot(word_vector) +\n self.weights_hh[1].dot(previous_state) +\n self.bias[1]\n )\n\n hypothesis = T.tanh(\n self.weights_eh[2].dot(word_vector) +\n self.weights_hh[2].dot(previous_state * reset_gate) +\n self.bias[2]\n )\n\n current_state = (T.ones_like(update_gate) - update_gate) * hypothesis + update_gate * previous_state\n\n # Output layer\n current_output = T.nnet.softmax(\n self.weights_ho.dot(current_state) + self.out_bias\n )[0]\n\n # Not sure why current_output[0] and not just current_output...\n return [current_output, current_state]\n\n #######################################################################\n # Symbolically represents going through each input sentence word and\n # then calculating the state of the hidden layer and output word for\n # each word. The forward_propagate function is the one used to\n # generate the output word and hidden layer state.\n #######################################################################\n self.theano = {}\n\n [out, state], updates = theano.scan(\n forward_propagate,\n sequences=input,\n truncate_gradient=self.bptt_truncate,\n outputs_info=[None, dict(initial=T.zeros(self.hidden_size))],\n name=\"forward_propagate\"\n )\n\n # Predicts the output words for each word in the sentence\n prediction = T.argmax(out, axis=1)\n\n # Calculates the output error between the predicted output and the\n # actual output\n out_error = T.sum(T.nnet.categorical_crossentropy(out, output))\n\n # Symbolically represents gradient calculations for gradient descent\n d_weights_ie = T.grad(out_error, self.weights_ie)\n d_weights_eh = T.grad(out_error, self.weights_eh)\n d_weights_hh = T.grad(out_error, self.weights_hh)\n d_weights_ho = T.grad(out_error, self.weights_ho)\n d_bias = T.grad(out_error, self.bias)\n d_out_bias = T.grad(out_error, self.out_bias)\n\n # Symbolic theano functions\n self.forward_propagate = theano.function([input], out,\n name=\"forward_propagate\")\n self.predict = theano.function([input], prediction, name=\"predict\")\n self.calculate_error = theano.function([input, output], out_error,\n name=\"calculate_error\")\n self.bptt = theano.function([input, output],\n [d_weights_ie, d_weights_eh, d_weights_hh, d_weights_ho, d_bias,\n d_out_bias],\n name=\"bptt\")\n\n # RMSprop parameters\n cache_ie = (decay * self.cache_ie) + ((1 - decay) * d_weights_ie ** 2)\n cache_eh = (decay * self.cache_eh) + ((1 - decay) * d_weights_eh ** 2)\n cache_hh = (decay * self.cache_hh) + ((1 - decay) * d_weights_hh ** 2)\n cache_ho = (decay * self.cache_ho) + ((1 - decay) * d_weights_ho ** 2)\n cache_bias = (decay * self.cache_bias) + ((1 - decay) * d_bias ** 2)\n cache_out_bias = (decay * self.cache_out_bias) + ((1 - decay) * d_out_bias ** 2)\n eps = 1e-6 # Prevents division by 0\n\n self.sgd_step = theano.function(\n [input, output, learning_rate, theano.In(decay, value=0.9)],\n [],\n updates=[\n (self.weights_ie, self.weights_ie - learning_rate *\n d_weights_ie / (T.sqrt(self.cache_ie + eps))),\n (self.weights_eh, self.weights_eh - learning_rate *\n d_weights_eh / (T.sqrt(self.cache_eh + eps))),\n (self.weights_hh, self.weights_hh - learning_rate *\n d_weights_hh / (T.sqrt(self.cache_hh + eps))),\n (self.weights_ho, self.weights_ho - learning_rate *\n d_weights_ho / (T.sqrt(self.cache_ho + eps))),\n (self.bias, self.bias - learning_rate * d_bias /\n (T.sqrt(self.cache_bias + eps))),\n (self.out_bias, self.out_bias - learning_rate *\n d_out_bias / (T.sqrt(self.cache_out_bias + eps))),\n (self.cache_ie, cache_ie),\n (self.cache_eh, cache_eh),\n (self.cache_hh, cache_hh),\n (self.cache_ho, cache_ho),\n (self.cache_bias, cache_bias),\n (self.cache_out_bias, cache_out_bias)]\n )\n\n self.x_train = None\n self.y_train = None","def add_word_embedding_op(self):\n if self.pos:\n print(\"adding pos embeddings\")\n with tf.variable_scope(\"pos\"):\n _pos_embeddings = tf.Variable(self.pos_embeddings,\n name=\"la_pos_embeddings\",\n dtype=tf.float32, trainable=False)\n pos_embeddings = tf.nn.embedding_lookup(_pos_embeddings, self.pos_ids,\n name=\"pos_embeddings\")\n self.pos_vecs = pos_embeddings\n print(\"adding word_embeddings\")\n with tf.variable_scope(\"words\"):\n _word_embeddings = tf.Variable(self.embeddings, name=\"_word_embeddings\",\n dtype=tf.float32, trainable=False)\n word_embeddings = tf.nn.embedding_lookup(_word_embeddings,\n self.word_ids,\n name=\"word_embeddings\")\n if self.use_window:\n print(\"Concatenating word vectors of context words\")\n word_embeddings_sl = tf.nn.embedding_lookup(_word_embeddings,\n self.word_ids_sl,\n name=\"word_embeddings_sl\")\n word_embeddings_sr = tf.nn.embedding_lookup(_word_embeddings,\n self.word_ids_sr,\n name=\"word_embeddings_sr\")\n word_embeddings = tf.concat([word_embeddings_sr, word_embeddings,\n word_embeddings_sl], axis=-1)\n if self.use_char_embeddings:\n print(\"adding CNN for char embeddings\")\n with tf.variable_scope(\"chars\"):\n _char_embeddings = tf.get_variable(name=\"_char_embeddings\",\n dtype=tf.float32,\n shape=[self.char_count, \n self.c_dim_input])\n char_embeddings = tf.nn.embedding_lookup(_char_embeddings, \n self.char_ids, \n name=\"char_embeddings\")\n s = char_embeddings.shape\n # the shape of our char_embeddings is now (batch_size, max number of words\n # in each sentence, max number of chars in each word, self.c_dim )\n char_filter = tf.get_variable(\"char_filter\", dtype=tf.float32,\n shape=[self.c_filter_width, \n self.c_filter_height,\n self.c_dim_input,\n self.c_dim_output])\n print(\"adding 2d convolution layer\")\n char_conv_layer = tf.nn.conv2d(char_embeddings, char_filter, \n strides=[1, 1, 1, 1], \n padding=\"SAME\")\n char_conv_layer = tf.nn.tanh(char_conv_layer)\n print(\"adding 2d pooling layer\")\n char_conv_layer = tf.layers.max_pooling2d(char_conv_layer, \n 1, \n strides=1)\n char_output = tf.reshape(char_conv_layer, shape=[-1, self.max_len, \n self.max_word_length*\n self.c_dim_output])\n word_embeddings = tf.concat([word_embeddings, char_output], axis=-1)\n if self.pos and self.concat_pos:\n print(\"concatenating pos with word_embeddings\")\n word_embeddings = tf.concat([word_embeddings, pos_embeddings], axis=-1)\n self.word_embeddings = word_embeddings\n if self.use_additional and self.hybrid:\n print(\"using additional embeddings\")\n _word_embeddings_2 = tf.Variable(self.additional_embeddings,\n name=\"two_word_embeddings\",\n dtype=tf.float32, trainable=False)\n word_embeddings_2 = tf.nn.embedding_lookup(_word_embeddings_2,\n self.word_ids,\n name=\"two_word_embeddings\")\n self.word_embeddings_2 = word_embeddings_2","def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, neu1=None, learn_doctags=True,\n learn_words=True, learn_hidden=True, word_vectors=None, word_locks=None,\n doctag_vectors=None, doctag_locks=None):\n if word_vectors is None:\n word_vectors = model.wv.syn0\n if word_locks is None:\n word_locks = model.syn0_lockf\n if doctag_vectors is None:\n doctag_vectors = model.docvecs.doctag_syn0\n if doctag_locks is None:\n doctag_locks = model.docvecs.doctag_syn0_lockf\n\n word_vocabs = [model.wv.vocab[w] for w in doc_words if w in model.wv.vocab and\n model.wv.vocab[w].sample_int > model.random.rand() * 2 ** 32]\n doctag_len = len(doctag_indexes)\n if doctag_len != model.dm_tag_count:\n return 0 # skip doc without expected number of doctag(s) (TODO: warn/pad?)\n\n null_word = model.wv.vocab['\\0']\n pre_pad_count = model.window\n post_pad_count = model.window\n padded_document_indexes = (\n (pre_pad_count * [null_word.index]) # pre-padding\n + [word.index for word in word_vocabs if word is not None] # elide out-of-Vocabulary words\n + (post_pad_count * [null_word.index]) # post-padding\n )\n\n for pos in range(pre_pad_count, len(padded_document_indexes) - post_pad_count):\n word_context_indexes = (\n padded_document_indexes[(pos - pre_pad_count): pos] # preceding words\n + padded_document_indexes[(pos + 1):(pos + 1 + post_pad_count)] # following words\n )\n predict_word = model.wv.vocab[model.wv.index2word[padded_document_indexes[pos]]]\n # numpy advanced-indexing copies; concatenate, flatten to 1d\n l1 = concatenate((doctag_vectors[doctag_indexes], word_vectors[word_context_indexes])).ravel()\n neu1e = train_cbow_pair(model, predict_word, None, l1, alpha,\n learn_hidden=learn_hidden, learn_vectors=False)\n\n # filter by locks and shape for addition to source vectors\n e_locks = concatenate((doctag_locks[doctag_indexes], word_locks[word_context_indexes]))\n neu1e_r = (neu1e.reshape(-1, model.vector_size)\n * np_repeat(e_locks, model.vector_size).reshape(-1, model.vector_size))\n\n if learn_doctags:\n np_add.at(doctag_vectors, doctag_indexes, neu1e_r[:doctag_len])\n if learn_words:\n np_add.at(word_vectors, word_context_indexes, neu1e_r[doctag_len:])\n\n return len(padded_document_indexes) - pre_pad_count - post_pad_count","def train(self, examples):\n print(examples)\n # first we will do gensim to get word embeddings\n tokens = []\n for example in examples:\n for tuple in example:\n tokens.append([tuple[0]])\n self.model = Word2Vec(tokens, min_count=1, size=100).wv\n # shuffle the examples so that they are gone through 'randomly'\n #print(examples)\n random.shuffle(examples)\n #print(examples)\n # iterate through our examples\n for j in range(len(examples)):\n # the stored label for the previous token\n prev_label = None\n prev_word = None\n # iterate through our tokens for the example\n for i in range(len(examples[j])):\n # store our token and its label\n token = examples[j][i][0]\n y = examples[j][i][1]\n # get the features for our current token\n next_word = None\n if i <= (len(examples)-1):\n next_word = examples[j][i+1][0]\n features = self.featurize(prev_label, prev_word, token, next_word)\n # set our previous label to our current since\n # we are done featurizing and need to store it for\n # the next iteration\n prev_label = y\n # a dictionary that will store our z values\n z = {}\n # calculate our z value for every state for\n # the example we are on\n # z(state) = features * weights\n # z[state] = np.dot(features, weights[state])\n for state in self.states:\n z[state] = np.dot(features, self.weights[state])\n # store our max\n max = -1\n # store our y_hat\n y_hat = None\n # store our probabilities\n prob = {}\n # this runs softmax on our z's\n # y_hat = softmax(z)\n denom = sum(np.exp(np.array(list(z.values()))))\n for state in self.states:\n # softmax = p(state) = e^z[state] / (sum[e^z for all z's)\n # making sure this works the way I want it to, should\n # be three values\n #print(np.array(list(z.values())))\n #print(np.exp(np.array(list(z.values()))))\n prob[state] = np.exp(z[state]) / denom\n # if our current prob is greater than the others then it is our boy\n if prob[state] > max:\n # save the new prob as the max\n max = prob[state]\n # save the state as our prediction y_hat\n y_hat = state\n # this will hold our gradients for all the states\n gradients = {}\n for state in self.states:\n # gradient[state] = ((y_hat == state) - prob[state]) * features\n gradients[state] = ((y_hat == state) - prob[state]) * features\n # weights[state] -= loss * gradients\n self.weights[state] -= self.loss * gradients[state]","def __init__(self, vocab_size, embedding_size, context_size, hid_dim, out_dim):\n super(Net, self).__init__()\n self.E = nn.Embedding(vocab_size, embedding_size) # Embedding matrix\n self.after_embed_size = embedding_size * context_size\n self.lin = nn.Linear(self.after_embed_size, hid_dim)\n self.lin2 = nn.Linear(hid_dim, out_dim)","def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n emb_dim = word_to_vec_map[\"cucumber\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n emb_matrix = np.zeros((vocab_len, emb_dim)) # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\n for word, index in word_to_index.items(): # Set each row \"index\" of the embedding matrix to be the word vector representation of the \"index\"th word of the vocabulary\n emb_matrix[index, :] = word_to_vec_map[word]\n embedding_layer = Embedding(vocab_len, emb_dim, trainable = False) # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \n embedding_layer.build((None,)) # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \"None\".\n embedding_layer.set_weights([emb_matrix]) # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n return embedding_layer","def sample_beam(model, input_embedding, char2idx, idx2char, k=5, maxlen=30,\n start='START', use_head=True):\n with torch.no_grad():\n device = input_embedding.device\n softmax = nn.Softmax(dim=1)\n if use_head:\n input_embedding = input_embedding.view(1, -1)\n\n inp = [torch.LongTensor([char2idx[start]]).to(device)]\n inp = nn.utils.rnn.pack_sequence(inp)\n out, hidden = model(input_embedding, inp, use_head=use_head)\n\n out = softmax(out.data).view(-1).cpu().numpy()\n max_k = np.argsort(out)[-k:][::-1]\n oldprobs = out[max_k]\n words = [[i] for i in max_k]\n inp = pack([torch.LongTensor([j]).to(device) for j in max_k])\n\n if model.mode == 'LSTM':\n hidden0 = torch.cat([hidden[0] for i in range(k)], dim=1)\n hidden1 = torch.cat([hidden[1] for i in range(k)], dim=1)\n hidden = hidden0, hidden1\n else:\n hidden = torch.cat([hidden for i in range(k)], dim=1)\n WORDS = []\n for c in range(maxlen):\n out, hidden = model(hidden, inp, use_head=False)\n out = softmax(out.data).cpu().numpy()\n\n #print(out.shape)\n inpnp = inp.data.detach().cpu().numpy()\n done = np.where(inpnp == char2idx['END'])\n out[done] = 0\n if len(out[done]) != 0:\n #print(out[done].shape)\n for d in done[0]:\n out[d][char2idx['END']] = 1\n #print(done)\n\n #print(out)\n #print(out[done])\n out = (oldprobs.reshape(-1, 1)*out)\n max_k = np.argsort(out)[:, -k:][:, ::-1]\n\n #print(max_k)\n probs = np.array([out[i][max_k[i]] for i in range(k)])\n #print(probs)\n flat = probs.reshape(-1)\n max_k2 = np.argsort(flat)[::-1][:k]\n word_inds = max_k2//k\n next_chars_inds = max_k2%k\n\n oldprobs = flat[max_k2]\n #print(oldprobs)\n\n new_words = []\n new_inp = []\n for i, word_ind in enumerate(word_inds):\n next_char = max_k[word_ind][next_chars_inds[i]]\n if next_char == char2idx['END']:\n #print(\"HIT AN END at word {}\".format(word_ind))\n WORDS.append((words[word_ind], oldprobs[i]))\n #the_word = words[word_ind]\n #return ''.join([idx2char[i] for i in the_word])\n new_inp.append(torch.LongTensor([next_char]).to(device))\n word = words[word_ind][:]\n word = word + [next_char]\n new_words.append(word)\n words = new_words[:]\n\n if model.mode == 'LSTM':\n h1, h2 = hidden\n h1, h2 = h1[0][word_inds].view(1, k, -1), h2[0][word_inds].view(1, k, -1)\n hidden = h1, h2\n else:\n hidden = hidden[0][word_inds].view(1, k, -1)\n\n\n inp = pack(new_inp)\n\n return [''.join([idx2char[i] for i in word if i != char2idx['END']]) for word in words], oldprobs","def generate_conll2003_embeddings():\n glove_embedding = get_glove_embedding()\n\n word2index = {}\n idx2word = {}\n embed_array = []\n\n word2index[\"<pad>\"] = 1\n embed_array.append(init_embedding())\n\n word2index[\"<unk>\"] = 0\n embed_array.append(init_embedding())\n\n data = []\n with open(TRAIN_DATA_PATH, \"r\") as f:\n for line in f:\n data.append(json.loads(line))\n\n idx = 2\n\n for sample in tqdm(data, total=len(data)):\n words = sample[\"tokens\"]\n\n for w in words:\n w = w.lower()\n\n # if word is not present in dictionary, add to dictionary and append embedding vector\n if w not in word2index.keys():\n word2index[w] = idx\n idx += 1\n if w not in glove_embedding.keys():\n ev = init_embedding()\n else:\n ev = glove_embedding[w]\n\n embed_array.append(ev)\n\n else:\n continue\n\n # save embeddings\n embed_array = np.vstack(embed_array)\n np.save(EMBD_OUTPUT_PATH, embed_array)\n\n # save dictionary\n print(\"Dicitionary Size: \", len(word2index))\n with open(DICTIONARY_OUTPUT_PATH, \"w\") as f:\n json.dump(word2index, f)","def _embed(self):\n with tf.variable_scope('word_embedding'):\n self.pretrained_word_mat = tf.get_variable(\"word_emb_mat\",\n [self.vocab.word_size() - 2, self.vocab.word_embed_dim],\n dtype=tf.float32,\n initializer=tf.constant_initializer(\n self.vocab.word_embeddings[2:],\n dtype=tf.float32),\n trainable=False)\n self.word_pad_unk_mat = tf.get_variable(\"word_unk_pad\",\n [2, self.pretrained_word_mat.get_shape()[1]],\n dtype=tf.float32,\n initializer=tf.constant_initializer(\n self.vocab.word_embeddings[:2],\n dtype=tf.float32),\n trainable=True)\n\n self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)\n self.p_emb = tf.nn.embedding_lookup(self.word_mat, self.p)\n self.q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)","def make_idx2word():\n idx2word = {}\n d = train_data.shared['word2idx']\n for word, idx in d.items():\n print(word)\n idx2word[idx] = word\n if config.use_glove_for_unk:\n d2 = train_data.shared['new_word2idx']\n for word, idx in d2.items():\n print(word)\n idx2word[idx+len(d)] = word\n return idx2word","def build_doc_sense_vec(self):\n\t\twith codecs.open(self.vocab_file, encoding='utf-8', mode='r') as infile:\n\t\t\tline = infile.readline()\n\t\t\ti = 0\n\t\t\twhile line:\n\t\t\t\tword = line.split()[0]\n\t\t\t\tif not self.word2IdVocabulary.has_key(word):\n\t\t\t\t\t# print i, word\n\t\t\t\t\t# else:\n\t\t\t\t\tself.word2IdVocabulary[word] = i\n\t\t\t\tif not self.id2WordVocabulary.has_key(i):\n\t\t\t\t\tself.id2WordVocabulary[i] = word\n\t\t\t\tline = infile.readline()\n\t\t\t\ti += 1\n\t\t\tself.vocab_num = len(self.word2IdVocabulary)\n\t\t\tprint \"vocabulary number:\" + str(self.vocab_num)\n\n\t\twith codecs.open(self.vec_file, encoding='utf-8', mode='r') as vecfile:\n\t\t\twith codecs.open(self.vec_out_file, encoding='utf-8', mode='a+') as vec_outfile:\n\n\t\t\t\tfor i, line in enumerate(vecfile):\n\t\t\t\t\tif i % 10000 == 0:\n\t\t\t\t\t\tprint i\n\t\t\t\t\t# if i > 72:\n\t\t\t\t\t# \tbreak\n\t\t\t\t\tif i == 0:\n\t\t\t\t\t\ta, b, c = map(int, line.split()[:3])\n\t\t\t\t\t\tprint('Number of sememes: {}\\n'\n\t\t\t\t\t\t\t 'Number of words: {}\\n'\n\t\t\t\t\t\t\t 'Dimension of vectors: {}'.format(a, b, c))\n\t\t\t\t\telif i > 462667:\n\t\t\t\t\t\tsline = line.strip('\\n').split()\n\t\t\t\t\t\tword = sline[0]\n\t\t\t\t\t\tvector_list = []\n\t\t\t\t\t\tvector_list.append(sline[1:])\n\t\t\t\t\t\tvector_array = np.array(vector_list)\n\t\t\t\t\t\tword_id = self.word2IdVocabulary[word]\n\t\t\t\t\t\tif not self.vectors.has_key(word_id):\n\t\t\t\t\t\t\tself.vectors[word_id] = vector_array\n\t\t\t\t\t\t# vector_mean = np.mean(vector_array, axis=0)\n\t\t\t\t\t\tif not self.vector_mean.has_key(word_id):\n\t\t\t\t\t\t\tself.vector_mean[word_id] = vector_array\n\t\t\t\t\t\t# vec_outfile.write(line)\n\t\t\t\t\telif i > 462887:\n\t\t\t\t\t\tbreak\n\t\t\t\t\telse:\n\t\t\t\t\t\tsline = line.strip('\\n').split()\n\t\t\t\t\t\tword = sline[0]\n\t\t\t\t\t\tsense_num = int(sline[1])\n\t\t\t\t\t\tvectors = sline[2:sense_num*c+2] # (sense_num*c+2)\n\t\t\t\t\t\tvector_list = []\n\t\t\t\t\t\tfor start in range(0, len(vectors), c):\n\t\t\t\t\t\t\tvector_list.append(list(map(float, vectors[start: start+c])))\n\t\t\t\t\t\tvector_array = np.array(vector_list)\n\t\t\t\t\t\tword_id = self.word2IdVocabulary[word]\n\t\t\t\t\t\tif not self.vectors.has_key(word_id):\n\t\t\t\t\t\t\tself.vectors[word_id] = vector_array\n\t\t\t\t\t\tvector_mean = np.mean(vector_array, axis=0)\n\t\t\t\t\t\tif not self.vector_mean.has_key(word_id):\n\t\t\t\t\t\t\tself.vector_mean[word_id] = vector_mean\n\t\t\t\t\t\t'''j = 0\n\t\t\t\t\t\tfor each_sense_vec in vector_array:\n\t\t\t\t\t\t\tif len(vector_array) > 1:\n\t\t\t\t\t\t\t\tnew_line = word + '_' + str(j) + ' ' + np.array2string(each_sense_vec, max_line_width=2000,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tformatter={'float_kind': lambda x: '%6f' % x})[1:-1] + '\\n'\n\t\t\t\t\t\t\t\tj += 1\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tnew_line = word + ' ' + np.array2string(each_sense_vec, max_line_width=2000,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t formatter={'float_kind': lambda\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t x: '%6f' % x})[1:-1] + '\\n'\n\n\t\t\t\t\t\t\tvec_outfile.write(new_line)'''\n\n\t\twith codecs.open(self.doc_file, encoding='utf-8', mode='r') as docfile:\n\t\t\twith codecs.open(self.doc_out_file, encoding='utf-8', mode='a+') as doc_outfile:\n\t\t\t\twith codecs.open(self.vec_out_file_bydoc, encoding='utf-8', mode='a+') as vec_outfile_bydoc:\n\t\t\t\t\tprint \"Processing document file......\"\n\t\t\t\t\tline = docfile.readline().strip('\\n')\n\t\t\t\t\twhile line:\n\t\t\t\t\t\twords = line.split()\n\t\t\t\t\t\tnew_words = [x for x in words]\n\t\t\t\t\t\tfor i in range(len(words)):\n\t\t\t\t\t\t\tword_id = self.word2IdVocabulary[words[i]]\n\t\t\t\t\t\t\tsense_vecs = self.vectors[word_id]\n\t\t\t\t\t\t\tsense_num = len(sense_vecs)\n\t\t\t\t\t\t\tif sense_num > 1:\n\t\t\t\t\t\t\t\tcontext_words = []\n\t\t\t\t\t\t\t\tfor x in range(i-int(self.context_num), i+int(self.context_num)+1):\n\t\t\t\t\t\t\t\t\tif x != i and 0 <= x < len(words):\n\t\t\t\t\t\t\t\t\t\tcontext_words.append(words[x])\n\t\t\t\t\t\t\t\tsense_index = self.select_attention(context_words, sense_vecs)\n\t\t\t\t\t\t\t\tword_vec_i = sense_vecs[sense_index]\n\t\t\t\t\t\t\t\tnew_wordi = words[i] + '_' + str(sense_index)\n\t\t\t\t\t\t\t\tself.vector_word_doc[new_wordi.encode('utf-8')] = word_vec_i\n\t\t\t\t\t\t\t\tnew_words[i] = new_wordi\n\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tword_vec_i = sense_vecs[0]\n\t\t\t\t\t\t\t\tself.vector_word_doc[words[i].encode('utf-8')] = word_vec_i\n\t\t\t\t\t\t\tvec_outfile_bydoc.write(new_words[i] + ' ' + np.array2string(word_vec_i, max_line_width=2000,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t formatter={'float_kind': lambda x: '%6f' % x})[1:-1] + '\\n')\n\n\t\t\t\t\t\tdoc_outfile.write(' '.join(new_words) + '\\n')\n\n\t\t\t\t\t\tline = docfile.readline()\n\n\t\treturn self.vector_word_doc","def visualise_embedding(embedding_dir, perplexity, learning_rate, image_path_name, label_mapping):\n tsne = TSNE(\n n_components=2,\n random_state=0,\n perplexity=perplexity,\n learning_rate=learning_rate,\n n_iter=20000\n )\n\n with open(embedding_dir, 'r') as emb_file:\n embedding_list = emb_file.readlines()\n\n print('Number of subword units: {}'.format(len(embedding_list)))\n\n embedding_dict = {}\n vector_emb = []\n subword_labels = []\n # TODO: Make this a clean solution\n # Start at 2 to skip the random </s> character which is coming through (may need to be 3 for Georgian)\n for embedding in embedding_list[2:]:\n segmented_embedding = embedding.split()\n subword_labels.append(label_mapping[segmented_embedding[0]])\n embedding_vector = [float(dim) for dim in segmented_embedding[1:]]\n vector_emb.append(embedding_vector)\n embedding_dict[segmented_embedding[0]] = embedding_vector\n\n emb_2d = tsne.fit_transform(vector_emb)\n\n datapoint_indices = range(len(emb_2d))\n fig, ax = plt.subplots()\n for i, subword_label in zip(datapoint_indices, subword_labels):\n ax.scatter(emb_2d[i, 0], emb_2d[i, 1], c='c', label=subword_label)\n\n\n for i, subword_label in enumerate(subword_labels):\n ax.annotate(subword_label, (emb_2d[i, 0], emb_2d[i, 1]))\n\n plt.savefig(image_path_name)\n return embedding_dict","def load_word2vec(emb_path, id_to_word, word_dim, old_weights):\n new_weights = old_weights\n print('Loading pretrained embeddings from {}...'.format(emb_path))\n pre_trained = {}\n emb_invalid = 0\n for i, line in enumerate(codecs.open(emb_path, 'r', 'utf-8')):\n line = line.rstrip().split()\n if len(line) == word_dim + 1:\n pre_trained[line[0]] = np.array(\n [float(x) for x in line[1:]]\n ).astype(np.float32)\n else:\n emb_invalid += 1\n if emb_invalid > 0:\n print('WARNING: %i invalid lines' % emb_invalid)\n c_found = 0\n c_lower = 0\n c_zeros = 0\n n_words = len(id_to_word)\n # Lookup table initialization\n for i in range(n_words):\n word = id_to_word[i]\n if word in pre_trained:\n new_weights[i] = pre_trained[word]\n c_found += 1\n elif word.lower() in pre_trained:\n new_weights[i] = pre_trained[word.lower()]\n c_lower += 1\n elif re.sub('\\d', '0', word.lower()) in pre_trained:\n new_weights[i] = pre_trained[\n re.sub('\\d', '0', word.lower())\n ]\n c_zeros += 1\n print('Loaded %i pretrained embeddings.' % len(pre_trained))\n print('%i / %i (%.4f%%) words have been initialized with '\n 'pretrained embeddings.' % (\n c_found + c_lower + c_zeros, n_words,\n 100. * (c_found + c_lower + c_zeros) / n_words)\n )\n print('%i found directly, %i after lowercasing, '\n '%i after lowercasing + zero.' % (\n c_found, c_lower, c_zeros\n ))\n return new_weights","def __init__(self, vocab, embed_size=512, dropout_rate=0.1, max_len=200):\n super(DecoderEmbeddings, self).__init__()\n pad_token_idx = 0 #vocab.tokenizer.ids_to_tokens[0]\n assert vocab.tokenizer.ids_to_tokens[0] == '[PAD]'\n self.embeddings = nn.Embedding(len(vocab.tokenizer.ids_to_tokens), embed_size, padding_idx=pad_token_idx)\n self.positional_encoding = PositionalEncoding(d_model=embed_size, dropout=dropout_rate, max_len=max_len)","def process_input_sentence(sess, char_dict, model_settings, model_vars, sentence):\n chars = list(sentence.strip())\n cids = char_dict.chars2cids(chars)\n \n in_embedding = np.eye(model_settings['input_classes'])\n one_hot = in_embedding[cids]\n one_hot_by_t = np.expand_dims(one_hot, 1)\n\n probs = process_sentence(sess, model_settings, model_vars, one_hot_by_t)\n labels = viterbi(probs)\n words = char_dict.chars2words(chars, labels)\n print('|'.join(words))","def _documents2vectors(\n self,\n dm: int = 1,\n vector_size: int = 2 ** 7,\n window: int = 2 ** 3,\n alpha: float = 2 ** -6,\n min_alpha: float = 2 ** -11,\n min_count: int = 3,\n workers: int = 2 ** 2,\n epochs: int = 2 ** 7,\n dm_mean: int = 0,\n dm_concat: int = 0,\n dm_tag_count: int = 2 ** 5,\n dbow_words: int = 1,\n ):\n\n file_name = join(PARENT_BASE_DIR, '.files/model.model')\n if isfile(file_name):\n model = gensim_document_to_vector.load(file_name)\n else:\n data = self.data\n data = data[0]\n tags = self.tags\n tags = tags[0]\n corpus_keywords_list = []\n\n for doc in data:\n corpus_keywords_list.append(self.kywrds.by_frequency(doc))\n _data = []\n for doc in data:\n _doc = []\n for _sent in doc:\n for wrd in _sent:\n _doc.append(wrd)\n _data.append(_doc)\n data = _data\n\n documents = []\n for i in range(len(data)):\n keywords_list = corpus_keywords_list[i]\n if self.exist_tags:\n for tag in tags[i]:\n keywords_list.append(tag)\n documents.append(TaggedDocument(data[i], keywords_list))\n\n model = gensim_document_to_vector(\n documents=documents,\n dm=dm,\n vector_size=vector_size,\n window=window,\n alpha=alpha,\n min_alpha=min_alpha,\n min_count=min_count,\n workers=workers,\n epochs=epochs,\n dm_mean=dm_mean,\n dm_concat=dm_concat,\n dm_tag_count=dm_tag_count,\n dbow_words=dbow_words,\n )\n model.save(file_name)\n return model","def __glove_embed__(sequence, model):\n embedded = []\n for word in sequence:\n embedded.append(model[word])\n return embedded","def model(input_shape, model,model2,model3, word_to_index):\n \n ### START CODE HERE ###\n # Define sentence_indices as the input of the graph.\n # It should be of shape input_shape and dtype 'int32' (as it contains indices, which are integers).\n sentence_indices = Input(input_shape,dtype='int32')\n \n # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\n embedding_layer, ignored_words = pretrained_embedding_layer(model,model2,model3,word_to_index,300)\n \n # Propagate sentence_indices through your embedding layer\n # (See additional hints in the instructions).\n embeddings = embedding_layer(sentence_indices)\n \n # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\n # The returned output should be a batch of sequences.\n X = LSTM(units=128,input_shape=input_shape,return_sequences=True)(embeddings)\n # Add dropout with a probability of 0.5\n X = Dropout(rate=0.5)(X)\n # Propagate X trough another LSTM layer with 128-dimensional hidden state\n # The returned output should be a single hidden state, not a batch of sequences.\n X = LSTM(units=128,input_shape=input_shape,return_sequences=False)(X)\n # Add dropout with a probability of 0.5\n X = Dropout(rate=0.5)(X)\n # Propagate X through a Dense layer with 5 units\n X = Dense(units=num_classes)(X)\n# X = Dense(6, activation='softmax')\n # Add a softmax activation\n# print(X)\n# print(type(X))\n# print(X.shape)\n# print(sum(X))\n X = Activation('softmax')(X)\n \n # Create Model instance which converts sentence_indices into X.\n model = Model(inputs=sentence_indices,outputs=X)\n \n return model","def input_dim(use_word_emb, embedding_dim, word_emb_enc_dim):\n return embedding_dim + use_word_emb * word_emb_enc_dim","def sentences_2_idxs(self):\n fo_pos = open(self.config.parsed_train_file_pos, 'w')\n fo_neg = open(self.config.parsed_train_file_neg, 'w')\n self.load_dicts()\n labels = pd.read_csv(self.config.train_file, usecols=[\"target\"])\n\n labels = list(labels.values[:, 0])\n questions = pd.read_csv(self.config.train_file,\n usecols=[\"question_text\"], index_col=False)\n unk_idx = self.word2idx.get(self.config.unknown_token)\n\n for label, quest in zip(labels, questions.question_text):\n tokens = preprocess_text(quest)\n\n if self.config.include_unknown:\n idxs = [self.word2idx.get(token, unk_idx) for token in\n tokens]\n else:\n idxs = [self.word2idx.get(token) for token in tokens]\n idxs = [idx for idx in idxs if idx]\n out_line = (str(\" \".join(str(num) for num in idxs)) + \"\\n\")\n if label == 1:\n fo_pos.write(out_line)\n else:\n fo_neg.write(out_line)","def parse_input(input_data, dictionary, model):\n vec_text = TextBlob(input_data).words.lower().lemmatize()\n vec_bow = dictionary.doc2bow(vec_text)\n return model[vec_bow]","def forward(self, input):\n \n ## A4 code\n # print(\"vocab_len\",len(self.vocab.char2id))\n # print(\"input_size\",input.size()) \n \n char_embeddings = self.embedding(input)\n max_sent_lenth,batch_size,max_word_lenth,char_e_size = char_embeddings.size() \n char_embeddings=char_embeddings.view(max_sent_lenth*batch_size,max_word_lenth,char_e_size)\n conv_out = self.CNN(char_embeddings.permute(0,2,1))\n highway_out = self.Highway(conv_out)\n word_emb = self.dropout(highway_out)\n output = word_emb.contiguous().view(max_sent_lenth, batch_size, self.embed_size)\n return output\n ## End A4 code\n\n ### YOUR CODE HERE for part 1j\n\n\n ### END YOUR CODE","def build_seq_embeddings(self):\n with tf.variable_scope(\"seq_embedding\"), tf.device(\"/cpu:0\"):\n embedding_map = tf.get_variable(\n name=\"map\",\n shape=[self.config.vocab_size, self.config.word_embedding_size],\n initializer=self.initializer)\n \n # We need to store the normalized lookup table for efficient mapping of embedding vectors to closest words\n self.normed_embedding_map = tf.nn.l2_normalize(embedding_map, dim=1)\n \n seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs) \n # seq_embeddings has the shape (batch_size, sequence_length, sentence_length, embedding_size)\n # meaning, for each index in input_seqs (with shape (batch_size, sequence_length, sentence_length)) it stores an embedding vector\n\n #print('Shape seq_embeddings: ' + str(seq_embeddings.get_shape()))\n\n self.seq_embeddings = seq_embeddings","def _add_pre_trained_embedding(self):\n\n if self.embedding_type['type'] == 'glove':\n self.logging.info('use pre-trained glove word2vec')\n # a. load pre trained glove\n GLOVE_DIR = '../data/glove_pretrained/glove.6B'\n glove_suffix_name = 'glove.6B.' + str(self.embedding_size) + 'd.txt'\n import os\n import numpy as np\n\n embeddings_index = {}\n f = open(os.path.join(GLOVE_DIR, glove_suffix_name)) # 'glove.6B.100d.txt'))\n for line in f:\n values = line.split()\n word = values[0]\n coefs = np.asarray(values[1:], dtype='float32')\n embeddings_index[word] = coefs\n f.close()\n\n self.logging.info('')\n self.logging.info('Found %s word vectors.' % len(embeddings_index))\n\n # b. compute embedding matrix\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\n cnt = 0\n for word, i in self.word_index.items():\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector # words not found in embedding index will be all-zeros.\n else:\n # self.logging.info('token in train missing in word2vec: ' + str(word))\n cnt += 1\n self.logging.info('total tokens missing: ' + str(cnt) + ' / ' + str(len(self.word_index)))\n\n # c. build embedding layer\n from keras.layers import Embedding\n embedding_layer = Embedding(len(self.word_index) + 1,\n self.embedding_size,\n weights=[embedding_matrix],\n input_length=self.maxlen,\n trainable=False)\n\n elif self.embedding_type['type'] == 'gensim':\n self.logging.info('use pre-trained gensim word2vec')\n\n import gzip\n import gensim\n from keras.layers import Embedding\n import numpy as np\n\n # fname = '../data/word2vec_pretrained/motors/d_300_k_712904_w_6_e_60_v_motors'\n # fname = '../data/word2vec_pretrained/fashion/d_300_k_1341062_w_6_e_70_v_fashion'\n\n self.logging.info('load word2vec path: ' + str(self.embedding_type['path']))\n model = gensim.models.Word2Vec.load(self.embedding_type['path'])\n pretrained_weights = model.wv.syn0\n vocab_size, vector_dim = pretrained_weights.shape\n\n method = 3\n if method == 1:\n self.logging.info('word2vec attempt to fit into embedding layer - middle complex')\n # convert the wv word vectors into a numpy matrix that is suitable for insertion\n # into our TensorFlow and Keras models\n\n embedding_matrix = np.zeros((len(model.wv.vocab), vector_dim))\n for i in range(len(model.wv.vocab)):\n embedding_vector = model.wv[model.wv.index2word[i]]\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector\n\n embedding_layer = Embedding(input_dim=embedding_matrix.shape[0],\n output_dim=embedding_matrix.shape[1],\n # input_length=self.maxlen,\n weights=[embedding_matrix],\n trainable=False)\n elif method == 2:\n self.logging.info('word2vec simple embedding matching - simple complex')\n embedding_layer = Embedding(input_dim=vocab_size,\n output_dim=vector_dim,\n input_length=self.maxlen,\n weights=[pretrained_weights],\n trainable=False)\n elif method == 3:\n\n self.logging.info('word2vec match using word_index from keras tokenizer - as used in glove match above')\n # b. compute embedding matrix\n\n # sd = 1 / np.sqrt(len(self.word_index) + 1)\n # embedding_matrix = np.random.normal(0, scale=sd, size=(len(self.word_index) + 1, self.embedding_size))\n\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\n cnt = 0\n for word, i in self.word_index.items():\n if word in model.wv:\n embedding_vector = model.wv[word]\n embedding_matrix[i] = embedding_vector\n else:\n # self.logging.info('token in train missing in word2vec: ' + str(word))\n cnt += 1\n self.logging.info('total tokens missing: ' + str(cnt))\n\n\n # c. build embedding layer\n from keras.layers import Embedding\n embedding_layer = Embedding(len(self.word_index) + 1,\n self.embedding_size,\n weights=[embedding_matrix],\n input_length=self.maxlen,\n trainable=False)\n else:\n raise ValueError('unknown method value')\n\n else:\n raise ValueError('unknown embedding type')\n self.logging.info('create glove pre-trained embedding: ' + str(self.embedding_size))\n return embedding_layer","def load_embeddings(emb_file, oovs=[], pads=[], sep=' ', lower=False, case_dim=True):\n word2emb = {}\n word2idx = {}\n\n # read and store all word vectors\n for line in open(emb_file, errors = 'ignore', encoding = 'utf-8'):\n try:\n fields = line.strip().split(sep)\n word = fields[0]\n vec = np.asarray(fields[1:], dtype='float32')\n if case_dim:\n is_upper = float(word[0].isupper())\n vec = np.insert(vec, 0, is_upper, axis=0)\n if lower:\n word = word.lower()\n word2emb[word] = vec\n if word not in word2idx:\n word2idx[word] = len(word2idx)\n except Exception as e:\n print('[WARNING] Exception in `load_embeddings`:', e)\n\n # get dimensions from the last vector added\n emb_dim = word2emb[word].shape[0]\n\n # add custom embeddings for special characters\n mu = 0\n sigma = 0.01\n\n for word in pads:\n if word not in word2idx:\n if case_dim:\n vec = npr.normal(mu, sigma, emb_dim-1)\n vec = np.insert(vec, 0, 0., axis=0)\n else:\n vec = npr.normal(mu, sigma, emb_dim)\n word2emb[word] = vec\n word2idx[word] = len(word2idx)\n else:\n print('[WARNING] Padding item ' + word + ' has an embedding vector')\n\n for word in oovs:\n if word not in word2idx:\n if case_dim:\n vec = npr.normal(mu, sigma, emb_dim-1)\n is_upper = float(word[0].isupper())\n vec = np.insert(vec, 0, is_upper, axis=0)\n else:\n vec = npr.normal(mu, sigma, emb_dim)\n word2emb[word] = vec\n word2idx[word] = len(word2idx)\n else:\n print('[WARNING] OOV alias ' + word + ' has an embedding vector')\n\n # create an embedding matrix\n vocab_size = len(word2emb)\n emb_matrix = np.zeros((vocab_size, emb_dim))\n for word, idx in word2idx.items():\n if word in word2emb:\n vec = word2emb[word]\n if vec is not None and vec.shape[0] == emb_dim:\n emb_matrix[idx] = np.asarray(vec)\n\n # print feedback data and return mappings\n print('[INFO] Embedding vocabulary:', emb_matrix.shape[0], '(lowercase: ' + str(lower) + ')')\n print('[INFO] OOV aliases:', oovs)\n print('[INFO] Padding items:', pads)\n print('[INFO] Embedding dimensions:', emb_dim, '(extra case dimension: ' + str(case_dim) + ')')\n return word2idx, np.asarray(emb_matrix), emb_dim","def get_embed(input_data, vocab_size, embed_dim):\n # todo 需要编程：\n # 1、构建嵌入矩阵的查找表\n lookup_w = tf.Variable(\n initial_value=tf.random_uniform([vocab_size, embed_dim], -1.0, 1.0)\n )\n # 2、获得嵌入输出\n embed = tf.nn.embedding_lookup(params=lookup_w, ids=input_data)\n # [N, n_steps, embed_size]\n return embed","def get_dis_xy(self, volatile):\n # select random word IDs\n bs = 32\n mf = 50\n #mf = 75000 #only choose top mf number of most frequent words\n assert mf <= min(len(self.src_dic), len(self.tgt_dic))\n src_ids = torch.LongTensor(bs).random_(mf)\n tgt_ids = torch.LongTensor(bs).random_(mf)\n\n #use cuda..\n useGPU = True\n if useGPU:\n src_ids = src_ids.cuda()\n tgt_ids = tgt_ids.cuda()\n\n # get word embeddings\n src_emb = self.src_emb(Variable(src_ids, volatile=True))\n tgt_emb = self.tgt_emb(Variable(tgt_ids, volatile=True))\n src_emb = self.mapping(Variable(src_emb.data, volatile=volatile))\n tgt_emb = Variable(tgt_emb.data, volatile=volatile)\n\n # input / target\n x = torch.cat([src_emb, tgt_emb], 0)\n y = torch.FloatTensor(2 * bs).zero_()\n dis_smooth = 0.1 #discriminator smooth predication\n y[:bs] = 1 - dis_smooth\n y[bs:] = dis_smooth\n y = Variable(y.cuda() if useGPU else y)\n\n return x, y","def build_sense_embedding(target_sense_to_id, word_freq, EMBEDDING_DIM):\r\n res = {}\r\n wordvecs = load_glove(EMBEDDING_DIM)\r\n \r\n for target_sense_list in target_sense_to_id:\r\n for key, _ in target_sense_list.items():\r\n sense_vector = np.zeros(EMBEDDING_DIM)\r\n senses = key.split(',')\r\n n = 0\r\n for sensekey in senses:\r\n #print(sensekey) \r\n if '/' in sensekey:\r\n continue\r\n sense_synset = sc2ss(sensekey)\r\n if sense_synset:\r\n sense_vector += build_sense_vector(sense_synset, word_freq, wordvecs)\r\n n += 1\r\n if n != 0:\r\n res[key] = sense_vector/n\r\n return res","def glove_embedding(self, word_index, padded, embedding_matrix, part='q'):\n print(\"*\" * 50, \"Start Glove embedding process\", \"*\" * 50)\n start_time = time()\n\n\n MAX_SEQ_LEN = None\n if part == 'q':\n MAX_SEQ_LEN = self.MAX_Q_SEQ_LEN\n elif part == 'a':\n MAX_SEQ_LEN = self.MAX_A_SEQ_LEN\n else:\n print(f\"Please indicate you want embedding question part or answer part\")\n\n\n input_layer = Input(shape=(MAX_SEQ_LEN,), dtype='int32')\n embedding_layer = Embedding(input_dim = len(word_index) + 1,\n output_dim = self.EMBEDDING_DIM,\n weights=[embedding_matrix],\n input_length=MAX_SEQ_LEN,\n trainable=False)(input_layer)\n # (number of sample, MAX_SEQ_LEN, EMBEDING_DIM)\n model = Model(inputs=input_layer, outputs=embedding_layer)\n model.compile('rmsprop', 'mse')\n output_array = model.predict(padded)\n\n cost_time = round((time() - start_time), 4)\n print(\"*\" * 40, \"End Glove embedding() with {} seconds\".format(cost_time), \"*\" * 40, end='\\n\\n')\n\n return output_array, embedding_layer","def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=None,\n learn_doctags=True, learn_words=True, learn_hidden=True,\n word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None):\n if word_vectors is None:\n word_vectors = model.wv.syn0\n if word_locks is None:\n word_locks = model.syn0_lockf\n if doctag_vectors is None:\n doctag_vectors = model.docvecs.doctag_syn0\n if doctag_locks is None:\n doctag_locks = model.docvecs.doctag_syn0_lockf\n\n word_vocabs = [model.wv.vocab[w] for w in doc_words if w in model.wv.vocab and\n model.wv.vocab[w].sample_int > model.random.rand() * 2 ** 32]\n\n for pos, word in enumerate(word_vocabs):\n reduced_window = model.random.randint(model.window) # `b` in the original doc2vec code\n start = max(0, pos - model.window + reduced_window)\n window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start)\n word2_indexes = [word2.index for pos2, word2 in window_pos if pos2 != pos]\n l1 = np_sum(word_vectors[word2_indexes], axis=0) + np_sum(doctag_vectors[doctag_indexes], axis=0)\n count = len(word2_indexes) + len(doctag_indexes)\n if model.cbow_mean and count > 1:\n l1 /= count\n neu1e = train_cbow_pair(model, word, word2_indexes, l1, alpha,\n learn_vectors=False, learn_hidden=learn_hidden)\n if not model.cbow_mean and count > 1:\n neu1e /= count\n if learn_doctags:\n for i in doctag_indexes:\n doctag_vectors[i] += neu1e * doctag_locks[i]\n if learn_words:\n for i in word2_indexes:\n word_vectors[i] += neu1e * word_locks[i]\n\n return len(word_vocabs)","def train_sentence_dbow(model, sentence, lbls, alpha, work=None, train_words=True, train_lbls=True):\n neg_labels = []\n if model.negative:\n # precompute negative labels\n neg_labels = zeros(model.negative + 1)\n neg_labels[0] = 1.0\n\n for label in lbls:\n if label is None:\n continue # OOV word in the input sentence => skip\n for word in sentence:\n if word is None:\n continue # OOV word in the input sentence => skip\n train_sg_pair(model, word, label, alpha, neg_labels, train_words, train_lbls)\n\n return len([word for word in sentence if word is not None])","def get_model_bicond_sepembed(batch_size, max_seq_length, input_size, hidden_size, target_size,\n vocab_size, pretrain, tanhOrSoftmax, dropout):\n\n # batch_size x max_seq_length\n inputs = tf.placeholder(tf.int32, [batch_size, max_seq_length])\n inputs_cond = tf.placeholder(tf.int32, [batch_size, max_seq_length])\n\n cont_train = True\n if pretrain == \"pre\": # continue training embeddings or not. Currently works better to continue training them.\n cont_train = False\n embedding_matrix = tf.Variable(tf.random_uniform([vocab_size, input_size], -0.1, 0.1), # input_size is embeddings size\n name=\"embedding_matrix\", trainable=cont_train)\n embedding_matrix_cond = tf.Variable(tf.random_uniform([vocab_size, input_size], -0.1, 0.1),\n name=\"embedding_matrix\", trainable=cont_train)\n\n\n # batch_size x max_seq_length x input_size\n embedded_inputs = tf.nn.embedding_lookup(embedding_matrix, inputs)\n embedded_inputs_cond = tf.nn.embedding_lookup(embedding_matrix_cond, inputs_cond)\n\n\n # [batch_size x inputs_size] with max_seq_length elements\n # fixme: possibly inefficient\n # inputs_list[0]: batch_size x input[0] <-- word vector of the first word\n inputs_list = [tf.squeeze(x) for x in\n tf.split(1, max_seq_length, embedded_inputs)]\n inputs_cond_list = [tf.squeeze(x) for x in\n tf.split(1, max_seq_length, embedded_inputs_cond)]\n\n drop_prob = None\n if dropout:\n drop_prob = 0.1\n lstm_encoder = Encoder(rnn_cell.BasicLSTMCell, input_size, hidden_size, drop_prob, drop_prob)\n\n start_state = tf.zeros([batch_size, lstm_encoder.state_size])\n\n ### FORWARD\n\n # [h_i], [h_i, c_i] <-- LSTM\n # [h_i], [h_i] <-- RNN\n fw_outputs, fw_states = lstm_encoder(inputs_list, start_state, \"LSTM\")\n\n # running a second LSTM conditioned on the last state of the first\n fw_outputs_cond, fw_states_cond = lstm_encoder(inputs_cond_list, fw_states[-1],\n \"LSTMcond\")\n\n fw_outputs_fin = fw_outputs_cond[-1]\n\n ### BACKWARD\n bw_outputs, bw_states = lstm_encoder(inputs_list[::-1], start_state, \"LSTM_bw\")\n bw_outputs_cond, bw_states_cond = lstm_encoder(inputs_cond_list[::-1], bw_states[-1],\n \"LSTMcond_bw\")\n bw_outputs_fin = bw_outputs_cond[-1]\n\n outputs_fin = tf.concat(1, [fw_outputs_fin, bw_outputs_fin])\n\n\n if tanhOrSoftmax == \"tanh\":\n model = Projector(target_size, non_linearity=tf.nn.tanh, bias=True)(outputs_fin) # tf.nn.softmax\n else:\n model = Projector(target_size, non_linearity=tf.nn.softmax, bias=True)(outputs_fin) # tf.nn.softmax\n\n return model, [inputs, inputs_cond]","def make_embedding(path, words, indices):\n #root = '/'.join(path.split('/')[0:-1])\n #all_paths = [root+'/'+x for x in os.listdir(root)] #'/'.join(path.split('/')[0:-1]))\n #for path in all_paths:\n vec_path = 'data/'+path.split('/')[-1]+'_'+mode\n print(vec_path)\n if os.path.exists(vec_path+'.npy'):\n np_vecs = np.load(vec_path+'.npy')\n else:\n words_len = len(words)\n vecs = []\n if mode == 'word':\n f = load_model('wiki.en.bin')\n for i, w in enumerate(words):\n if mode == 'word':\n vec = f.get_word_vector(w)\n else:\n vec = eye[indices[w]]\n vecs.append(vec) \n if i % 10000 == 0:\n print(\"{} / {}\".format(i, words_len))\n np_vecs = np.asarray(vecs, dtype=np.int8)\n np.save(vec_path, np_vecs)\n return np_vecs","def model_extract_document_embedding(self):\n input_ids = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\"ids\")\n attention_mask = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\"att\")\n token = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\"tok\")\n\n # Embedding :\n if self.method_embedding == 'CamemBERT':\n Camembert_model = transformers.TFCamembertModel.from_pretrained(\"jplu/tf-camembert-base\")\n x = Camembert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'FlauBERT':\n # lr = 0.00001\n Flaubert_model = transformers.TFFlaubertModel.from_pretrained(\"jplu/tf-flaubert-base-uncased\")\n x = Flaubert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'XLM-RoBERTa':\n # lr = 0.00001\n XLMRoBERTa_model = transformers.TFXLMRobertaModel.from_pretrained(\"jplu/tf-xlm-roberta-base\")\n x = XLMRoBERTa_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'RoBERTa':\n # Experience Test path weights :\n PATH = '/kaggle/input/tf-roberta/'\n config = transformers.RobertaConfig.from_pretrained(PATH + 'config-roberta-base.json')\n Roberta_model = transformers.TFRobertaModel.from_pretrained(PATH + 'pretrained-roberta-base.h5',\n config=config)\n # Sinon :\n # Roberta_model = transformers.TFRobertaModel.from_pretrained('roberta-base')\n x = Roberta_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'BERT':\n BERT_model = transformers.TFBertModel.from_pretrained('bert-base-uncased')\n x = BERT_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n else:\n logger.critical(\"unknown embedding method name : '{}'\".format(self.method_embedding))\n\n # word vectors shape : (None, maxlen, 768)\n x = x[0]\n cls_token = x[:, 0, :]\n\n model = tf.keras.models.Model(inputs=[input_ids, attention_mask, token], outputs=cls_token)\n return model","def get_vector(word, model):\n return model.wv[word]","def get_embeddings(self, in_data):\n context, da = in_data\n if self.fixed_divide:\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=True)\n else:\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=False)\n\n # Shubhangi: what this step essentially does is it replaces the context words by their token, with UNK as default.\n # again , we don't need this since our context data is essentially vectors therefore commenting this out\n # similary we don't need context embedding , that's exactly what context is already .\n\n # context_emb = []\n context_emb = [float(parameter[0]) for parameter in context]\n\n # for tok in context[-max_context_len:]:\n # context_emb.append(self.dict_token.get(tok, self.UNK_TOKEN))\n\n # Shubhangi: padding is needed because each context sentence could be of different length ,\n # we don't need to include context in padding as we're going to have a fixed size\n # (max_context_len - len(context)) = 0\n\n\n # padding = [self.UNK_TOKEN] * (max_context_len - len(context))\n\n # Shubhangi: padding might be harmless for now therefore not removing ,\n # essentially what this is doing is concatenating the arrays and sending\n if self.use_div_token:\n return context_emb + [self.DIV_TOKEN] + da_emb\n # return padding + context_emb + [self.DIV_TOKEN] + da_emb\n # return padding + context_emb + da_emb\n return context_emb + da_emb","def id2vec_preprocess(args):\n log = logging.getLogger(\"preproc\")\n log.info(\"Loading docfreq model from %s\", args.docfreq_in)\n df_model = DocumentFrequencies(log_level=args.log_level).load(source=args.docfreq_in)\n coocc_model = Cooccurrences().load(args.input)\n if numpy.any(coocc_model.matrix.data < 0):\n raise ValueError((\"Co-occurrence matrix %s contains negative elements. \"\n \"Please check its correctness.\") % args.input)\n if numpy.any(numpy.isnan(coocc_model.matrix.data)):\n raise ValueError((\"Co-occurrence matrix %s contains nan elements. \"\n \"Please check its correctness.\") % args.input)\n\n try:\n df_meta = coocc_model.get_dep(DocumentFrequencies.NAME)\n if df_model.meta != df_meta:\n raise ValueError((\n \"Document frequency model you provided does not match dependency inside \"\n \"Cooccurrences model:\\nargs.docfreq.meta:\\n%s\\ncoocc_model.get_dep\"\n \"(\\\"docfreq\\\")\\n%s\\n\") % (df_model.meta, df_meta))\n except KeyError:\n pass # There is no docfreq dependency\n\n vs = args.vocabulary_size\n if len(df_model) < vs:\n vs = len(df_model)\n sz = args.shard_size\n if vs < sz:\n raise ValueError(\n \"vocabulary_size=%s is less than shard_size=%s. You should specify a smaller \"\n \"shard_size (e.g. shard_size=%s).\" % (vs, sz, vs))\n vs -= vs % sz\n log.info(\"Effective vocabulary size: %d\", vs)\n df_model = df_model.greatest(vs)\n log.info(\"Sorting the vocabulary...\")\n chosen_words = sorted(df_model.tokens())\n word_indices = {w: i for i, w in enumerate(chosen_words)}\n\n if not os.path.exists(args.output):\n os.makedirs(args.output)\n with open(os.path.join(args.output, \"row_vocab.txt\"), \"w\") as out:\n out.write(\"\\n\".join(chosen_words))\n log.info(\"Saved row_vocab.txt\")\n shutil.copyfile(os.path.join(args.output, \"row_vocab.txt\"),\n os.path.join(args.output, \"col_vocab.txt\"))\n log.info(\"Saved col_vocab.txt\")\n del chosen_words\n\n ccmatrix = extract_coocc_matrix((vs, vs), word_indices, coocc_model)\n\n log.info(\"Planning the sharding...\")\n bool_sums = ccmatrix.indptr[1:] - ccmatrix.indptr[:-1]\n reorder = numpy.argsort(-bool_sums)\n with open(os.path.join(args.output, \"row_sums.txt\"), \"w\") as out:\n out.write(\"\\n\".join(map(str, bool_sums.tolist())))\n log.info(\"Saved row_sums.txt\")\n shutil.copyfile(os.path.join(args.output, \"row_sums.txt\"),\n os.path.join(args.output, \"col_sums.txt\"))\n log.info(\"Saved col_sums.txt\")\n\n log.info(\"Writing the shards...\")\n os.makedirs(args.output, exist_ok=True)\n nshards = vs // args.shard_size\n for row in progress_bar(range(nshards), log, expected_size=nshards):\n for col in range(nshards):\n indices_row = reorder[row::nshards]\n indices_col = reorder[col::nshards]\n shard = ccmatrix[indices_row][:, indices_col].tocoo()\n\n example = tf.train.Example(features=tf.train.Features(feature={\n \"global_row\": _int64s(indices_row),\n \"global_col\": _int64s(indices_col),\n \"sparse_local_row\": _int64s(shard.row),\n \"sparse_local_col\": _int64s(shard.col),\n \"sparse_value\": _floats(shard.data)}))\n\n with open(os.path.join(args.output,\n \"shard-%03d-%03d.pb\" % (row, col)),\n \"wb\") as out:\n out.write(example.SerializeToString())\n log.info(\"Success\")","def embed(self, loader, model):\n print(\" ** Embedding words\")\n\n words = loader.words\n vectors = [model.get_word_vector(word) for word in words]\n\n return [(w, *v) for w, v in zip(words, vectors)]","def data_word2vec_one_label(input_file,\n word2vec_model):\n vocab = dict([(k, v.index) for (k, v) in word2vec_model.wv.vocab.items()])\n\n def _token_to_index(content):\n result = []\n for item in content:\n word2id = vocab.get(item)\n if word2id is None:\n word2id = 0\n result.append(word2id)\n return result\n\n\n with open(input_file) as fin:\n raw_tokens_list_gov = []\n raw_tokens_list_art = []\n test_id_list = []\n content_index_list_gov = []\n content_index_list_art = []\n # labels_list = []\n onehot_labels_list = []\n labels_num_list = []\n total_line = 0\n\n for each_line in fin:\n data = json.loads(each_line)\n ds_art = data['testid']\n ds = ds_art.split(\"_\")[0]\n art = ds_art.split(\"_\")[1][1:-1]\n test_id = ds + \"_\" + art\n features_content_gov = data['gov']\n features_content_art = data['art']\n label = data['label']\n\n test_id_list.append(test_id)\n content_index_list_gov.append(_token_to_index(\n features_content_gov))\n content_index_list_art.append(_token_to_index(\n features_content_art))\n\n raw_tokens_list_gov.append(features_content_gov)\n raw_tokens_list_art.append(features_content_art)\n\n # labels_list.append(label)\n # onehot_labels_list.append(_create_onehot_labels(labels_index,\n # num_labels))\n onehot_labels_list.append(label)\n labels_num = 1\n labels_num_list.append(labels_num)\n total_line += 1\n\n\n class _Data:\n def __init__(self):\n pass\n\n @property\n def number(self):\n return total_line\n\n @property\n def testid(self):\n return test_id_list\n\n @property\n def raw_tokens_gov(self):\n return raw_tokens_list_gov\n\n @property\n def raw_tokens_art(self):\n return raw_tokens_list_art\n\n @property\n def tokenindex_gov(self):\n return content_index_list_gov\n\n @property\n def tokenindex_art(self):\n return content_index_list_art\n\n # @property\n # def labels(self):\n # return labels_list\n\n @property\n def onehot_labels(self):\n return onehot_labels_list\n\n @property\n def labels_num(self):\n return labels_num_list\n\n return _Data()","def __init__(self, directory, dataset, B_SIZE = 32):\n \n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n \n self.directory = directory\n self.dataset = dataset\n self.batch_size = B_SIZE\n self.hidden_dim = 64\n self.embedding_dim = 300\n \n all_data = pickle.load(open(directory + dataset + \"/data.p\", \"rb\"))\n \n self.w2ix = all_data.w2ix\n self.vocab_size = len(self.w2ix) \n \n self.mask_list = []\n self.mask_tokens = [\"<PAD>\", \"<SOS>\", \"<EOS>\", \".\"]\n \n for item in self.mask_tokens:\n \n if item in self.w2ix:\n \n self.mask_list.append(self.w2ix[item])\n \n self.pretrained_embeds = all_data.pretrained_embeds\n \n \n # In[4]:\n \n \n x_train, y_train = zip(*all_data.train)\n x_dev, y_dev = zip(*all_data.dev)\n x_test, y_test = zip(*all_data.test)\n \n print(\"\\nVocab size:\", len(self.w2ix),\n \"\\nTraining size:\", len(y_train),\n \"\\nDev size:\", len(y_dev),\n \"\\nTest size:\", len(y_test))\n \n # In[5]:\n \n self.output_size= len(np.unique(y_train))\n \n print(\"\\nOutput dimension: \", self.output_size, \"\\n\")\n \n \n self.sequence_length = all_data.sequence_length()\n \n if dataset == \"mimicanemia\":\n \n \tself.sequence_length = 2200\n \n print(\"--Sequence length :\", self.sequence_length, \"\\n\")\n \n # In[10]:\n \n from modules.utils import padder\n \n x_train_pad, train_lengths = padder(x_train, pad_len = self.sequence_length)\n x_dev_pad, dev_lengths = padder(x_dev, pad_len = self.sequence_length)\n x_test_pad, test_lengths = padder(x_test, pad_len = self.sequence_length)\n \n \n # In[11]:\n \n x_train_pad = torch.LongTensor(x_train_pad)#.to(device)\n x_dev_pad = torch.LongTensor(x_dev_pad)#.to(device)\n x_test_pad = torch.LongTensor(x_test_pad)#.to(device)\n train_lengths = torch.LongTensor(train_lengths)#.to(device)\n dev_lengths = torch.LongTensor(dev_lengths)#.to(device)\n test_lengths = torch.LongTensor(test_lengths)#.to(device)\n y_train = torch.LongTensor(y_train)#.to(device)\n y_dev = torch.LongTensor(y_dev)#.to(device)\n y_test = torch.LongTensor(y_test)#.to(device)\n \n \n # In[12]:\n \n \n training_prebatch = list(zip(x_train_pad, train_lengths, y_train))\n dev_prebatch = list(zip(x_dev_pad, dev_lengths, y_dev))\n testing_prebatch = list(zip(x_test_pad, test_lengths, y_test))\n \n \n training_prebatch = sorted(training_prebatch, key = lambda x : x[1], reverse = False)\n dev_prebatch = sorted(dev_prebatch, key = lambda x : x[1], reverse = False)\n testing_prebatch = sorted(testing_prebatch, key = lambda x : x[1], reverse = False)\n \n # In[13]:\n \n ### removing sos and eos only sentences\n \n train_prebatch = [x for x in training_prebatch if x[1] > 2]\n dev_prebatch = [x for x in dev_prebatch if x[1] > 2]\n test_prebatch = [x for x in testing_prebatch if x[1] > 2]\n \n \n self.training = DataLoader(train_prebatch, batch_size = self.batch_size, \n shuffle = True, pin_memory = False)\n \n self.development = DataLoader(dev_prebatch, batch_size = self.batch_size, \n shuffle = False, pin_memory = False)\n \n \n self.testing = DataLoader(test_prebatch, batch_size = self.batch_size, \n shuffle = False, pin_memory = False)","def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size, vocab_file):\n print(\"Generating word embedding...\")\n # load word embeddings\n embedding_dict = {}\n with open(emb_file, \"r\", encoding=\"utf-8\") as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = \"\".join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n\n TRANSLATE = {\n \"-lsb-\": \"[\", \"-rsb-\": \"]\", \"-lrb-\": \"(\", \"-rrb-\": \")\", \"-lcb-\": \"{\",\n \"-rcb-\": \"}\", \"-LSB-\": \"[\", \"-RSB-\": \"]\", \"-LRB-\": \"(\", \"-RRB-\": \")\",\n \"-LCB-\": \"{\", \"-RCB-\": \"}\"\n }\n SPECIAL_TOKENS = [\"<NULL>\", \"<UNK>\", \"<S>\", \"</S>\"]\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count","def e_step(self, wordids, wordcts):\n # Declare theta (topic mixtures) of minibatch and list of non-zero indexes\n batch_size = len(wordids)\n theta = np.zeros((batch_size, self.num_topics))\n index = [{} for d in range(batch_size)]\n # Inference\n for d in range(batch_size):\n (thetad, indexd) = self.infer_doc(wordids[d], wordcts[d])\n theta[d, :] = thetad\n index[d] = indexd\n return (theta, index)","def load_embedding(self, glove_dir='glove.6B/'):\n\n f = open(os.path.join(glove_dir, 'glove.6B.100d.txt'))\n for line in f:\n values = line.split()\n word = values[0]\n self.embeddings_index[word] = np.asarray(values[1:], dtype='float32')\n f.close()","def __init__(self, docs, dict_path= 'wordindex.npy'):\n super(NNModel, self).__init__()\n self.stopwords += self.additional_stopwords\n self.words = set(['OOB', 'UNK']) # OOB for out of boundary, UNK for unknown words\n self.docs = []\n\n for doc in docs:\n datum = []\n for word in self.cut_words(doc):\n self.words.add(word)\n datum.append(word)\n self.docs.append(datum)\n\n self.words = list(self.words)\n self.word2idx = dict([(self.words[i], i) for i in range(len(self.words))])\n logging.info(f'{len(docs)} articles loaded, with word bag length: {len(self.words)}')\n if dict_path != '': # save dict\n np.save(DATA_DIR + dict_path, self.word2idx)","def build_model(options,worddicts):\n opt_ret=dict()\n params=dict()\n word_xr1_mask=tf.reverse(word_x1_mask,[1])\n word_xr2_mask = tf.reverse(word_x2_mask, [1])\n\n\n\n #embedding layer\n word_embedding = norm_weight(options['n_words'], options['dim_word'])\n if options['embedding']:\n with open(options['embedding'], 'r',encoding='iso-8859-1') as f:\n for line in f:\n temp=line.split()\n word=temp[0]\n vector=temp[1:]\n if word in worddicts and worddicts[word]<options['n_words']:\n word_embedding[worddicts[word],:]=vector\n\n word_embedding_layer=tf.Variable(word_embedding,name='word_embedding')\n\n emb1=tf.nn.embedding_lookup(word_embedding_layer,word_x1,name='embedding_word_lookup1')\n emb2=tf.nn.embedding_lookup(word_embedding_layer,word_x2,name='embedding_word_lookup2')\n\n if options['use_dropout']:\n emb1=tf.cond(use_noise,lambda :tf.nn.dropout(emb1,0.5),lambda :emb1)\n emb2 = tf.cond(use_noise, lambda: tf.nn.dropout(emb2, 0.5), lambda: emb2)\n\n #1-layer LSTM\n print('LSTM result')\n for l in range(1):\n #param_init_lstm\n prefix = 'encoder_{}'.format(str(l + 1))\n if l==0:\n nin=options['dim_word']\n else:\n nin = options['dim_word']+2*options['dim']\n dim=options['dim']\n\n W = numpy.concatenate([norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim)], axis=1)\n params[_p(prefix, 'W')] = tf.Variable(W)\n\n # for the previous hidden activation\n U = numpy.concatenate([ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim)], axis=1)\n params[_p(prefix, 'U')] = tf.Variable(U)\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\n\n #param_init_rlstm\n prefix = 'encoder_r_{}'.format(str(l + 1))\n if l==0:\n nin=options['dim_word']\n else:\n nin = options['dim_word'] +2*options['dim']\n dim=options['dim']\n\n W = numpy.concatenate([norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim)], axis=1)\n params[_p(prefix, 'W')] = tf.Variable(W)\n\n # for the previous hidden activation\n U = numpy.concatenate([ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim)], axis=1)\n params[_p(prefix, 'U')] = tf.Variable(U)\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\n\n\n\n if l==0:\n ctx1=emb1\n ctx2=emb2\n else:\n ctx1=tf.concat([ctx1,emb1],axis=2)\n ctx2=tf.concat([ctx2,emb2],axis=2)\n\n print(ctx1)\n\n ctxr1=tf.reverse(ctx1,axis=[1])\n ctxr2=tf.reverse(ctx2,axis=[1])\n\n proj1=RNN_layer(ctx1,word_x1_mask,options,params,prefix='encoder_{}'.format(str(l+1)))\n projr1=RNN_layer(ctxr1,word_xr1_mask,options,params,prefix='encoder_r_{}'.format(str(l+1)))\n proj2=RNN_layer(ctx2,word_x2_mask,options,params,prefix='encoder_{}'.format(str(l+1)))\n projr2=RNN_layer(ctxr2,word_xr2_mask,options,params,prefix='encoder_r_{}'.format(str(l+1)))\n\n ctx1=tf.concat([proj1[0],projr1[0][::-1]],axis=len(projr1[0].shape)-1)\n ctx2 = tf.concat([proj2[0], projr2[0][::-1]], axis=len(projr2[0].shape) - 1)\n ctx1 = tf.transpose(ctx1, [1, 0, 2])\n ctx2 = tf.transpose(ctx2, [1, 0, 2])\n print(ctx1)\n\n ctx1=ctx1*word_x1_mask[:,:,None]\n ctx2 = ctx2 * word_x2_mask[:, :, None]\n def _step(h,x):\n return tf.matmul(x[0],x[1])\n temp=tf.zeros((tf.shape(ctx1)[1],tf.shape(ctx2)[1]))\n weight_martrix=tf.scan(_step,[ctx1,tf.transpose(ctx2,[0,2,1])],temp)\n weight_martrix_1=tf.exp(weight_martrix)*word_x2_mask[:,None,:]\n weight_martrix_2=tf.transpose(tf.exp(weight_martrix)*word_x1_mask[:,:,None],[0,2,1])\n weight_martrix_1=weight_martrix_1/tf.reduce_sum(weight_martrix_1,axis=2)[:,:,None]\n weight_martrix_2 = weight_martrix_2 / tf.reduce_sum(weight_martrix_2, axis=2)[:,:,None]\n\n ctx1_=tf.reduce_sum(weight_martrix_1[:,:,:,None]*ctx2[:,None,:,:],axis=2)\n ctx2_ = tf.reduce_sum(weight_martrix_2[:, :, :, None] * ctx1[:, None, :, :],axis=2)\n inp1=tf.concat([ctx1, ctx1_, ctx1*ctx1_, ctx1-ctx1_],axis=2)\n inp2 = tf.concat([ctx2, ctx2_, ctx2 * ctx2_, ctx2 - ctx2_], axis=2)\n params = param_init_fflayer(options, params, prefix='projection',\n nin=options['dim'] * 8, nout=options['dim'], ortho=False)\n\n\n s=tf.shape(inp1)\n inp1 = tf.nn.relu(tf.matmul(tf.reshape(inp1,[-1,int(inp1.shape[-1])]), params[_p('projection', 'W')]) + params[_p('projection', 'b')])\n inp1=tf.reshape(inp1,tf.concat([s[:2],[-1]],0))\n s=tf.shape(inp2)\n inp2 = tf.nn.relu(tf.matmul(tf.reshape(inp2,[-1,int(inp2.shape[-1])]), params[_p('projection', 'W')]) + params[_p('projection', 'b')])\n inp2=tf.reshape(inp2,tf.concat([s[:2],[-1]],0))\n if options['use_dropout']:\n inp1=tf.cond(use_noise,lambda :tf.nn.dropout(inp1,0.5),lambda :inp1)\n inp2 = tf.cond(use_noise, lambda: tf.nn.dropout(inp2, 0.5), lambda: inp2)\n\n\n for l in range(1):\n #param_init_lstm\n prefix = 'decoder_{}'.format(str(l + 1))\n if l==0:\n nin=options['dim']\n else:\n nin = options['dim']+2*options['dim']\n dim=options['dim']\n\n W = numpy.concatenate([norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim)], axis=1)\n params[_p(prefix, 'W')] = tf.Variable(W)\n\n # for the previous hidden activation\n U = numpy.concatenate([ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim)], axis=1)\n params[_p(prefix, 'U')] = tf.Variable(U)\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\n\n #param_init_rlstm\n prefix = 'decoder_r_{}'.format(str(l + 1))\n if l==0:\n nin=options['dim']\n else:\n nin = options['dim'] +2*options['dim']\n dim=options['dim']\n\n W = numpy.concatenate([norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim),\n norm_weight(nin, dim)], axis=1)\n params[_p(prefix, 'W')] = tf.Variable(W)\n\n # for the previous hidden activation\n U = numpy.concatenate([ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim),\n ortho_weight(dim)], axis=1)\n params[_p(prefix, 'U')] = tf.Variable(U)\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\n\n\n\n if l==0:\n ctx1=inp1\n ctx2=inp2\n else:\n ctx1=tf.concat([ctx1,inp1],axis=2)\n ctx2=tf.concat([ctx2,inp2],axis=2)\n\n print(ctx1)\n\n ctxr1=tf.reverse(ctx1,axis=[1])\n ctxr2=tf.reverse(ctx2,axis=[1])\n\n proj1=RNN_layer(ctx1,word_x1_mask,options,params,prefix='decoder_{}'.format(str(l+1)))\n projr1=RNN_layer(ctxr1,word_xr1_mask,options,params,prefix='decoder_r_{}'.format(str(l+1)))\n proj2=RNN_layer(ctx2,word_x2_mask,options,params,prefix='decoder_{}'.format(str(l+1)))\n projr2=RNN_layer(ctxr2,word_xr2_mask,options,params,prefix='decoder_r_{}'.format(str(l+1)))\n\n ctx1=tf.concat([proj1[0],projr1[0][::-1]],axis=len(projr1[0].shape)-1)\n ctx2 = tf.concat([proj2[0], projr2[0][::-1]], axis=len(projr2[0].shape) - 1)\n ctx1 = tf.transpose(ctx1, [1, 0, 2])\n ctx2 = tf.transpose(ctx2, [1, 0, 2])\n print(ctx1)\n\n mean_1=tf.reduce_sum(ctx1*word_x1_mask[:,:,None],axis=1)/tf.reduce_sum(word_x1_mask,axis=1)[:,None]\n max_1=tf.reduce_max(ctx1*word_x1_mask[:,:,None],axis=1)\n\n mean_2=tf.reduce_sum(ctx2*word_x2_mask[:,:,None],axis=1)/tf.reduce_sum(word_x2_mask,axis=1)[:,None]\n max_2=tf.reduce_max(ctx2*word_x2_mask[:,:,None],axis=1)\n\n #represention and MLP layer\n logit=tf.concat([mean_1,mean_2,max_1,max_2],axis=1)\n if options['use_dropout']:\n logit=tf.cond(use_noise,lambda :tf.nn.dropout(logit,0.5),lambda :logit)\n\n\n params = param_init_fflayer(options, params, prefix='ff_layer_1',\n nin=options['dim'] * 8, nout=options['dim'], ortho=False)\n params = param_init_fflayer(options, params, prefix='ff_layer_output',\n nin=options['dim'], nout=3, ortho=False)\n logit=tf.nn.tanh(tf.matmul(logit,params[_p('ff_layer_1','W')])+params[_p('ff_layer_1','b')])\n if options['use_dropout']:\n logit=tf.cond(use_noise,lambda :tf.nn.dropout(logit,0.5),lambda :logit)\n\n logit=tf.matmul(logit, params[_p('ff_layer_output', 'W')]) + params[_p('ff_layer_output', 'b')]\n probs=tf.nn.softmax(logit)\n pred=tf.argmax(probs,1)\n cost=tf.losses.sparse_softmax_cross_entropy(y,logit)\n return opt_ret,cost,pred,probs","def forward(self, idx, targets=None):\n B, T = idx.shape\n tok_emb = self.token_embedding_table(idx) # (B,T,C)\n pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n x = tok_emb + pos_emb # (B,T,C)\n x = self.blocks(x)\n logits = self.lm_head(x) # (B,T,vocab_size) \n\n if targets is None:\n loss = None\n else:\n B, T, C = logits.shape\n logits = logits.view(B*T, C)\n targets = targets.view(B*T)\n loss = F.cross_entropy(logits, targets)\n return logits, loss","def add_word_embeddings_op(self):\n with tf.variable_scope(\"words\"):\n if self.config.embeddings is None:\n self.logger.info(\"WARNING: randomly initializing word vectors\")\n _word_embeddings = tf.get_variable(\n name=\"_word_embeddings\",\n dtype=tf.float32,\n shape=[self.config.nwords, self.config.dim_word])\n else:\n _word_embeddings = tf.Variable(\n self.config.embeddings,\n name=\"_word_embeddings\",\n dtype=tf.float32,\n trainable=self.config.train_embeddings)\n\n word_embeddings = tf.nn.embedding_lookup(_word_embeddings,\n self.word_ids, name=\"word_embeddings\")\n\n with tf.variable_scope(\"chars\"):\n if self.config.use_chars:\n # get char embeddings matrix\n _char_embeddings = tf.get_variable(\n name=\"_char_embeddings\",\n dtype=tf.float32,\n shape=[self.config.nchars, self.config.dim_char])\n char_embeddings = tf.nn.embedding_lookup(_char_embeddings,\n self.char_ids, name=\"char_embeddings\")\n\n # put the time dimension on axis=1\n s = tf.shape(char_embeddings)\n char_embeddings = tf.reshape(char_embeddings,\n shape=[s[0]*s[1], s[-2], self.config.dim_char])\n word_lengths = tf.reshape(self.word_lengths, shape=[s[0]*s[1]])\n\n # bi lstm on chars\n cell_fw = tf.contrib.rnn.LSTMCell(self.config.hidden_size_char,\n state_is_tuple=True)\n cell_bw = tf.contrib.rnn.LSTMCell(self.config.hidden_size_char,\n state_is_tuple=True)\n _output = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, char_embeddings,\n sequence_length=word_lengths, dtype=tf.float32)\n\n # read and concat output\n _, ((_, output_fw), (_, output_bw)) = _output\n output = tf.concat([output_fw, output_bw], axis=-1)\n\n # shape = (batch size, max sentence length, char hidden size)\n output = tf.reshape(output,\n shape=[s[0], s[1], 2*self.config.hidden_size_char])\n word_embeddings = tf.concat([word_embeddings, output], axis=-1)\n\n self.word_embeddings = tf.nn.dropout(word_embeddings, self.dropout)","def get_embed(input_data, vocab_size, embed_dim):\n embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))\n embed = tf.nn.embedding_lookup(embedding, input_data)\n\n return embed","def load_pretrain_embedding(vocab, embed_size, embedding_path):\n model = KeyedVectors.load_word2vec_format(embedding_path)\n\n print('{} {}'.format(vocab.size(), embed_size))\n for token, id in vocab.token2id.items():\n if token in model:\n print('{} {}'.format(token, ' '.join(map(str, model[token]))))\n else:\n emb = np.random.random((embed_size,)) - 0.5\n print('{} {}'.format(token, ' '.join(map(str, emb))))","def predict_sentences_2_idxs(self):\n fo = open(self.config.parsed_predict_file, 'w')\n self.load_dicts()\n\n questions = pd.read_csv(self.config.predict_file,\n usecols=[\"question_text\"], index_col=False)\n unk_idx = self.word2idx[self.config.unknown_token]\n\n for quest in questions.question_text:\n tokens = preprocess_text(quest)\n if self.config.include_unknown:\n idxs = [self.word2idx.get(token, unk_idx) for token in\n tokens]\n else:\n idxs = [self.word2idx.get(token) for token in tokens]\n idxs = [idx for idx in idxs if idx]\n fo.write((str(\" \".join(str(num) for num in idxs)) + \"\\n\"))","def get_docs_embedding(docs_tok, model, dim=300):\n all_docs_embedding = []\n for doc in docs_tok:\n all_docs_embedding.append(text2vec(doc, model, dim))\n cols = [str(i) for i in range(dim)]\n embeddings = pd.DataFrame(data=all_docs_embedding)\n embeddings.columns = cols\n embeddings.to_parquet('../model/docs_embeddings.parquet', index=False)\n return np.array(all_docs_embedding)","def Emojify_V2(input_shape, word_to_vec_map, word_to_index):\n \n ### START CODE HERE ###\n # Define sentence_indices as the input of the graph.\n # It should be of shape input_shape and dtype 'int32' (as it contains indices, which are integers).\n sentence_indices = Input(shape = input_shape, dtype = 'int32')\n \n # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\n # def pretrained_embedding_layer(word_to_vec_map, word_to_index): # return embedding_layer\n embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\n \n # Propagate sentence_indices through your embedding layer\n # (See additional hints in the instructions).\n embeddings = embedding_layer(sentence_indices) \n \n # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\n # The returned output should be a batch of sequences.\n X = LSTM(units = 128, return_sequences = True)(embeddings)\n # Add dropout with a probability of 0.5\n X = Dropout(rate = 0.5)(X)\n # Propagate X trough another LSTM layer with 128-dimensional hidden state\n # The returned output should be a single hidden state, not a batch of sequences.\n X = LSTM(units = 128, return_sequences = False)(X)\n # Add dropout with a probability of 0.5\n X = Dropout(rate = 0.5)(X) \n # Propagate X through a Dense layer with 5 units\n X = Dense(units = 5)(X)\n # Add a softmax activation\n X = Activation(activation = 'softmax')(X)\n \n # Create Model instance which converts sentence_indices into X.\n model = Model(inputs = sentence_indices, outputs = X)\n \n ### END CODE HERE ###\n \n return model","def generateByWord(model, voc, maxlen=20, diversity=0.5, numwords=42):\n\n text, sym_indices, indices_sym = voc\n syms = set(text)\n start_index = random.randint(0, len(text) - maxlen - 1) \n generated = ''\n sentence = text[start_index: start_index + maxlen]\n \n #generated += sentence\n generated += ' '.join(sentence)\n print('----- Generating with seed: \"' + ' '.join(sentence) + '\"')\n sys.stdout.write(generated)\n\n for i in range(numwords):\n x = np.zeros((1, maxlen, len(syms)))\n for t, sym in enumerate(sentence):\n x[0, t, sym_indices[sym]] = 1.\n \n preds = model.predict(x, verbose=0)[0]\n next_index = sample(preds, diversity)\n next_sym = indices_sym[next_index]\n generated += ' '+next_sym\n sentence.append(next_sym)\n tmpsentence = sentence[1:]\n sentence = tmpsentence\n sys.stdout.write(next_sym+' ')\n sys.stdout.flush()\n print()","def _embed(self):\n batch_size = tf.shape(self.p)[0]\n with tf.variable_scope(\"emb\"):\n with tf.variable_scope(\"char\"):\n pc_emb = tf.reshape(tf.nn.embedding_lookup(\n self.char_embed, self.pc), \n [batch_size * self.max_p_len, self.max_w_len, self.vocab.char_embed_dim])\n qc_emb = tf.reshape(tf.nn.embedding_lookup(\n self.char_embed, self.qc), \n [batch_size * self.max_q_len, self.max_w_len, self.vocab.char_embed_dim])\n cell_fw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\n cell_bw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, pc_emb, self.pc_length, dtype=tf.float32)\n pc_emb = tf.concat([state_fw, state_bw], axis=1)\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, qc_emb, self.qc_length, dtype=tf.float32)\n qc_emb = tf.concat([state_fw, state_bw], axis=1)\n pc_emb = tf.reshape(pc_emb, [batch_size, self.max_p_len, 2 * self.char_hidden_size])\n qc_emb = tf.reshape(qc_emb, [batch_size, self.max_q_len, 2 * self.char_hidden_size])\n\n with tf.name_scope(\"word\"):\n p_emb = tf.nn.embedding_lookup(self.word_embed, self.p)\n q_emb = tf.nn.embedding_lookup(self.word_embed, self.q)\n\n with tf.name_scope(\"pos\"):\n p_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.p_pos)\n q_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.q_pos)\n \n with tf.name_scope(\"em\"):\n sh = tf.shape(self.p_em)\n resh = [sh[0], sh[1], 1]\n p_em_feat = tf.reshape(tf.cast(self.p_em, dtype=tf.float32), shape=resh)\n\n self.p_emb = tf.concat([p_emb, pc_emb, p_pos_emb, p_em_feat], axis=2)\n self.q_emb = tf.concat([q_emb, qc_emb, q_pos_emb], axis=2)","def get_word_embeddings(self):\n embedding_index = {}\n with open('./glove/glove.6B.100d.txt', encoding=\"utf8\") as f:\n for line in f:\n values = line.split()\n word = values[0]\n coefs = np.asarray(values[1:], dtype='float32')\n embedding_index[word] = coefs\n return embedding_index","def sentence_to_embedding(sent, word_to_vec, seq_len, embedding_dim):\n embedding_vec = np.zeros((seq_len,embedding_dim))\n for i in range(min(len(sent),seq_len)):\n embedding_vec[i,:] = word_to_vec.get(sent[i])\n return embedding_vec","def forward(self, sentence, p_sentence, pos_tags, lengths, target_idx_in, region_marks,\n local_roles_voc, frames, local_roles_mask,\n sent_pred_lemmas_idx, dep_tags, dep_heads, targets, predicate_identification, all_l_ids,\n Predicate_link, Predicate_Labels_nd, Predicate_Labels,\n unlabeled_sentence_in=False, p_unlabeled_sentence_in=False, unlabeled_sen_lengths=False, test=False,\n cvt_train=False):\n #contruct input for DEP\n embeds_DEP = self.word_embeddings_DEP(sentence)\n add_zero = torch.zeros((self.batch_size, 1, self.word_emb_dim)).to(device)\n embeds_DEP = embeds_DEP.view(self.batch_size, len(sentence[0]), self.word_emb_dim)\n embeds_DEP_cat = torch.cat((self.VR_word_embedding_random + add_zero, embeds_DEP), 1)\n\n\n fixed_embeds_DEP = self.word_fixed_embeddings(p_sentence)\n add_zero = torch.zeros((self.batch_size, 1, self.word_emb_dim)).to(device)\n fixed_embeds_DEP_cat = torch.cat((self.VR_word_embedding + add_zero, fixed_embeds_DEP), 1)\n\n embeds_forDEP = torch.cat((embeds_DEP_cat, fixed_embeds_DEP_cat), 2)\n\n embeds_forDEP = self.DEP_input_dropout(embeds_forDEP)\n\n\n #first layer\n embeds_sort, lengths_sort, unsort_idx = self.sort_batch(embeds_forDEP, lengths+1)\n embeds_sort = rnn.pack_padded_sequence(embeds_sort, lengths_sort, batch_first=True)\n # hidden states [time_steps * batch_size * hidden_units]\n hidden_states, self.hidden = self.BiLSTM_0(embeds_sort, self.hidden)\n # it seems that hidden states is already batch first, we don't need swap the dims\n # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(self.batch_size, -1, )\n hidden_states, lens = rnn.pad_packed_sequence(hidden_states, batch_first=True)\n # hidden_states = hidden_states.transpose(0, 1)\n hidden_states_0 = hidden_states[unsort_idx]\n\n # second_layer\n embeds_sort, lengths_sort, unsort_idx = self.sort_batch(hidden_states_0, lengths+1)\n embeds_sort = rnn.pack_padded_sequence(embeds_sort, lengths_sort, batch_first=True)\n # hidden states [time_steps * batch_size * hidden_units]\n hidden_states, self.hidden_2 = self.BiLSTM_1(embeds_sort, self.hidden_2)\n # it seems that hidden states is already batch first, we don't need swap the dims\n # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(self.batch_size, -1, )\n hidden_states, lens = rnn.pad_packed_sequence(hidden_states, batch_first=True)\n #hidden_states = hidden_states.transpose(0, 1)\n hidden_states_1 = hidden_states[unsort_idx]\n\n ###########################################\n\n Head_hidden = self.DEP_link_H_dropout(F.relu(self.hidLayerFOH_link(hidden_states_1)))\n Dependent_hidden = self.DEP_link_M_dropout(F.relu(self.hidLayerFOM_link(hidden_states_1)))\n\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\n Head_hidden = torch.cat((Head_hidden, Variable(bias_one)), 2)\n\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\n Dependent_hidden = torch.cat((Dependent_hidden, Variable(bias_one)), 2)\n\n left_part = torch.mm(Dependent_hidden.view(self.batch_size * (len(sentence[0]) + 1), -1), self.W_R_link)\n left_part = left_part.view(self.batch_size, (len(sentence[0]) + 1), -1)\n Head_hidden = Head_hidden.view(self.batch_size, (len(sentence[0]) + 1), -1).transpose(1, 2)\n tag_space = torch.bmm(left_part, Head_hidden).view(\n (len(sentence[0]) + 1) * self.batch_size, len(sentence[0]) + 1)\n\n heads = np.argmax(tag_space.cpu().data.numpy(), axis=1)\n\n nums = 0.0\n wrong_nums = 0.0\n for a, b in zip(heads, dep_heads.flatten()):\n if b== -1:\n continue\n nums += 1\n if a != b:\n wrong_nums += 1\n\n loss_function = nn.CrossEntropyLoss(ignore_index=-1)\n DEPloss_link = loss_function(tag_space, torch.from_numpy(dep_heads).to(device).view(-1))\n\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++\n Head_hidden_tag = self.DEP_tag_H_dropout(F.relu(self.hidLayerFOH_tag(hidden_states_1)))\n Dependent_hidden_tag = self.DEP_tag_M_dropout(F.relu(self.hidLayerFOM_tag(hidden_states_1)))\n\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\n Head_hidden_tag = torch.cat((Head_hidden_tag, Variable(bias_one)), 2)\n\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\n Dependent_hidden_tag = torch.cat((Dependent_hidden_tag, Variable(bias_one)), 2)\n\n left_part = torch.mm(Dependent_hidden_tag.view(self.batch_size * (len(sentence[0]) + 1), -1), self.W_R_tag)\n left_part = left_part.view(self.batch_size, (len(sentence[0]) + 1) * self.dep_size, -1)\n Head_hidden_tag = Head_hidden_tag.view(self.batch_size, (len(sentence[0]) + 1), -1).transpose(1, 2)\n tag_space_tag = torch.bmm(left_part, Head_hidden_tag).view(\n (len(sentence[0]) + 1) * self.batch_size, self.dep_size, len(sentence[0]) + 1).transpose(1, 2)\n\n tag_space_tag = tag_space_tag[np.arange(0, (len(sentence[0]) + 1) * self.batch_size), dep_heads.flatten()]\n tag_space_tag = tag_space_tag.view((len(sentence[0]) + 1) * self.batch_size, -1)\n heads_tag = np.argmax(tag_space_tag.cpu().data.numpy(), axis=1)\n\n nums_tag = 0.0\n wrong_nums_tag = 0.0\n for a, b in zip(heads_tag, dep_tags.view(-1).cpu().data.numpy()):\n if b == -1 or b == 0:\n continue\n nums_tag += 1\n if a != b:\n wrong_nums_tag += 1\n\n loss_function = nn.CrossEntropyLoss(ignore_index=0)\n DEPloss_tag = loss_function(tag_space_tag, dep_tags.view(-1))\n\n #####################################################\n h_layer_0 = hidden_states_0[:, 1:] # .detach()\n h_layer_1 = hidden_states_1[:, 1:] # .detach()\n\n w = F.softmax(self.elmo_w, dim=0)\n SRL_composer = self.elmo_gamma * (w[0] * h_layer_0 + w[1] * h_layer_1)\n SRL_composer = self.elmo_mlp(SRL_composer)\n\n\n fixed_embeds = self.word_fixed_embeddings(p_sentence)\n fixed_embeds = fixed_embeds.view(self.batch_size, len(sentence[0]), self.word_emb_dim)\n embeds_SRL = self.word_embeddings_SRL(sentence)\n embeds_SRL = embeds_SRL.view(self.batch_size, len(sentence[0]), self.word_emb_dim)\n sent_pred_lemmas_embeds = self.p_lemma_embeddings(sent_pred_lemmas_idx)\n pos_embeds = self.pos_embeddings(pos_tags)\n region_marks = self.region_embeddings(region_marks).view(self.batch_size, len(sentence[0]), 16)\n\n SRL_hidden_states = torch.cat((embeds_SRL, fixed_embeds, pos_embeds, sent_pred_lemmas_embeds, region_marks, SRL_composer), 2)\n SRL_hidden_states = self.SRL_input_dropout(SRL_hidden_states)\n\n\n # SRL layer\n embeds_sort, lengths_sort, unsort_idx = self.sort_batch(SRL_hidden_states, lengths)\n embeds_sort = rnn.pack_padded_sequence(embeds_sort, lengths_sort.cpu().numpy(), batch_first=True)\n # hidden states [time_steps * batch_size * hidden_units]\n hidden_states, self.hidden_4 = self.BiLSTM_SRL(embeds_sort, self.hidden_4)\n # it seems that hidden states is already batch first, we don't need swap the dims\n # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(self.batch_size, -1, )\n hidden_states, lens = rnn.pad_packed_sequence(hidden_states, batch_first=True)\n # hidden_states = hidden_states.transpose(0, 1)\n hidden_states = hidden_states[unsort_idx]\n #hidden_states = self.hidden_state_dropout_SRL(hidden_states)\n\n # B * H\n hidden_states_3 = hidden_states\n hidden_states_word = self.dropout_1_SRL(F.relu(self.Non_Predicate_Proj(hidden_states_3)))\n predicate_embeds = hidden_states_3[np.arange(0, hidden_states_3.size()[0]), target_idx_in]\n hidden_states_predicate = self.dropout_2_SRL(F.relu(self.Predicate_Proj(predicate_embeds)))\n\n bias_one = torch.ones((self.batch_size, len(sentence[0]), 1)).to(device)\n hidden_states_word = torch.cat((hidden_states_word, Variable(bias_one)), 2)\n\n bias_one = torch.ones((self.batch_size, 1)).to(device)\n hidden_states_predicate = torch.cat((hidden_states_predicate, Variable(bias_one)), 1)\n\n left_part = torch.mm(hidden_states_word.view(self.batch_size * len(sentence[0]), -1), self.W_R)\n left_part = left_part.view(self.batch_size, len(sentence[0]) * self.tagset_size, -1)\n hidden_states_predicate = hidden_states_predicate.view(self.batch_size, -1, 1)\n tag_space = torch.bmm(left_part, hidden_states_predicate).view(\n len(sentence[0]) * self.batch_size, -1)\n SRLprobs = F.softmax(tag_space, dim=1)\n\n targets = targets.view(-1)\n\n loss_function = nn.CrossEntropyLoss(ignore_index=0)\n SRLloss = loss_function(tag_space, targets)\n\n\n\n return SRLloss, DEPloss_tag, DEPloss_link, SRLloss, SRLprobs, wrong_nums, nums, wrong_nums_tag, nums_tag, \\\n nums - wrong_nums, nums, nums, \\\n nums_tag - wrong_nums_tag, nums_tag, nums_tag,","def load_word2vect(self, file_path):\n self.embeddings = []\n self.word_to_idx = {'<pad>' : 0}\n self.vocab = ['<pad>']\n\n model = w2v.load(file_path)\n self.embedding_size = model.vectors.shape[1]\n pad_embedding = np.zeros(self.embedding_size, \"float32\")\n self.embeddings.append(pad_embedding)\n\n train_words_set = set([word for text in self.train_data for word in\n text[1].split(\" \")])\n\n for w in model.vocab:\n if w in train_words_set:\n self.word_to_idx[w] = len(self.vocab)\n self.vocab.append(w)\n self.embeddings.append(model[w])\n\n del model","def visualize_attention(test_seq, i,\n model,\n id2wrd, # word_index.\n n):\n \n id2wrd = dict((v, k) for k, v in id2wrd.items()) # Exchange key with values.\n\n get_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[4].output])\n out = get_layer_output([test_seq, ])[0] # test mode\n\n att_w = model.layers[5].get_weights() # The attention layer is the sixth layer.\n\n eij = np.tanh(np.dot(out[i], att_w[0]))\n# print(\"1 eij is ............\")\n# print(eij)\n ListToCSV(eij, 'eij.csv') # added by Shigang\n ai = np.exp(eij)\n# print(\"1 ai is ............\")\n# print(ai)\n ListToCSV(ai, 'ai.csv') # added by Shigang\n weights = ai/np.sum(ai)\n# print(\"1 weights is ............\")\n# print(weights)\n ListToCSV(weights, '1weights.csv') # added by Shigang\n weights = np.sum(weights,axis=1)\n# print(\"2 weights is ............\")\n# print(weights)\n ListToCSV(weights, '2weights.csv') # added by Shigang\n topKeys = np.argpartition(weights,-n)[-n:]\n\n print (' '.join([id2wrd[wrd_id] for wrd_id in test_seq[i] if wrd_id != 0.]))\n print ('--------------------------Attentive Words start: --------------------------------------')\n \n for k in test_seq[i][topKeys]:\n if k != 0.:\n print (id2wrd[k])\n print ('--------------------------Attentive Words end: --------------------------------------')\n \n return","def getEmbeddings(model, words):\n\tembeddings = {}\n\tfor word in words:\n\t\tembeddings[word] = model[word]\n\treturn embeddings","def convert_data_to_model_input(self, origin_datas, add_unknow_words=True):\n def _word_to_id(word):\n if word in self.word_to_idx:\n return self.word_to_idx[word]\n elif add_unknow_words:\n self.word_to_idx[word] = len(self.vocab)\n self.vocab.append(word)\n self.embeddings.append(np.random.uniform(-0.25, 0.25,\n self.embedding_size))\n return self.word_to_idx[word]\n else:\n # padding id\n return 0\n\n x_inputs = []\n y_inputs = []\n\n for data in origin_datas:\n y_inputs.append(self.labels_to_idx[data[0]])\n\n words = data[1].split(\" \")\n ids = list(map(_word_to_id, words))\n\n x_inputs.append(ids)\n\n return x_inputs, y_inputs","def __init__(self, word_embed_size, vocab):\n super(ModelEmbeddings, self).__init__()\n\n ### YOUR CODE HERE for part 1h\n dropout_rate = 0.3\n n_chars = len(vocab.char2id)\n self.char_embed_size = 50\n self.word_embed_size = word_embed_size\n self.vocab = vocab\n self.char_embed = nn.Embedding(n_chars, self.char_embed_size)\n self.conv = CNN(self.char_embed_size, word_embed_size)\n self.highway = Highway(word_embed_size)\n self.dropout = nn.Dropout(dropout_rate)\n ### END YOUR CODE","def model(mtf_features, other_features, params, mesh, variable_dtype, context=None):\n\n x, batch_dim, sequence_dim, embd_dim, vocab_dim, embed_sequence_dim = parse_inputs(mtf_features, other_features)\n\n if is_incremental_inference(context):\n # reshape inputs if in inference mode\n x = mtf.gather(x, context.position - 1, sequence_dim)\n x = mtf.reshape(x, [batch_dim])\n\n use_axial_pos_emb = params[\"axial_pos_emb\"] is not None\n\n if not use_axial_pos_emb:\n # Use standard position encoding\n wpe = mtf.get_variable(mesh, \"wpe\", mtf.Shape([embed_sequence_dim, embd_dim]),\n initializer=tf.random_normal_initializer(stddev=0.01),\n master_dtype=variable_dtype.master_dtype,\n slice_dtype=variable_dtype.slice_dtype,\n activation_dtype=variable_dtype.activation_dtype)\n else:\n wpe = axial_positional_emb(embd_dim, mesh, params, variable_dtype)\n\n # Text encoding\n wte = mtf.get_variable(mesh, \"wte\", mtf.Shape([vocab_dim, embd_dim]),\n initializer=tf.random_normal_initializer(stddev=0.02),\n master_dtype=variable_dtype.master_dtype,\n slice_dtype=variable_dtype.slice_dtype,\n activation_dtype=variable_dtype.activation_dtype)\n\n with tf.variable_scope(\"token_embd\"):\n # Text embedding\n h = mtf.gather(wte, x, vocab_dim)\n if params[\"embed_dropout\"] > 0 and params[\"mode\"] == \"train\":\n h = mtf.dropout(h, rate=params[\"embed_dropout\"], name=\"wte_dropout\")\n\n with tf.variable_scope(\"pos_embd\"):\n # Positional embedding\n position_indices = mtf.range(mesh, sequence_dim, tf.int64) if not is_incremental_inference(context) else (\n context.position - 1)\n pos_emb = mtf.gather(wpe, position_indices, wpe.shape[0])\n if params[\"embed_dropout\"] > 0 and params[\"mode\"] == \"train\":\n pos_emb = mtf.dropout(pos_emb, rate=params[\"embed_dropout\"], name=\"wte_dropout\")\n h += pos_emb\n\n aux_losses = 0 # instantiate auxiliary losses (for MOE models)\n\n for layer in range(params[\"n_layer\"]):\n # attn blocks\n share_parameters = exists(params[\"share_parameters\"]) and params[\"share_parameters\"] == True\n block_scope = f\"h{layer}\" if not share_parameters else \"\"\n\n block_fn = block(params=params, scope=block_scope, layer_num=layer,\n bias=other_features[\"attn_bias\"],\n sequence_dim=sequence_dim,\n memory_length_dim=other_features[\"memory_length_dim\"],\n variable_dtype=variable_dtype,\n context=context)\n\n # If true and in train mode, enable gradient checkpointing\n recompute_grad = params[\"recompute_grad\"] and (params[\"mode\"] == \"train\") == True\n h, loss = block_fn(h) if not recompute_grad else mtf.recompute_grad(block_fn, [h])\n aux_losses += loss\n\n no_weight_tie_emb = params[\"no_weight_tie\"] == True\n if no_weight_tie_emb:\n with tf.variable_scope(\"wte_final_linear\"):\n logits = linear(h, \"linear_out\", vocab_dim, variable_dtype=variable_dtype, params=params)\n else:\n # Layer normalize & affine transform\n h = layer_norm(h, \"ln_f\", variable_dtype=variable_dtype)\n seq_dim = sequence_dim if not is_incremental_inference(context) else mtf.Dimension(\"sequence\", 1)\n with tf.variable_scope(\"wte_final_einsum\"):\n # Equivalent to tf.matmul\n logits = mtf.einsum([h, wte], output_shape=[batch_dim, seq_dim, vocab_dim])\n\n if params[\"mode\"] in [\"train\", \"eval\"]:\n labels = mtf_features[\"labels\"]\n z_loss = params.get(\"z_loss\", 1e-4) # an auxiliary loss used to stabilize mtf xentropy\n\n # Go to full precision for the logits \n logits = mtf.cast(logits, tf.float32)\n\n with tf.variable_scope(\"xentropy_final\"):\n loss_batch = mtf.layers.softmax_cross_entropy_with_logits(logits=logits, targets=labels,\n vocab_dim=logits.shape[-1], z_loss=z_loss)\n\n # For non-autoregressive models (masked language modeling training)\n # Make sure labels with padding tokens are not counted in the loss\n if not params[\"causal\"]:\n padding_id = params.get(\"padding_id\", 0)\n loss_batch = mtf.where(mtf.not_equal(labels, padding_id), loss_batch, mtf.zeros_like(loss_batch))\n\n with tf.variable_scope(\"reduce_mean_final\"):\n loss = mtf.reduce_mean(loss_batch)\n\n loss += aux_losses # Add on auxiliary losses (currently only used for MoE)\n loss /= params[\"num_microbatches\"]\n # Convert to train dtype\n loss = mtf.cast(loss, variable_dtype.slice_dtype)\n else:\n loss = None\n loss_batch = None\n\n # Cast back to checkpoint dtype\n logits = mtf.cast(logits, variable_dtype.master_dtype)\n return logits, loss, loss_batch","def skipgram(current_center_word, outside_words, word2ind,\n center_word_vectors, outside_vectors, dataset,\n word2vec_loss_and_gradient=naive_softmax_loss_and_gradient):\n loss = 0.0\n grad_center_vecs = np.zeros(center_word_vectors.shape)\n grad_outside_vectors = np.zeros(outside_vectors.shape)\n\n ### YOUR CODE HERE\n center_word_idx = word2ind[current_center_word]\n center_word_vec = center_word_vectors[center_word_idx] \n \n for outside_word in outside_words:\n outside_word_idx = word2ind[outside_word]\n _loss_sk, _grad_center_vecs, _grad_outside_vectors = \\\n word2vec_loss_and_gradient(\n center_word_vec, outside_word_idx, outside_vectors, dataset)\n loss += _loss_sk\n grad_center_vecs[center_word_idx] += _grad_center_vecs\n grad_outside_vectors += _grad_outside_vectors \n ### END YOUR CODE\n\n return loss, grad_center_vecs, grad_outside_vectors","def _add_seq2seq(self):\n hps = self._hps\n vsize = self._vocab.size() # size of the vocabulary\n \n with tf.variable_scope('seq2seq'):\n # Some initializers\n self.rand_unif_init = tf.random_uniform_initializer(-hps.rand_unif_init_mag, hps.rand_unif_init_mag, seed=123)\n self.trunc_norm_init = tf.truncated_normal_initializer(stddev=hps.trunc_norm_init_std)\n\n\n with tf.variable_scope('embedding'):\n if hps.pretrained_embeddings:\n word2vec = load_embeddings(hps.embeddings_path, self._vocab.word2id, hps.rand_unif_init_mag)\n self.embedding = tf.get_variable('embedding', [vsize, hps.emb_dim],\n dtype=tf.float32, initializer=tf.constant_initializer(word2vec))\n # self.assign_embedding = tf.assign(self.embedding, word2vec)\n else:\n self.embedding = tf.get_variable('embedding', [vsize, hps.emb_dim],\n dtype=tf.float32, initializer=self.trunc_norm_init)\n if hps.mode==\"train\": self._add_emb_vis(self.embedding) # add to tensorboard\n\n # tensor with shape (batch_size, max_enc_steps, emb_size)\n emb_enc_inputs = tf.nn.embedding_lookup(self.embedding, self._enc_batch)\n if self._hps.hier:\n enc_batch_sections = tf.unstack(self._enc_batch_sections, axis=1)\n sec_emb_enc_inputs = [tf.nn.embedding_lookup(self.embedding, section)\n for section in enc_batch_sections]\n # list length max_dec_steps containing shape (batch_size, emb_size)\n emb_dec_inputs = [tf.nn.embedding_lookup(self.embedding, x)\n for x in tf.unstack(self._dec_batch, axis=1)]\n\n\n # Hierarchical attention model\n if self._hps.hier:\n with tf.variable_scope('encoder'), tf.device(self._next_device()):\n sec_enc_outs = []\n states_fw = []\n states_bw = []\n states = []\n\n # level 1, encode words to sections\n with tf.variable_scope(\"word_level_encoder\", reuse=tf.AUTO_REUSE) as scope:\n encoder_outputs_words = []\n cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n fw_st, bw_st = None, None\n if self._hps.use_do: # DropOut\n cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=1.0 - self._hps.do_prob)\n cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=1.0 - self._hps.do_prob)\n for i in range(self._hps.num_sections):\n encoder_tmp_output, (fw_st, bw_st) = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, inputs=sec_emb_enc_inputs[i], dtype=tf.float32,\n sequence_length=self._batch_sections_len[:,i], swap_memory=True, initial_state_bw=bw_st, initial_state_fw=fw_st)\n # concatenate the forwards and backwards states\n encoder_tmp_output = tf.concat(axis=2, values=encoder_tmp_output) #shape=[batch x seq_len x hidden_size]\n \n encoder_outputs_words.append(encoder_tmp_output)\n # instead of concating the fw and bw states, we use a ff network\n combined_state = self._reduce_states(fw_st, bw_st)\n states.append(combined_state)\n scope.reuse_variables()\n \n # level 2, encode sections to doc\n encoder_outputs_words = tf.stack(encoder_outputs_words, axis=1) # shape [batch x num_sections x seq_len x hidden_size]\n shapes = encoder_outputs_words.shape\n encoder_outputs_words = tf.reshape(encoder_outputs_words, (shapes[0].value, -1, shapes[-1].value)) #shape=[batch x (seq_len * num_sections) x hidden_size]\n\n doc_sections_h = tf.stack([s.h for s in states], axis=1) # [batch x num_sections x hidden_size]\n doc_sections_c = tf.stack([s.c for s in states], axis=1) # [batch x num_sections x hidden_size]\n\n with tf.variable_scope(\"section_level_encoder\"):\n if FLAGS.section_level_encoder == 'RNN':\n cell_fw_1 = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n cell_bw_1 = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n if self._hps.use_do:\n cell_fw_1 = tf.contrib.rnn.DropoutWrapper(cell_fw_1, output_keep_prob=1.0 - self._hps.do_prob)\n cell_bw_1 = tf.contrib.rnn.DropoutWrapper(cell_bw_1, output_keep_prob=1.0 - self._hps.do_prob)\n encoder_output_sections, (fw_st_2, bw_st_2) =\\\n tf.nn.bidirectional_dynamic_rnn(cell_fw_1, cell_bw_1, inputs=doc_sections_h, sequence_length=self._doc_sec_lens, dtype=tf.float32, swap_memory=True)\n encoder_output_sections = tf.concat(axis=2, values=encoder_output_sections)\n doc_sections_state = self._reduce_states(fw_st_2, bw_st_2)\n else:\n if FLAGS.section_level_encoder == 'AVG': # average section cells\n doc_sections_state_h = tf.reduce_mean(doc_sections_h, axis=1)\n doc_sections_state_c = tf.reduce_mean(doc_sections_c, axis=1)\n elif FLAGS.section_level_encoder == 'FF': # use a feedforward network to combine section cells\n doc_sections_state_h = tf.reshape([doc_sections_h.shape[0].eval(), -1])\n doc_sections_state_h = tf.layers.dense(\n inputs=doc_sections_state_h,\n units=self._hps.hidden,\n activation=tf.nn.relu) \n doc_sections_state_c = tf.reshape([doc_sections_c.shape[0].eval(), -1])\n doc_sections_state_c = tf.layers.dense(\n inputs=doc_sections_state_c,\n units=self._hps.hidden,\n activation=tf.nn.relu)\n else:\n raise AttributeError('FLAGS.section_level_encoder={} is not a valid option'.format(FLAGS.section_level_encoder))\n doc_sections_state = tf.contrib.rnn.LSTMStateTuple(doc_sections_state_c, doc_sections_state_h)\n encoder_output_sections = doc_sections_h \n \n elif not self._hps.multi_layer_encoder:\n with tf.variable_scope('encoder'):\n with tf.variable_scope('word_level_encoder'):\n cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n (encoder_outputs, (fw_st, bw_st)) =\\\n tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs=emb_enc_inputs, dtype=tf.float32, sequence_length=self._enc_lens, swap_memory=True)\n # concatenate the forwards and backwards states\n encoder_outputs = tf.concat(axis=2, values=encoder_outputs)\n \n # stack n layers of lstms for encoder\n elif self._hps.multi_layer_encoder:\n # TODO: check\n for layer_i in xrange(self._hps.enc_layers):\n with tf.variable_scope('encoder%d'%layer_i), tf.device(\n self._next_device()):\n cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\n if self._hps.use_do: # add dropout\n cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=1.0 - self._hps.do_prob)\n cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=1.0 - self._hps.do_prob)\n emb_enc_inputs, (fw_st, bw_st) =\\\n tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs=emb_enc_inputs, dtype=tf.float32, sequence_length=self._enc_lens, swap_memory=True)\n emb_enc_inputs = tf.concat(axis=2, values=emb_enc_inputs)\n encoder_outputs = emb_enc_inputs\n \n if self._hps.hier:\n self._enc_sec_states = encoder_output_sections\n self._enc_states = encoder_outputs_words \n else:\n self._enc_states = encoder_outputs\n self._enc_sec_states = None\n \n # convert the encoder bidirectional hidden state to the decoder state\n # (unidirectional) by an MLP\n if self._hps.hier:\n self._dec_in_state = doc_sections_state\n else:\n with tf.variable_scope('encoder'):\n with tf.variable_scope('word_level_encoder'):\n self._dec_in_state = self._reduce_states(fw_st, bw_st) \n \n # Add the decoder\n\n with tf.variable_scope('decoder'), tf.device(self._next_device()):\n cell = tf.contrib.rnn.LSTMCell(\n self._hps.hidden_dim,\n state_is_tuple=True,\n initializer=self.rand_unif_init)\n \n # We need to pass in the previous step's coverage vector each time\n prev_coverage = self.prev_coverage\\\n if hps.mode==\"decode\" and self._hps.coverage \\\n else None \n \n \n if self._hps.hier:\n decoder_outputs, self._dec_out_state, self.attn_dists, self.p_gens, self.coverage, self.attn_dists_sec =\\\n self.attn_decoder(emb_dec_inputs,\n self._dec_in_state,\n self._enc_states,\n cell,\n self._enc_sec_states,\n num_words_section=self._batch_sections_len,\n enc_padding_mask=self._enc_padding_mask,\n enc_section_padding_mask=self._enc_section_padding_mask,\n initial_state_attention=(self._hps.mode==\"decode\"),\n pointer_gen=self._hps.pointer_gen,\n use_coverage=self._hps.coverage,\n prev_coverage=prev_coverage,\n temperature=self._hps.temperature\n )\n \n else:\n decoder_outputs, self._dec_out_state, self.attn_dists, self.p_gens, self.coverage, _ =\\\n self.attn_decoder(emb_dec_inputs,\n self._dec_in_state,\n self._enc_states,\n cell,\n encoder_section_states=None,\n num_words_section=None,\n enc_padding_mask=self._enc_padding_mask,\n initial_state_attention=(self._hps.mode==\"decode\"),\n pointer_gen=self._hps.pointer_gen,\n use_coverage=self._hps.coverage,\n prev_coverage=prev_coverage,\n ) \n \n\n # Project decoder output to vocabulary\n with tf.variable_scope('output_projection'), tf.device(self._next_device()):\n if self._hps.output_weight_sharing:\n # share weights of embedding layer with projection\n # self.embedding is in shape [vsize, hps.emb_dim]\n w_proj = tf.get_variable('w_proj', [self._hps.emb_dim, self._hps.hidden_dim],\n dtype=tf.float32, initializer=self.trunc_norm_init)\n w = tf.tanh(tf.transpose(tf.matmul(self.embedding, w_proj))) # shape = [vsize, hps.hidden_dim]\n \n # w_t = tf.transpose(w)\n b = tf.get_variable('b', [vsize],\n dtype=tf.float32, initializer=self.trunc_norm_init)\n else: \n w = tf.get_variable('w', [self._hps.hidden_dim, vsize],\n dtype=tf.float32, initializer=self.trunc_norm_init)\n # w_t = tf.transpose(w)\n b = tf.get_variable('b', [vsize],\n dtype=tf.float32, initializer=self.trunc_norm_init)\n # vocabulary score at each decoder step\n vocab_scores = []\n for i,output in enumerate(decoder_outputs):\n if i > 0:\n tf.get_variable_scope().reuse_variables()\n vocab_scores.append(tf.nn.xw_plus_b(output, w, b)) # apply the linear layer\n\n # the final vocab distribution for each decoder time step\n # shape of each element is [batch_size, vsize]\n vocab_dists = [tf.nn.softmax(s) for s in vocab_scores] \n\n \n # pointing / generating\n if FLAGS.pointer_gen:\n final_dists = self._calc_final_dist(vocab_dists, self.attn_dists)\n# log_dists = [tf.log(dist) for dist in final_dists]\n else:\n# log_dists = [tf.log(dist) for dist in vocab_dists]\n final_dists = vocab_dists\n \n\n # Calculate Losses:\n \n if self._hps.mode in ['train', 'eval']:\n # Calculate the loss\n with tf.variable_scope('loss'), tf.device(self._next_device()):\n if FLAGS.pointer_gen:\n # Calculate the loss per step\n # This is fiddly; we use tf.gather_nd to pick out the gold target words\n # will be list length max_dec_steps containing shape (batch_size)\n loss_per_step = [] \n batch_nums = tf.range(0, limit=hps.batch_size) # shape (batch_size)\n for dec_step, dist in enumerate(final_dists):\n # The indices of the target words. shape (batch_size)\n targets = self._target_batch[:,dec_step] \n indices = tf.stack( (batch_nums, targets), axis=1) # shape (batch_size, 2)\n # shape (batch_size). loss on this step for each batch\n gold_probs = tf.gather_nd(dist, indices)\n losses = -tf.log(gold_probs)\n loss_per_step.append(losses)\n\n # Apply dec_padding_mask mask and get loss\n self._loss = _mask_and_avg(loss_per_step, self._dec_padding_mask)\n \n\n else: # baseline model\n # this applies softmax internally\n self._loss = tf.contrib.seq2seq.sequence_loss(\n tf.stack(vocab_scores, axis=1), self._target_batch, self._dec_padding_mask) # this applies softmax internally\n\n tf.summary.scalar('loss', self._loss)\n\n # Calculate coverage loss from the attention distributions\n if self._hps.coverage:\n with tf.variable_scope('coverage_loss'):\n self._coverage_loss = _coverage_loss(self.attn_dists, self._dec_padding_mask)\n tf.summary.scalar('coverage_loss', self._coverage_loss)\n self._total_loss = self._loss + self._hps.cov_loss_wt * self._coverage_loss\n tf.summary.scalar('total_loss', self._total_loss)\n \n # ---------------------------/\n\n\n if self._hps.mode == \"decode\":\n assert len(final_dists) == 1 # final_dists is a singleton list containing shape (batch_size, extended_vsize)\n final_dists = final_dists[0]\n topk_probs, self._topk_ids = tf.nn.top_k(final_dists, hps.batch_size*2) # take the k largest probs. note batch_size=beam_size in decode mode\n self._topk_log_probs = tf.log(topk_probs)","def forward(self, input):\n ### YOUR CODE HERE for part 1h\n # sentence_length x batch_size x max_word_length\n orig_size = input.size()\n merged_input = input.reshape(-1, orig_size[-1])\n # s*b x max_word_length\n x_char_embed = self.char_embed(merged_input)\n # s*b x max_word_length x char_embed_size\n x_reshaped = x_char_embed.transpose(-1, -2)\n # s*b x char_embed_size x max_word_length\n x_conv = self.conv(x_reshaped)\n # out s*b x word_embed_size\n x_emb = self.highway(x_conv)\n x_drop = self.dropout(x_emb)\n split_output = x_drop.reshape(list(orig_size[:-1]) + [self.word_embed_size])\n return split_output\n ### END YOUR CODE","def word2vec_main(df: pd.DataFrame,\n window_size: int = WINDOW_SIZE,\n layer_size: int = VECTOR_DEPTH,\n negative_sample_size: int = NEG_SAMPLE_SIZE,\n num_epochs: int = NUM_EPOCHS, \n learn_rate: float = LEARN_RATE,\n dist_alpha: float = DIST_ALPHA,\n verbose: bool = VERBOSE\n ) -> tuple: \n \n # Separate the keywords and the sentences from the DataFrame\n keywords, corpus = separate_keywords_corpus(df)\n \n # Preprocess the corpus\n print(\"Commencing data preprocessing...\")\n w2vp = W2VPreprocessor(keywords, corpus)\n \n # Obtain a noise distribution containing vocabulary for use within the\n # negative sampling\n noise_distribution = get_noise_distribution(\n w2vp.preprocess_corpus, w2vp.vocabulary, dist_alpha\n )\n \n # Encode the words such that they correspond with matrix indices\n word2onehot = one_hot_vocab_encoding(w2vp)\n \n # Initialise the embedding and contex matrices\n matrices = get_matrices((w2vp.vocab_size, layer_size))\n\n # Initialise the dataset via the skipgrams methodology\n skipgram_data = obtain_skipgram_dataset(\n w2vp.preprocess_corpus, \n window_size\n )\n # print(skipgram_data.head(15))\n \n if verbose:\n print(\n f\"\\nTraining Word2vec, via skipgrams and negative sampling, \" \n f\"using the following parameters. \\n\"\n f\"Vocabulary size: {w2vp.vocab_size} \\n\"\n f\"Window size: {window_size} \\n\"\n f\"Word vector depth: {layer_size} \\n\"\n f\"Negative sample size: {negative_sample_size} \\n\"\n f\"Distribution parameter: {dist_alpha} \\n\"\n f\"Number of epochs: {num_epochs} \\n\"\n f\"Learning rate (alpha): {learn_rate} \\n\"\n )\n \n # Train the model to obtain the final embedding and context matrices. The\n # embedding matrix will contain the final word vectors, which can be \n # extracted using the one hot encodings of the original vocabulary\n matrices = train_w2v(\n skipgram_data, \n matrices, \n word2onehot, \n w2vp.vocabulary, \n noise_distribution,\n negative_sample_size, \n num_epochs,\n learn_rate,\n verbose\n )\n \n return word2onehot, w2vp, matrices","def embedding_sentence_with_model(input_file, save_path, max_length, model_path):\n # load glove model\n model = gensim.models.KeyedVectors.load_word2vec_format(model_path)\n lines = _read_csv(input_file)\n split_lines = []\n label_list = []\n for line in lines:\n split_lines.append(sentence_split(line[1], max_length))\n label_list.append(int(line[2]))\n del lines\n\n writer = tf.python_io.TFRecordWriter(save_path)\n for index, line in enumerate(split_lines):\n bytes_words = []\n for word in line:\n if word in model:\n bytes_words.extend(model[word])\n else:\n bytes_words.extend([0] * 300)\n example = tf.train.Example(features=tf.train.Features(feature={\n \"label\":\n tf.train.Feature(int64_list=tf.train.Int64List(value=[label_list[index]])),\n \"features\":\n tf.train.Feature(float_list=tf.train.FloatList(value=bytes_words))\n }))\n writer.write(example.SerializeToString())","def train_model(self, d=0.7):\n #eg: model = spammy.train_model()\n count_dict = self.freq_count()\n N = sum(count_dict.values())\n n_plus = len(count_dict)\n alpha = (d * n_plus) / N\n normalizer = alpha * (1/len(self._vocab_set))\n model = defaultdict(lambda:normalizer)\n \n for word in set(self._vocab_set):\n prob = normalizer\n if word in count_dict:\n prob += (count_dict[word] - d) / N\n prob = math.log(prob)\n model[word] = prob\n return model","def update_embedding_layer(line_tokens, model, dictionary):\n\n global elmo_embedder\n\n out_of_corpus_vocab = [word for word in line_tokens if word not in dictionary.word2idx]\n if len(out_of_corpus_vocab) == 0:\n return\n\n print(\"OOV words found:\", out_of_corpus_vocab, file=sys.stderr)\n\n if model.using_pretrained == \"fasttext\":\n pretrained_emb_model = vc.FastText()\n elif model.using_pretrained == \"glove\":\n pretrained_emb_model = vc.GloVe()\n elif model.using_pretrained == \"elmo_top\" or model.using_pretrained == \"elmo_avg\":\n dirname = os.path.dirname(__file__)\n options_file = os.path.join(dirname, '../elmo-config/elmo_2x4096_512_2048cnn_2xhighway_options.json')\n weight_file = os.path.join(dirname, '../elmo-config/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5')\n # Retrieve cached version if already loaded, saves a lot of time if this code path is executed multiple times\n if elmo_embedder is None:\n elmo_embedder = ElmoEmbedder(options_file, weight_file)\n else:\n raise Exception(\"Unsupported embedding model:\", model.using_pretrained)\n\n print(\"Using Pretrained embedding:\", model.using_pretrained)\n\n if model.using_pretrained in ['fasttext', 'glove']:\n\n pretrained_vectors = pretrained_emb_model.vectors\n pretrained_stoi = pretrained_emb_model.stoi\n\n elif 'elmo' in model.using_pretrained:\n # We get the embeddings for all line_tokens which includes both known and unknown, if any.\n # As elmo is character level we can find embedding of any word\n reduced_vocab = [token for token in line_tokens if token not in\n [\"#\", \"<EOT>\", \"<EOL>\", \"</s>\", \"<eos>\", \"<P>\", \"<unk>\"]]\n pretrained_stoi = {v: k for k, v in enumerate(reduced_vocab)}\n elmo_embeddings = elmo_embedder.embed_sentence(reduced_vocab)\n if 'top' in model.using_pretrained:\n pretrained_vectors = elmo_embeddings[-1]\n elif 'avg' in model.using_pretrained:\n pretrained_vectors = np.average(elmo_embeddings, axis=0)\n pretrained_vectors = torch.from_numpy(pretrained_vectors)\n out_of_corpus_vocab = [word for word in line_tokens if word not in dictionary.word2idx and word in pretrained_stoi]\n\n if len(out_of_corpus_vocab) == 0:\n return\n\n # Update for only unknown/new word\n new_vectors = []\n for word in out_of_corpus_vocab:\n dictionary.add_word(word)\n new_vectors.append(pretrained_stoi[word])\n\n new_vectors = torch.index_select(pretrained_vectors, 0, torch.LongTensor(new_vectors))\n\n model.embedder = torch.nn.Embedding.from_pretrained(torch.cat([model.embedder.weight, new_vectors]))\n if model.tie_weights:\n model.decoder.weight = model.encoder.weight","def load_char_embeddings(self, embedding_dim):\n self.char_embeddings = np.random.rand(self.char_size(), embedding_dim)\n self.word_char_embeddings = np.random.rand(self.size(), embedding_dim)\n for term in [self.pad_term, self.unk_term, self.eos_term]:\n self.word_char_embeddings[self.get_id(term)] = np.zeros([self.embed_dim])\n # self.word_char_embeddings = self.embeddings\n char_emb = CharEmbedding(embedding_dim)\n for term in self.term2id.keys():\n if term not in self.initial_terms:\n char_ids = [self.char_embeddings[self.char2id[char]] for char in term]\n chars = torch.FloatTensor(char_ids)\n chars = torch.unsqueeze(chars, 1)\n\n hidden = char_emb(chars)\n self.word_char_embeddings[self.get_id(term)] = hidden.data.numpy()\n # self.embeddings[self.get_id(term)] = np.zeros([self.embed_dim])\n # self.embeddings = np.random.rand(self.size(), embedding_dim)","def makeD2VLabels(sequences, **kargs): # refactored from seqAnalzyer \n # from collections import namedtuple # can customize your own attributes (instead of using gensim's attributes such as words and tags)\n import gensim\n def index_label(i): \n return '%s_%s' % (label_prefix, i)\n\n # [params] redundant? \n # cohort_name = kargs.get('cohort', 'diabetes')\n # seq_ptype = kargs.get('seq_ptype', 'regular') # values: regular, random, diag, med, lab ... default: regular\n\n # attributes = D2V.label_attributes # ['codes', 'labels', ] \n\n # [old] use gensim.models.doc2vec.TaggedDocument\n # LabelDoc = namedtuple('LabelDoc', attributes) # a namedtuple with 2 attributes words and tags\n # LabelDoc = namedtuple('LabelDoc', ['words', 'labels'])\n label_prefix = seqparams.TDoc.doc_label_prefix \n exclude = set(string.punctuation)\n all_docs = []\n\n # [input]\n assert sequences is not None and len(sequences) > 0\n\n labels = kargs.get('labels', []) # precomputed sentence labels \n if not labels: \n # df_ldoc = labelDoc(sequences, load_=load_label, seqr='full', sortby='freq', seq_ptype=seq_ptype)\n raise ValueError, \"No user-defined labels given.\"\n \n # [note] below is for generating surrogate class labels \n # labeling_routine = kargs.get('labeler', labelDocByFreqDiag) # any labelDoc*\n # assert hasattr(labeling_routine, '__call__'), \"Invalid labeler: %s\" % labeling_routine\n # labels = mlabels = labeling_routine(sequences, **kargs)\n # labelx = labelize()\n else: \n assert len(labels) == len(sequences)\n\n # label normalization: ensure that each label is a list \n labelx = TDocTag.labelAsIs(labels) # TDocTag.canonicalize(labels)\n print('makeD2VLabels> doc tag examples:\\n%s\\n' % labelx[:10])\n # each element in tagx should be a list\n\n for i, sen in enumerate(sequences):\n if isinstance(sen, str): \n word_list = sen.split() \n else: \n word_list = sen # split is already done\n\n # For every sentences, if the length is less than 3, we may want to discard it\n # as it seems too short. \n # if len(word_list) < 3: continue # filter short sentences\n \n tagl = labelx[i] # condition tagl is in the list (multilabel) format\n assert isinstance(tagl, list)\n if isinstance(sen, str): \n sen = ''.join(ch for ch in sen if ch not in exclude) # filter excluded characters\n\n all_docs.append(gensim.models.doc2vec.TaggedDocument(sen.split(), tagl))\n # all_docs.append(LabelDoc(sen.split(), tagl)) # format: sequence (list of tokens) + labels (a list of labels)\n else: \n\n all_docs.append(gensim.models.doc2vec.TaggedDocument(sen, tagl))\n # all_docs.append(LabelDoc(sen, tagl)) # assuming unwanted char already filetered \n\n # Print out a sample for one to view what the structure is looking like \n # print all_docs[0:10]\n for i, doc in enumerate(all_docs[0:5]+all_docs[-5:]): \n print('> doc #%d: %s' % (i, doc))\n # [log] e.g. doc #3: LabelDoc(words=['583.81', '250.41', 'V45.81', ... , '48003'], tags=['362.01_599.0_250.51'])\n\n return all_docs","def get_weibo_data(vocab_file, vector_file):\n if os.path.exists(\"word_misc.pkl\"):\n return cPickle.load(open(\"word_misc.pkl\", \"rb\"))\n\n word_misc, word2id, id2word = {}, {}, {}\n word_count = 0\n\n # vocab file\n print \"Building vocabulary ...\"\n for lines in open(vocab_file).readlines():\n word = lines.split()[0]\n if not is_unwanted_words(word, ['', '\\n']):\n word2id[word] = word_count\n id2word[word_count] = word\n word_count += 1\n word2id['_START'] = word_count\n id2word[word_count] = '_START'\n word_count += 1\n word2id['_END'] = word_count\n id2word[word_count] = '_END'\n word_count += 1\n word2id['_UNK'] = word_count\n id2word[word_count] = '_UNK'\n word_count += 1\n word2id['_MASK'] = word_count\n id2word[word_count] = '_MASK'\n word_count += 1\n print \"Vocabulary size:\", word_count\n\n # Initialization is refered to in https://www.tensorflow.org/versions/r0.7/tutorials/word2vec/index.html\n word_emb = (1/np.sqrt(word_count)*(2*np.random.rand(word_count, options['embedding_size']) - 1)).tolist()\n\n # load word vectors\n for lines in open(vector_file).readlines()[1:]:\n word = lines.split()[0]\n #if word == '</s>' or word not in word2id.keys():\n # continue\n if word not in word2id.keys():\n continue\n ids = word2id[word]\n #print ids, lines, len(word_emb)\n word_emb[ids] = [float(w) for w in lines.split()[1:]]\n\n print len(word_emb), \"words have been loaded with\", len(word_emb[0]), \"dimensions\"\n\n # load word misc\n word_misc['id2word'] = id2word\n word_misc['word2id'] = word2id\n word_misc['word_count'] = word_count\n word_misc['word_emb'] = word_emb\n cPickle.dump(word_misc, open(\"word_misc.pkl\", \"wb\"))\n print \"Dump complete.\"\n return word_misc","def __init__(self, lstm_step=80, input_d=300, vocab_size=2196018, embedding=None):\n self.raw_premise = tf.placeholder(shape=[None, lstm_step], dtype=tf.int32, name='premise')\n self.premise_length = tf.placeholder(shape=[None], dtype=tf.int32, name='premise_length')\n\n self.raw_hypothesis = tf.placeholder(shape=[None, lstm_step], dtype=tf.int32, name='hypothesis')\n self.hypothesis_length = tf.placeholder(shape=[None], dtype=tf.int32, name='hypothesis_length')\n\n self.label = tf.placeholder(shape=[None], dtype=tf.int32)\n # Those operations take too many memory\n # Use cpu for those operations (deprecated when using truncate embedding)\n if embedding is not None:\n self.input_embedding = tf.placeholder(dtype=tf.float32, shape=embedding.shape, name='word_embedding')\n self.embedding = tf.Variable(tf.zeros(embedding.shape, dtype=tf.float32))\n else:\n \"\"\"\n If embedding is not provided, then use random number as embedding\n \"\"\"\n self.embedding = tf.Variable(tf.random_uniform([vocab_size, input_d], minval=-0.05, maxval=0.05))\n \"\"\"\n This is the embedding operation. It will be invoked by loading embedding function in the actual model\n \"\"\"\n self.load_embedding_op = self.embedding.assign(self.input_embedding)\n\n self.premise = tf.nn.embedding_lookup(self.embedding, self.raw_premise)\n self.hypothesis = tf.nn.embedding_lookup(self.embedding, self.raw_hypothesis)","def process_decoder_input(target_data, target_vocab_to_int, batch_size):\n # Create a constant tensor with the 'go id'.\n go_id = tf.constant(target_vocab_to_int['<GO>'], shape=(batch_size,1), dtype=tf.int32)\n # Concatenate the vector without the last word id with the go ids vector\n processed_input = tf.concat([go_id,target_data[:,:-1]],1)\n return processed_input","def shape_datasets(built_eps, word2id_dict, max_l, n_relation_types):\r\n # min_pf = -n_pf/2\r\n # max_pf = n_pf/2 # 左闭右开\r\n # assert pf_pad < max_pf\r\n #\r\n # pf_pad_dict = dict() # 为了复用idx_pad_sentence()\r\n # for i in range(min_pf, max_pf):\r\n # pf_pad_dict[str(i)] = i\r\n\r\n train_x, train_y, test_x, test_y = [], [], [], []\r\n for ep in built_eps: # 每个ep一个list. list中每个元素是ep对应的每个mention sentence对应的word list\r\n ep_sent_withPF = []\r\n for i in range(len(ep[\"sentences\"])):\r\n sentence = ep[\"sentences\"][i]\r\n indexed_sent = idx_sentence(sentence, word2id_dict)\r\n padded_idx_sent = pad_indexed_sentence(indexed_sent, max_l, 0)\r\n # pf1_str = \" \".join([str(pfp[0]) for pfp in pf_sent]) # 为了复用idx_pad_sentence, 来给pf打pad.\r\n # pf2_str = \" \".join([str(pfp[1]) for pfp in pf_sent])\r\n # padded_pf1 = pad_indexed_sentence(pf1_sent, max_l, pf_pad_mark)\r\n # padded_pf2 = pad_indexed_sentence(pf2_sent, max_l, pf_pad_mark)\r\n sent_withPF = [padded_idx_sent, ep[\"PFinfo\"][i]]\r\n ep_sent_withPF.append(sent_withPF)\r\n # word_indexed_sents = [idx_pad_sentence(sentence, word2idx_dict, max_l) for sentence in ep[\"sentences\"]]\r\n # labels转vec\r\n labels_vec = [0] * n_relation_types\r\n for label_idx in ep['labels']: # 所以如果这时0s_as_NA=True, ep['labels']为空dict, label_vec全0.\r\n labels_vec[label_idx] = 1\r\n\r\n if ep[\"ds_split\"] == \"test\":\r\n test_x.append(ep_sent_withPF)\r\n test_y.append(labels_vec)\r\n elif ep[\"ds_split\"] == \"train\":\r\n train_x.append(ep_sent_withPF)\r\n train_y.append(labels_vec)\r\n else:\r\n logging.error('ds_split is {0}, not the required(train or test).'.format(ep[\"ds_split\"]))\r\n return train_x, train_y, test_x, test_y","def add_positional_embedding_nd(x, max_length, name=None):\n with tf.name_scope(\"add_positional_embedding_nd\"):\n x_shape = x.shape.as_list()\n num_dims = len(x_shape) - 2\n depth = x_shape[-1]\n base_shape = [1] * (num_dims + 1) + [depth]\n base_start = [0] * (num_dims + 2)\n base_size = [-1] + [1] * num_dims + [depth]\n for i in range(num_dims):\n shape = base_shape[:]\n start = base_start[:]\n size = base_size[:]\n shape[i + 1] = max_length\n size[i + 1] = x_shape[i + 1]\n var = tf.get_variable(\n name + \"_%d\" % i,\n shape,\n initializer=tf.random_normal_initializer(0, depth ** -0.5))\n var = var * depth ** 0.5\n x += tf.slice(var, start, size)\n return x","def train(self, tagged_sentences: Iterator[Tuple[TokenSeq, PosSeq]]) -> Tuple[NDArray, NDArray]:\n #add tokens\n for sentence in tagged_sentences:\n tokens, pos_tags = sentence\n for pos in pos_tags:\n self.pos_tags.append(pos)\n pos_tags.insert(0, \"<s>\")\n pos_tags.pop(len(pos_tags) - 1)\n for i in range(0, len(tokens)):\n temp_dict = {}\n temp_dict = add_features(tokens,pos_tags[i],i, temp_dict)\n self.features.append(temp_dict)\n #print(self.features)\n feature_matrix = self.vectorizer.fit_transform(self.features)\n label_vector = self.le.fit_transform(self.pos_tags)\n for i in range(0, len(label_vector)):\n self.l[self.pos_tags[i]] = i\n \n self.feature_matrix = feature_matrix\n self.label_vector = label_vector\n self.clf.fit(self.feature_matrix, self.label_vector)\n\n return (self.feature_matrix, label_vector)","def glove():\n import numpy as np\n \"\"\" Read from Gluons embedding pickle files\"\"\"\n with np.load(glove_model_path) as f:\n matrix = f['idx_to_vec']\n matrix.setflags(write=0)\n return matrix, f['idx_to_token'], token_to_idx(f['idx_to_token'])","def build_embedding_layer(inputs_, vocab_size, embed_size):\n embedding = tf.Variable(tf.random_uniform((vocab_size, embed_size), -1, 1))\n embed = tf.nn.embedding_lookup(embedding, inputs_)\n \n return embed"],"string":"[\n \"def build_model(allidx,MAX_LENGTH,onlyArg):\\n wordidx, labelidx, featuresidx, extraidx=allidx\\n posidx, neridx, depidx, distanceidx, chnkidx, wikineridx, dbpedianeridx, subneridx = featuresidx\\n\\n main_input = Input(shape=(MAX_LENGTH,), name='main_input', dtype='int32')\\n inputNodes=[main_input]\\n\\n w2vmodel=\\\"../embeddings/Domain-Word2vec.model\\\"\\n\\n embedding_matrix,EMBEDDING_DIM,vocabulary_size=prepare.wv_embedded(wordidx,w2vmodel)\\n \\n x = Embedding(output_dim=EMBEDDING_DIM, weights=[embedding_matrix],input_dim=vocabulary_size, input_length=MAX_LENGTH, mask_zero=False)(main_input)\\n numnode=int(EMBEDDING_DIM/2)\\n\\n # pos embedding\\n inputNodes,pos_layer=layers.embedlayer(inputNodes,\\\"pos_input\\\",posidx,MAX_LENGTH)\\n x=Concatenate()([x,pos_layer])\\n numnode+=int(len(posidx)/2)\\n\\n # ner embedding\\n inputNodes,ner_layer=layers.embedlayer(inputNodes,\\\"ner_input\\\",neridx,MAX_LENGTH)\\n x=Concatenate()([x,ner_layer])\\n numnode+=int(len(neridx)/2)\\n\\n inputNodes,wikiner_layer=layers.embedlayer(inputNodes,\\\"wikiner_input\\\",wikineridx,MAX_LENGTH)\\n x=Concatenate()([x,wikiner_layer])\\n numnode+=int(len(wikineridx)/2)\\n\\n inputNodes,dbpedianer_layer=layers.embedlayer(inputNodes,\\\"dbpedianer_input\\\",dbpedianeridx,MAX_LENGTH)\\n x=Concatenate()([x,dbpedianer_layer])\\n numnode+=int(len(dbpedianeridx)/2)\\n\\n # dep embedding\\n inputNodes,dep0_layer=layers.embedlayer(inputNodes,\\\"dep0_input\\\",depidx,MAX_LENGTH)\\n x=Concatenate()([x,dep0_layer])\\n numnode+=int(len(depidx)/2)\\n\\n inputNodes,dep1_layer=layers.embedlayer(inputNodes,\\\"dep1_input\\\",depidx,MAX_LENGTH)\\n x=Concatenate()([x,dep1_layer])\\n numnode+=int(len(depidx)/2)\\n\\n inputNodes,dep2_layer=layers.embedlayer(inputNodes,\\\"dep2_input\\\",depidx,MAX_LENGTH)\\n x=Concatenate()([x,dep2_layer])\\n numnode+=int(len(depidx)/2)\\n\\n # chnk embedding\\n inputNodes,lvl_layer=layers.embedlayer(inputNodes,\\\"lvl_input\\\",distanceidx,MAX_LENGTH)\\n x=Concatenate()([x,lvl_layer])\\n numnode+=int(len(distanceidx)/2)\\n\\n inputNodes,chnk_layer=layers.embedlayer(inputNodes,\\\"chnk_input\\\",chnkidx,MAX_LENGTH)\\n x=Concatenate()([x,chnk_layer])\\n numnode+=int(len(chnkidx)/2)\\n\\n # wikiclass embedding\\n inputNodes,subner_layer=layers.embedlayer(inputNodes,\\\"subner_input\\\",subneridx,MAX_LENGTH)\\n x=Concatenate()([x,subner_layer])\\n numnode+=int(len(subneridx)/2)\\n\\n if onlyArg:\\n neartrigger_input = Input(shape=(MAX_LENGTH,), name='neartrigger_input', dtype='int32')\\n inputNodes.append(neartrigger_input)\\n neartrigger_layer = Embedding(output_dim=EMBEDDING_DIM, weights=[embedding_matrix],input_dim=vocabulary_size, \\\\\\n input_length=MAX_LENGTH, mask_zero=False)(neartrigger_input)\\n x=Concatenate()([x,neartrigger_layer])\\n numnode+=50\\n inputNodes,x,numnode=layers.extralayer(inputNodes,x,numnode,extraidx,featuresidx,MAX_LENGTH)\\n\\n lstm_out = Bidirectional(LSTM(numnode, dropout=0.2,return_sequences=True))(x)\\n numnode=int((numnode+len(labelidx))*2/3)\\n\\n if onlyArg:\\n lstm_out = SeqSelfAttention(attention_activation='tanh', attention_width=5)(lstm_out)\\n\\n lstm_out = Dropout(0.2)(lstm_out)\\n out = Dense(numnode)(lstm_out)\\n\\n crf = CRF(len(labelidx), sparse_target=False) # CRF layer\\n main_output=crf(out)\\n loss=crf_loss #crf.loss_function\\n acc=[crf_accuracy]\\n\\n model = Model(inputs=inputNodes, outputs=main_output) \\n model.compile(loss=loss,optimizer=Adam(0.001),metrics=acc)\\n model.summary()\\n\\n return model\",\n \"def pass_word(word, model, input_embedding, char2idx, device, use_head=True):\\n inp = torch.LongTensor([char2idx['START']] + [char2idx[c] for c in word]).to(device)\\n inp = pack([inp])\\n out, hidden = model(input_embedding.unsqueeze(0), inp, use_head=use_head)\\n return out, hidden\",\n \"def build_bilstm(self, verbose=True):\\r\\n word_ids = Input(batch_shape=(None, None), dtype='int32', name='word_input')\\r\\n inputs = [word_ids]\\r\\n\\r\\n if self._params.use_pretrain_embedding:\\r\\n if verbose: logging.info(\\\"initial word embedding with pretrained embeddings\\\")\\r\\n if self._params.word_embedding_dim == 100:\\r\\n glove_file = self._params.data_dir + '/glove.6B.100d.txt'\\r\\n elif self._params.word_embedding_dim == 300:\\r\\n glove_file = self._params.data_dir + '/glove.42B.300d.txt'\\r\\n else:\\r\\n logging.error(\\\"we only support glove embedding with dimension 100 or 300\\\")\\r\\n raise ValueError(\\\"unmatch word dimension, we only support glove embedding with dimension 100 or 300\\\")\\r\\n glove_embedding_index = load_glove(glove_file, self._params.word_embedding_dim)\\r\\n word_vocab = self.input_processor.word_vocab.vocab\\r\\n glove_embeddings_matrix = np.zeros([len(word_vocab), self._params.word_embedding_dim])\\r\\n for word, i in word_vocab.items():\\r\\n vector = glove_embedding_index.get(word)\\r\\n if vector is not None:\\r\\n glove_embeddings_matrix[i] = vector\\r\\n \\r\\n word_embeddings = Embedding(input_dim=glove_embeddings_matrix.shape[0],\\r\\n output_dim=glove_embeddings_matrix.shape[1],\\r\\n trainable=False,\\r\\n mask_zero=True,\\r\\n weights=[glove_embeddings_matrix],\\r\\n name='word_embedding')(word_ids)\\r\\n else:\\r\\n word_embeddings = Embedding(input_dim=self._params.word_vocab_size,\\r\\n output_dim=self._params.word_embedding_dim,\\r\\n mask_zero=True,\\r\\n name='word_embedding')(word_ids)\\r\\n\\r\\n input_embeddings = [word_embeddings]\\r\\n if self._params.use_char:\\r\\n char_ids = Input(batch_shape=(None, None, None), dtype='int32', name='char_input')\\r\\n inputs.append(char_ids)\\r\\n if self._params.char_feature == \\\"lstm\\\":\\r\\n char_embeddings = Embedding(input_dim=self._params.char_vocab_size,\\r\\n output_dim=self._params.char_embedding_dim,\\r\\n mask_zero=True,\\r\\n name='char_embedding')(char_ids)\\r\\n if verbose: logging.info(\\\"using charcter level lstm features\\\")\\r\\n char_feas = TimeDistributed(Bidirectional(LSTM(self._params.char_lstm_size)), name=\\\"char_lstm\\\")(char_embeddings)\\r\\n elif self._params.char_feature == \\\"cnn\\\":\\r\\n # cnn do not support mask\\r\\n char_embeddings = Embedding(input_dim=self._params.char_vocab_size,\\r\\n output_dim=self._params.char_embedding_dim,\\r\\n name='char_embedding')(char_ids)\\r\\n if verbose: logging.info(\\\"using charcter level cnn features\\\")\\r\\n char_feas = char_cnn_encode(char_embeddings, self._params.n_gram_filter_sizes, self._params.n_gram_filter_nums)\\r\\n else:\\r\\n raise ValueError('char feature must be lstm or cnn')\\r\\n\\r\\n input_embeddings.append(char_feas)\\r\\n\\r\\n if self._params.use_pos:\\r\\n if verbose: logging.info(\\\"use pos tag features\\\")\\r\\n pos_ids = Input(batch_shape=(None, None), dtype='int32', name='pos_input')\\r\\n inputs.append(pos_ids)\\r\\n\\r\\n\\r\\n pos_embeddings = Embedding(input_dim=self._params.pos_vocab_size,\\r\\n output_dim=self._params.pos_embedding_dim,\\r\\n mask_zero=True,\\r\\n name='pos_embedding')(pos_ids)\\r\\n input_embeddings.append(pos_embeddings)\\r\\n\\r\\n if self._params.use_dict:\\r\\n if verbose: logging.info(\\\"use user dict features\\\")\\r\\n dict_ids = Input(batch_shape=(None, None), dtype='int32', name='dict_input')\\r\\n inputs.append(dict_ids)\\r\\n\\r\\n dict_embeddings = Embedding(input_dim=self._params.dict_vocab_size,\\r\\n output_dim=self._params.dict_embedding_dim,\\r\\n mask_zero=True,\\r\\n name='dict_embedding')(dict_ids)\\r\\n input_embeddings.append(dict_embeddings)\\r\\n\\r\\n input_embedding = Concatenate(name=\\\"input_embedding\\\")(input_embeddings) if len(input_embeddings)>1 else input_embeddings[0]\\r\\n input_embedding_ln = LayerNormalization(name='input_layer_normalization')(input_embedding)\\r\\n #input_embedding_bn = BatchNormalization()(input_embedding_ln)\\r\\n input_embedding_drop = Dropout(self._params.dropout, name=\\\"input_embedding_dropout\\\")(input_embedding_ln)\\r\\n\\r\\n z = Bidirectional(LSTM(units=self._params.main_lstm_size, return_sequences=True, dropout=0.2, recurrent_dropout=0.2),\\r\\n name=\\\"main_bilstm\\\")(input_embedding_drop)\\r\\n z = Dense(self._params.fc_dim, activation='tanh', name=\\\"fc_dense\\\")(z)\\r\\n\\r\\n if self._params.use_crf:\\r\\n if verbose: logging.info('use crf decode layer')\\r\\n crf = CRF(self._params.num_labels, sparse_target=False,\\r\\n learn_mode='marginal', test_mode='marginal', name='crf_out')\\r\\n loss = crf.loss_function\\r\\n pred = crf(z)\\r\\n else:\\r\\n loss = 'categorical_crossentropy'\\r\\n pred = Dense(self._params.num_labels, activation='softmax', name='softmax_out')(z)\\r\\n\\r\\n model = Model(inputs=inputs, outputs=pred)\\r\\n model.summary(print_fn=lambda x: logging.info(x + '\\\\n'))\\r\\n model.compile(loss=loss, optimizer=self._params.optimizer)\\r\\n\\r\\n self.model = model\",\n \"def pretrained_embedding_layer(model,model2,model3, word_to_index,emb_dim_max):\\n words_ignored = []\\n vocab_len = len(word_to_index) + 1 \\n emb_matrix = np.zeros([vocab_len,emb_dim_max])\\n \\n print(' Total words would be processed : '+str(vocab_len))\\n for word, idx in word_to_index.items():\\n if word in model:\\n emb_matrix[idx,:200] = model[word]\\n emb_matrix[idx,200:] = 0\\n if word in model2:\\n emb_matrix[idx, :100] = model2[word]\\n emb_matrix[idx, 100:] = 0\\n if word in model3.keys():\\n emb_matrix[idx,:] = model3[word]\\n else:\\n words_ignored.append(word)\\n print(str(len(words_ignored))+\\\" words ignored\\\")\\n print(emb_matrix.shape) \\n \\n \\n embedding_layer = Embedding(vocab_len,emb_dim_max,trainable = True)\\n \\n # Build the embedding layer, it is required before setting the weights of the embedding layer. \\n embedding_layer.build((None,)) # Do not modify the \\\"None\\\". This line of code is complete as-is.\\n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\\n embedding_layer.set_weights([emb_matrix])\\n \\n return embedding_layer,words_ignored\",\n \"def text2vec(doc_tok, model, dim=300):\\n doc_embedding = np.zeros(dim)\\n valid_words = 0\\n for word in doc_tok:\\n if word in model:\\n valid_words += 1\\n doc_embedding += model.query(word)\\n else:\\n continue\\n if valid_words > 0:\\n return doc_embedding / valid_words\\n else:\\n return doc_embedding\",\n \"def train_sentence_dm(model, sentence, lbls, alpha, work=None, neu1=None, train_words=True, train_lbls=True):\\n lbl_indices = [lbl.index for lbl in lbls if lbl is not None]\\n lbl_sum = np_sum(model.syn0[lbl_indices], axis=0)\\n lbl_len = len(lbl_indices)\\n neg_labels = []\\n if model.negative:\\n # precompute negative labels\\n neg_labels = zeros(model.negative + 1)\\n neg_labels[0] = 1.\\n\\n for pos, word in enumerate(sentence):\\n if word is None:\\n continue # OOV word in the input sentence => skip\\n reduced_window = random.randint(model.window) # `b` in the original doc2vec code\\n start = max(0, pos - model.window + reduced_window)\\n window_pos = enumerate(sentence[start : pos + model.window + 1 - reduced_window], start)\\n word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)]\\n l1 = np_sum(model.syn0[word2_indices], axis=0) + lbl_sum # 1 x layer1_size\\n if word2_indices and model.cbow_mean:\\n l1 /= (len(word2_indices) + lbl_len)\\n neu1e = train_cbow_pair(model, word, word2_indices, l1, alpha, neg_labels, train_words, train_words)\\n if train_lbls:\\n model.syn0[lbl_indices] += neu1e\\n\\n return len([word for word in sentence if word is not None])\",\n \"def _get_embedding_layer(self, input_data, doc_input_data):\\n opts = self._options\\n word_embedding = tf.Variable(tf.random_uniform((self.vocab_size, opts.embed_dim), -1.0, 1.0))\\n embed = []\\n\\n temp = tf.zeros([opts.batch_size, opts.embed_dim])\\n embed_d = []\\n for n in range(opts.sentence_sample):\\n temp = tf.add(temp, tf.nn.embedding_lookup(word_embedding, doc_input_data[:, n]))\\n embed_d.append(temp)\\n\\n if opts.concat == 'True':\\n combined_embed_vector_length = opts.embed_dim * opts.window_size + opts.embed_dim\\n for j in range(opts.window_size):\\n embed_w = tf.nn.embedding_lookup(word_embedding, input_data[:, j])\\n embed.append(embed_w)\\n embed.append(embed_d)\\n else:\\n combined_embed_vector_length = opts.embed_dim\\n embed_w = tf.zeros([opts.batch_size, opts.embed_dim])\\n for j in range(opts.window_size):\\n embed_w += tf.nn.embedding_lookup(word_embedding, input_data[:, j])\\n embed_w += embed_d\\n embed.append(embed_w)\\n\\n return tf.concat(embed, 1), word_embedding, combined_embed_vector_length\",\n \"def forward(self, doc):\\n out = torch.tensor([]).float().to(self.device)\\n\\n for i in range(len(doc)):\\n sentences_raw = sentencesplit(cleantxt(doc[i]))\\n sentences_ready = torch.tensor([]).float().to(self.device)\\n for sentence in sentences_raw:\\n sentence = sentence.split()\\n if sentence == []:\\n continue\\n lookup_tensor = torch.tensor([]).long().to(self.device)\\n for word in sentence:\\n if word in self.embedd_dict:\\n lookup_tensor = torch.cat((lookup_tensor,\\n torch.LongTensor([self.embedd_dict[word]])), 0)\\n else:\\n lookup_tensor = torch.cat((lookup_tensor, torch.LongTensor([0])), 0)\\n # Word embedding\\n xw = self.word_embedding(lookup_tensor).view(1, -1, self.embedding_dim).to(self.device)\\n # Word GRU\\n self.hidden_gru_words = self.init_hidden_words()\\n hw, self.hidden_gru_words = self.gru_word(xw, self.hidden_gru_words)\\n # Word MLP\\n uw = nn.Tanh()(self.MLP_word(hw)).to(self.device)\\n # Word attention\\n attention_score = torch.matmul(uw, self.attention_word).squeeze().to(self.device)\\n attention_score = F.softmax(attention_score, dim=0).view(uw.size(0), uw.size(1), 1).to(self.device)\\n scored_x = (hw * attention_score).to(self.device)\\n s = torch.sum(scored_x, dim=1).to(self.device)\\n #collecting sentences\\n sentences_ready = torch.cat((sentences_ready, s), 0)\\n # Sentence GRU\\n if len(sentences_ready) == 0:\\n out = torch.cat((out,\\n torch.randn(1, self.number_cat).to(self.device)), 0).to(self.device)\\n continue\\n sentences_ready_gru = sentences_ready.view(1, -1, self.embedding_dim).to(self.device)\\n self.hidden_gru_sentences = self.init_hidden_sentences()\\n hs, self.hidden_gru_sentences = self.gru_sentence(torch.tensor(sentences_ready_gru), self.hidden_gru_sentences)\\n # SENTENCE MLP\\n us = nn.Tanh()(self.MLP_sentence(hs)).to(self.device)\\n # Sentence attention\\n attention_score = torch.matmul(us, self.attention_sentence).squeeze().to(self.device)\\n attention_score = F.softmax(attention_score, dim=0).view(us.size(0), us.size(1), 1).to(self.device)\\n scored_x = (hs * attention_score).to(self.device)\\n v = torch.sum(scored_x, dim=1).to(self.device)\\n # classification\\n p = self.MLP_classification(v).to(self.device)\\n out = torch.cat((out, p.float()), 0).float().to(self.device)\\n return out\",\n \"def get_embed(input_data, vocab_size, embed_dim):\\n embedding = tf.Variable(tf.random_uniform((vocab_size,embed_dim), -1, 1))\\n embed = tf.nn.embedding_lookup(embedding, input_data)\\n #print (\\\"embed_dim: \\\",embed_dim) # 向量表达维度为 256\\n #print (\\\"input_data.shape: \\\",input_data.shape) # (50, 5)\\n #print (\\\"embed.shap: \\\", embed.shape) # word 的向量表达 ==特征 (50, 5, 256) ==(batch_size, num_step, embed_dim)\\n return embed # 返回input的向量表达\",\n \"def forward(self,input,mask,length):\\r\\n max_length = length.max()\\r\\n batch_size = input.shape[0]\\r\\n h,c = self.init_hidden_state(batch_size)\\r\\n embeddings = torch.zeros(batch_size,max_length,self.hidden_size).cuda()\\r\\n end_indx = length.unsqueeze(-1).long().cuda() - 1\\r\\n mask[:,0] = 0\\r\\n mask = mask.scatter_(1,end_indx,1)\\r\\n for t in range(max_length):\\r\\n pad_mask = mask[:,t].unsqueeze(-1).repeat(1,self.hidden_size)\\r\\n h = h*pad_mask[:h.shape[0],:]\\r\\n c = c*pad_mask[:h.shape[0],:]\\r\\n batch_size_t = sum([l >t for l in length])\\r\\n h,c = self.rnn(input[:batch_size_t,t,:], (h[:batch_size_t],c[:batch_size_t])\\r\\n )\\r\\n embeddings[:batch_size_t,t,:] = h\\r\\n \\r\\n word_nums = mask.sum(1)\\r\\n max_word_num = word_nums.max().int()\\r\\n i = 0\\r\\n\\r\\n # take the\\r\\n for num in word_nums:\\r\\n p = (num - word_nums.max()).int()\\r\\n # if mask[i,-1] != 0:\\r\\n # p = p-1\\r\\n # mask[i,p:] = 1\\r\\n if p != 0:\\r\\n mask[i,p:] = 1\\r\\n while mask[i].sum()<word_nums.max():\\r\\n p -= 1\\r\\n mask[i,p:] = 1\\r\\n i += 1\\r\\n index = mask.nonzero() # index[0,:] batch position, index[:,1] frame position\\r\\n index = index[:,0] * mask.shape[1] + index[:,1] - 1 # we take the frame of the boundary as the begaining of next word\\r\\n flat = embeddings.view(-1,embeddings.shape[-1])\\r\\n word_embeddings = flat[index.long()]\\r\\n output = word_embeddings.view(batch_size,max_word_num,-1)\\r\\n output = output.transpose(2,1)\\r\\n\\r\\n\\r\\n\\r\\n return output, word_nums\",\n \"def prepare_word_emb_matrices(experiment):\\r\\n\\r\\n with open(\\\"public_data/stats/stats_train.pkl\\\", 'rb') as stats:\\r\\n stats = pickle.load(stats)\\r\\n vocab = stats[\\\"VOCAB\\\"]\\r\\n stops = [word.lower() for word in set(stopwords.words('english'))]\\r\\n vocab = vocab + stops\\r\\n\\r\\n if experiment == \\\"RANDOM\\\":\\r\\n word_embs = np.random.uniform(low=-1.0, high=1.0, size=(len(vocab), PARAMS[\\\"SIZE\\\"])).astype(\\\"float32\\\")\\r\\n\\r\\n else:\\r\\n word_embs = []\\r\\n count_unk = 0\\r\\n count_kn = 0\\r\\n\\r\\n if experiment == \\\"5\\\":\\r\\n emb_model = KeyedVectors.load_word2vec_format(\\\"public_data/models/experiment_5/embeddings_5.bin\\\",\\r\\n binary=True)\\r\\n elif experiment == \\\"6\\\":\\r\\n emb_model = Word2Vec.load(\\\"public_data/models/experiment_6/embeddings_6\\\")\\r\\n\\r\\n elif experiment in [\\\"7\\\", \\\"8\\\"]:\\r\\n emb_model = FastText.load_fasttext_format(\\\"public_data/models/experiment_%s/embeddings_%s.bin\\\"\\r\\n %(experiment, experiment))\\r\\n for word in vocab:\\r\\n if word in emb_model:\\r\\n word_embs.append(emb_model[word])\\r\\n count_kn += 1\\r\\n else:\\r\\n word_embs.append(np.random.uniform(low=-1.0, high=1.0, size=PARAMS[\\\"SIZE\\\"]))\\r\\n count_unk += 1\\r\\n\\r\\n word_embs = np.array(word_embs).astype(\\\"float32\\\")\\r\\n print(count_unk / (count_kn + count_unk))\\r\\n\\r\\n pad = np.zeros(shape=PARAMS[\\\"SIZE\\\"]).astype(\\\"float32\\\")\\r\\n unk = np.random.uniform(low=-1.0, high=1.0, size=PARAMS[\\\"SIZE\\\"]).astype(\\\"float32\\\")\\r\\n word_embs = np.insert(word_embs, 0, unk, axis=0) #id 1\\r\\n word_embs = np.insert(word_embs, 0, pad, axis=0) #id 0\\r\\n\\r\\n with open(\\\"public_data/embeddings/word_embeddings_%s.pkl\\\" %experiment, 'wb') as out:\\r\\n pickle.dump(word_embs, out, protocol=4)\\r\\n\\r\\n return word_embs\",\n \"def create_embedding_matrix(self):\\n self.id2word = dict([(self.vocab[word]['id'], word) for word in self.vocab])\\n vocab_size = len(self.vocab)\\n result = np.zeros((vocab_size, self.embed_dim))\\n unknown_token_set = set()\\n\\n found_words = 0\\n avg = np.zeros(self.embed_dim)\\n for _ in range(1, vocab_size): # skip PAD embedding (initialize as zero embedding)\\n try:\\n result[_] = self.pretrained_embedding[self.id2word[_]]\\n avg += result[_]\\n found_words += 1\\n except:\\n unknown_token_set.add(_)\\n\\n avg /= found_words\\n for _ in unknown_token_set:\\n result[_] = avg\\n self.embedding = result\\n return found_words, len(self.id2word)\",\n \"def __init__(self, embed_size, vocab):\\n super(ModelEmbeddings, self).__init__()\\n \\n self.embed_size = embed_size\\n self.char_embed_size = 50\\n self.max_word_len = 21\\n self.dropout_rate = 0.3\\n self.vocab = vocab \\n \\n ## A4 code\\n pad_token_idx = vocab.char2id['<pad>']\\n self.embedding = nn.Embedding(num_embeddings =len(vocab.char2id),\\n embedding_dim =self.char_embed_size,\\n padding_idx =pad_token_idx,)\\n \\n self.CNN = CNN(char_embed_size=self.char_embed_size,\\n num_filters=embed_size,\\n max_word_length=self.max_word_len,)\\n self.Highway = Highway(word_embed_size=self.embed_size)\\n self.dropout = nn.Dropout(p=self.dropout_rate)\\n ## End A4 code\\n\\n ### YOUR CODE HERE for part 1j\\n\\n\\n ### END YOUR CODE\",\n \"def construct_embedding(self):\\n i = 0\\n self.load_dicts()\\n embedding_shape = (max(self.word2idx.values()) + 1,\\n self.embedding_size)\\n self.embedding = np.zeros(embedding_shape)\\n\\n with open(self.config.word_vec_fi_glove, 'r') as fi:\\n for line in fi:\\n word_vec = line.split(\\\" \\\")[1:]\\n self.embedding[i, :] = np.array(word_vec, dtype=np.float32)\\n i += 1\\n\\n self.write_embedding()\",\n \"def pretrained_embedding_layer(word_to_vec_map, word_to_index):\\n \\n vocab_size = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\\n any_word = list(word_to_vec_map.keys())[0]\\n emb_dim = word_to_vec_map[any_word].shape[0] # define dimensionality of your GloVe word vectors (= 50)\\n \\n ### START CODE HERE ###\\n # Step 1\\n # Initialize the embedding matrix as a numpy array of zeros.\\n # See instructions above to choose the correct shape.\\n emb_matrix = np.zeros((vocab_size, emb_dim))\\n \\n # Step 2\\n # Set each row \\\"idx\\\" of the embedding matrix to be \\n # the word vector representation of the idx'th word of the vocabulary\\n for word, idx in word_to_index.items():\\n emb_matrix[idx, :] = word_to_vec_map[word]\\n\\n # Step 3\\n # Define Keras embedding layer with the correct input and output sizes\\n # Make it non-trainable.\\n embedding_layer = tensorflow.keras.layers.Embedding(input_dim = vocab_size, output_dim = emb_dim, trainable = False)\\n ### END CODE HERE ###\\n\\n # Step 4 (already done for you; please do not modify)\\n # Build the embedding layer, it is required before setting the weights of the embedding layer. \\n embedding_layer.build((None,)) # Do not modify the \\\"None\\\". This line of code is complete as-is.\\n \\n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\\n embedding_layer.set_weights([emb_matrix])\\n \\n return embedding_layer\",\n \"def set_glove_embedding(self,fpath,embedding_dim):\\n\\t\\temb = np.random.randn(self._count,embedding_dim)\\n#\\ttf.logging.info(emb[0])\\n\\t\\twith open(fpath) as f: #python 3.x support \\n\\t\\t\\tfor k,line in enumerate(f):\\n\\t\\t\\t\\tfields = line.split()\\n\\t\\t\\t\\tif len(fields) - 1 != embedding_dim:\\n\\t\\t\\t\\t\\t# Sometimes there are funny unicode parsing problems that lead to different\\n\\t\\t\\t\\t\\t# fields lengths (e.g., a word with a unicode space character that splits\\n\\t\\t\\t\\t\\t# into more than one colum n). We skip those lines. Note that if you have\\n\\t\\t\\t\\t\\t# some kind of long header, this could result in all of your lines getting\\n\\t\\t\\t\\t\\t# skipped. It's hard to check for that here; you just have to look in the\\n\\t\\t\\t\\t\\t# embedding_misses_file and at the model summary to make sure things look\\n\\t\\t\\t\\t\\t# like they are supposed to.\\n\\t\\t\\t\\t\\t#logger.warning(\\\"Found line with wrong number of dimensions (expected %d, was %d): %s\\\",\\n\\t\\t\\t\\t\\t\\t\\t# embedding_dim, len(fields) - 1, line)\\n\\t\\t\\t\\t\\traise Exception(\\\"Found line with wrong number of dimensions (expected %d, was %d): %s\\\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t embedding_dim, len(fields) - 1, line)\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tword = fields[0]\\n\\t\\t\\t\\tif word in self._word_to_id:\\n\\t\\t\\t\\t\\tvector = np.asarray(fields[1:], dtype='float32')\\n\\t\\t\\t\\t\\temb[self._word_to_id[word]] = vector\\n#\\t\\tif k%1000 == 0:\\n#\\t\\t tf.logging.info('glove : %d',k)\\n\\t\\tself.glove_emb = emb\",\n \"def embeddings_layer(x, Wemb, dim_proj):\\n\\n n_words = x.shape[0]\\n n_max_letters_in_word = x.shape[1]\\n n_batch = x.shape[2]\\n\\n dist = Wemb[x.flatten()].reshape([n_words, n_max_letters_in_word, n_batch, dim_proj])\\n return dist\",\n \"def __getitem__(self, key):\\n \\\"\\\"\\\"\\n 原始batch的数据格式：\\n sent=[\\n word_id_list, # 包括ROOT_ID；0\\n char_id_list, # 包括ROOT_ID；1\\n upos_id_list, # 包括ROOT_ID；2\\n pretrain_vocab_list, # 包括ROOT_ID；3\\n graph_arc_list,\\n ]\\n graph_arc_list=[[head_id,red_id],...]\\n \\\"\\\"\\\"\\n if not isinstance(key, int):\\n raise TypeError\\n if key < 0 or key >= len(self.data):\\n raise IndexError\\n batch = self.data[key]\\n batch_size = len(batch)\\n\\n batch = list(zip(*batch))\\n\\n assert len(batch) == 5 # word_idx_list, char_idx_list, pos_id_list, pretrain_list, arc_list\\n\\n # sort sentences by lens for easy RNN operations\\n lens = [len(x) for x in batch[0]] # 统计所有句长\\n batch, orig_idx = sort_all(batch, lens) # 按照句长排序（降序），易于RNN处理\\n\\n # sort words by lens for easy char-RNN operations\\n # 对char排序，易于char-RNN处理\\n batch_words = [w for sent in batch[1] for w in sent]\\n word_lens = [len(x) for x in batch_words]\\n\\n batch_words, word_orig_idx = sort_all([batch_words], word_lens)\\n batch_words = batch_words[0] # 去掉外层括号\\n word_lens = [len(x) for x in batch_words]\\n\\n # convert to tensors and pad to max length\\n words = batch[0]\\n words = get_long_tensor(words, batch_size)\\n words_mask = torch.eq(words, PAD_ID)\\n\\n wordchars = get_long_tensor(batch_words, len(word_lens))\\n wordchars_mask = torch.eq(wordchars, PAD_ID)\\n\\n upos = get_long_tensor(batch[2], batch_size)\\n pretrained = get_long_tensor(batch[3], batch_size)\\n sentlens = [len(x) for x in batch[0]]\\n arcs = batch[4]\\n return words, words_mask, wordchars, wordchars_mask, upos, pretrained, arcs, orig_idx, word_orig_idx, sentlens, word_lens\",\n \"def _embed(self):\\n with tf.variable_scope('word_embedding'):\\n self.word_embeddings = tf.get_variable(\\n 'word_embeddings',\\n shape=(self.term_vocab.size(), self.term_vocab.embed_dim),\\n initializer=tf.constant_initializer(self.term_vocab.embeddings),\\n trainable=True\\n )\\n self.p_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.p)\\n self.q_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.q)\\n\\n with tf.variable_scope('char_embedding'):\\n self.char_embeddings = tf.get_variable(\\n 'char_embeddings',\\n shape=(self.char_vocab.size(), self.char_vocab.embed_dim),\\n initializer=tf.constant_initializer(self.char_vocab.embeddings),\\n trainable=True\\n )\\n self.p_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.p_char) # [batch, seqlen, max_char_num, embedding_size]\\n self.q_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.q_char)\\n\\n self.p_char_emb = self.cnn_emb(self.p_char_emb, \\\"p_emb\\\")\\n self.q_char_emb = self.cnn_emb(self.q_char_emb, \\\"q_emb\\\")\\n '''\\n self.p_char_emb = tf.reshape(self.p_char_emb, [-1, self.max_char_num, self.emb_size])\\n self.q_char_emb = tf.reshape(self.q_char_emb, [-1, self.max_char_num, self.emb_size])\\n\\n self.p_char_emb = cnn_layer.conv(self.p_char_emb, self.emb_size,\\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\\\"char_conv\\\", reuse=None)\\n self.q_char_emb = cnn_layer.conv(self.q_char_emb, self.emb_size,\\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\\\"char_conv\\\", reuse=True)\\n\\n self.p_char_emb = tf.reduce_max(self.p_char_emb, axis=1) # [batch*seqlen, 1, emb_size]\\n self.q_char_emb = tf.reduce_max(self.q_char_emb, axis=1)\\n\\n batch_size = tf.shape(self.p_word_emb)[0]\\n self.p_char_emb = tf.reshape(self.p_char_emb, [batch_size, -1, self.emb_size])\\n self.q_char_emb = tf.reshape(self.q_char_emb, [batch_size, -1, self.emb_size])\\n\\n self.p_char_emb = tf.nn.dropout(self.p_char_emb, 0.95)\\n self.q_char_emb = tf.nn.dropout(self.q_char_emb, 0.95)\\n '''\\n self.p_emb = tf.concat([self.p_word_emb, self.p_char_emb], -1)\\n self.q_emb = tf.concat([self.q_word_emb, self.q_char_emb], -1)\",\n \"def word2vec2embed(word2vec, word2idx):\\n\\temb_dim = word2vec['the'].shape[0]\\n\\temb = torch.nn.Embedding(len(word2idx), emb_dim) \\n\\temb_matrix = []\\n\\tfor w, idx in word2idx.items():\\n\\t\\tif w in word2vec:\\n\\t\\t\\temb_matrix.append(word2vec[w])\\n\\t\\telse:\\n\\t\\t\\temb_matrix.append(np.zeros(emb_dim,))\\n\\temb.weight.data.copy_(torch.from_numpy(np.array(emb_matrix)))\\n\\treturn emb\",\n \"def _use_embeddings(self, word):\\n if word == \\\"@PAD@\\\":\\n return torch.zeros(self.embeddings_dim)\\n else:\\n return self.embeddings[word]\",\n \"def __init__(self, voc_size=8000, embed_size=100, hid_size=100, trunc=4,\\n model=None):\\n\\n self.log = logging.getLogger(\\\"TEST.Embed\\\")\\n self.log.setLevel(logging.INFO)\\n\\n self.unknown_token = \\\"UNKNOWN_TOKEN\\\"\\n self.sentence_start_token = \\\"SENTENCE_START\\\"\\n self.sentence_end_token = \\\"SENTENCE_END\\\"\\n\\n if model is None:\\n self.log.info(\\\"Initializing RNN parameters and functions...\\\")\\n\\n self.vocabulary_size = voc_size\\n self.embed_size = embed_size\\n self.hidden_size = hid_size\\n self.bptt_truncate = trunc\\n\\n # Instantiate the network weights\\n # I feel like the first and third are switched for some reason...\\n # but it's pretty consistent in the example code. Perhaps it's\\n # backwards for a purpose\\n # The weights going from the input layer to the word embedding\\n # layer (E, in tutorial)\\n weights_ie = np.random.uniform(-np.sqrt(1./voc_size),\\n np.sqrt(1./voc_size),\\n (embed_size, voc_size))\\n\\n # The weights going from input layer to hidden layer\\n # (U, in tutorial)\\n weights_eh = np.random.uniform(-np.sqrt(1./voc_size),\\n np.sqrt(1./voc_size),\\n (3, hid_size, embed_size))\\n\\n # The weights going from hidden layer to hidden layer\\n # (W, in tutorial)\\n weights_hh = np.random.uniform(-np.sqrt(1./voc_size),\\n np.sqrt(1./voc_size),\\n (3, hid_size, hid_size))\\n\\n # The weights going from hidden layer to output layer\\n # (V, in tutorial)\\n weights_ho = np.random.uniform(-np.sqrt(1./voc_size),\\n np.sqrt(1./voc_size),\\n (voc_size, hid_size))\\n\\n # The bias for the hidden units (no bias applied to embedding layer)\\n bias = np.zeros((3, hid_size))\\n\\n # The bias for the output units\\n out_bias = np.zeros(voc_size)\\n\\n self.weights_ie = theano.shared(\\n name='weights_ie',\\n value=weights_ie.astype(theano.config.floatX))\\n\\n self.weights_eh = theano.shared(\\n name='weights_eh',\\n value=weights_eh.astype(theano.config.floatX))\\n\\n self.weights_hh = theano.shared(\\n name='weights_hh',\\n value=weights_hh.astype(theano.config.floatX))\\n\\n self.weights_ho = theano.shared(\\n name='weights_ho',\\n value=weights_ho.astype(theano.config.floatX))\\n\\n self.bias = theano.shared(\\n name='bias',\\n value=bias.astype(theano.config.floatX))\\n\\n self.out_bias = theano.shared(\\n name='out_bias',\\n value=out_bias.astype(theano.config.floatX))\\n\\n self.cache_ie = theano.shared(\\n name='cache_ie',\\n value=np.zeros(weights_ie.shape).astype(theano.config.floatX))\\n\\n self.cache_eh = theano.shared(\\n name='cache_eh',\\n value=np.zeros(weights_eh.shape).astype(theano.config.floatX))\\n\\n self.cache_hh = theano.shared(\\n name='cache_hh',\\n value=np.zeros(weights_hh.shape).astype(theano.config.floatX))\\n\\n self.cache_ho = theano.shared(\\n name='cache_ho',\\n value=np.zeros(weights_ho.shape).astype(theano.config.floatX))\\n\\n self.cache_bias = theano.shared(\\n name='cache_bias',\\n value=np.zeros(bias.shape).astype(theano.config.floatX))\\n\\n self.cache_out_bias = theano.shared(\\n name='cache_out_bias',\\n value=np.zeros(out_bias.shape).astype(theano.config.floatX))\\n\\n self.vocabulary = []\\n self.word_to_index = {}\\n self.index_to_word = []\\n else:\\n self.log.info(\\\"Loading model parameters from saved model...\\\")\\n\\n with open(model, \\\"rb\\\") as modelFile:\\n params = cPickle.load(modelFile)\\n\\n self.vocabulary_size = params[0]\\n self.embed_size = params[1]\\n self.hidden_size = params[2]\\n self.bptt_truncate = params[3]\\n\\n self.weights_ie = params[4]\\n self.weights_eh = params[5]\\n self.weights_hh = params[6]\\n self.weights_ho = params[7]\\n\\n self.vocabulary = params[8]\\n if not self.vocabulary[-1] == self.unknown_token:\\n self.log.info(\\\"Appending unknown token\\\")\\n self.vocabulary[-1] = self.unknown_token\\n self.index_to_word = params[9]\\n self.word_to_index = params[10]\\n\\n self.bias = params[11]\\n self.out_bias = params[12]\\n\\n self.cache_ie = params[13]\\n self.cache_eh = params[14]\\n self.cache_hh = params[15]\\n self.cache_ho = params[16]\\n self.cache_bias = params[17]\\n self.cache_out_bias = params[18]\\n # End of if statement\\n\\n # Symbolic representation of one input sentence\\n input = T.ivector('sentence')\\n\\n # Symbolic representation of the one output sentence\\n output = T.ivector('sentence')\\n\\n # Symbolic representation of the cache decay for RMSprop\\n decay = T.scalar('decay')\\n\\n # Stochastic Gradient Descent step\\n learning_rate = T.scalar('learning_rate')\\n\\n def forward_propagate(word, previous_state):\\n \\\"\\\"\\\"\\n Vertically propagates one of the words.\\n\\n :type word: int\\n :param word: the index of the current input word\\n\\n :type previous_state: T.dvector()\\n :param word: the output of the hidden layer from the previous\\n horizontal layer\\n \\\"\\\"\\\"\\n # Embedding layer\\n word_vector = self.weights_ie[:, word]\\n\\n # GRU layer\\n update_gate = T.nnet.hard_sigmoid(\\n self.weights_eh[0].dot(word_vector) +\\n self.weights_hh[0].dot(previous_state) +\\n self.bias[0]\\n )\\n\\n reset_gate = T.nnet.hard_sigmoid(\\n self.weights_eh[1].dot(word_vector) +\\n self.weights_hh[1].dot(previous_state) +\\n self.bias[1]\\n )\\n\\n hypothesis = T.tanh(\\n self.weights_eh[2].dot(word_vector) +\\n self.weights_hh[2].dot(previous_state * reset_gate) +\\n self.bias[2]\\n )\\n\\n current_state = (T.ones_like(update_gate) - update_gate) * hypothesis + update_gate * previous_state\\n\\n # Output layer\\n current_output = T.nnet.softmax(\\n self.weights_ho.dot(current_state) + self.out_bias\\n )[0]\\n\\n # Not sure why current_output[0] and not just current_output...\\n return [current_output, current_state]\\n\\n #######################################################################\\n # Symbolically represents going through each input sentence word and\\n # then calculating the state of the hidden layer and output word for\\n # each word. The forward_propagate function is the one used to\\n # generate the output word and hidden layer state.\\n #######################################################################\\n self.theano = {}\\n\\n [out, state], updates = theano.scan(\\n forward_propagate,\\n sequences=input,\\n truncate_gradient=self.bptt_truncate,\\n outputs_info=[None, dict(initial=T.zeros(self.hidden_size))],\\n name=\\\"forward_propagate\\\"\\n )\\n\\n # Predicts the output words for each word in the sentence\\n prediction = T.argmax(out, axis=1)\\n\\n # Calculates the output error between the predicted output and the\\n # actual output\\n out_error = T.sum(T.nnet.categorical_crossentropy(out, output))\\n\\n # Symbolically represents gradient calculations for gradient descent\\n d_weights_ie = T.grad(out_error, self.weights_ie)\\n d_weights_eh = T.grad(out_error, self.weights_eh)\\n d_weights_hh = T.grad(out_error, self.weights_hh)\\n d_weights_ho = T.grad(out_error, self.weights_ho)\\n d_bias = T.grad(out_error, self.bias)\\n d_out_bias = T.grad(out_error, self.out_bias)\\n\\n # Symbolic theano functions\\n self.forward_propagate = theano.function([input], out,\\n name=\\\"forward_propagate\\\")\\n self.predict = theano.function([input], prediction, name=\\\"predict\\\")\\n self.calculate_error = theano.function([input, output], out_error,\\n name=\\\"calculate_error\\\")\\n self.bptt = theano.function([input, output],\\n [d_weights_ie, d_weights_eh, d_weights_hh, d_weights_ho, d_bias,\\n d_out_bias],\\n name=\\\"bptt\\\")\\n\\n # RMSprop parameters\\n cache_ie = (decay * self.cache_ie) + ((1 - decay) * d_weights_ie ** 2)\\n cache_eh = (decay * self.cache_eh) + ((1 - decay) * d_weights_eh ** 2)\\n cache_hh = (decay * self.cache_hh) + ((1 - decay) * d_weights_hh ** 2)\\n cache_ho = (decay * self.cache_ho) + ((1 - decay) * d_weights_ho ** 2)\\n cache_bias = (decay * self.cache_bias) + ((1 - decay) * d_bias ** 2)\\n cache_out_bias = (decay * self.cache_out_bias) + ((1 - decay) * d_out_bias ** 2)\\n eps = 1e-6 # Prevents division by 0\\n\\n self.sgd_step = theano.function(\\n [input, output, learning_rate, theano.In(decay, value=0.9)],\\n [],\\n updates=[\\n (self.weights_ie, self.weights_ie - learning_rate *\\n d_weights_ie / (T.sqrt(self.cache_ie + eps))),\\n (self.weights_eh, self.weights_eh - learning_rate *\\n d_weights_eh / (T.sqrt(self.cache_eh + eps))),\\n (self.weights_hh, self.weights_hh - learning_rate *\\n d_weights_hh / (T.sqrt(self.cache_hh + eps))),\\n (self.weights_ho, self.weights_ho - learning_rate *\\n d_weights_ho / (T.sqrt(self.cache_ho + eps))),\\n (self.bias, self.bias - learning_rate * d_bias /\\n (T.sqrt(self.cache_bias + eps))),\\n (self.out_bias, self.out_bias - learning_rate *\\n d_out_bias / (T.sqrt(self.cache_out_bias + eps))),\\n (self.cache_ie, cache_ie),\\n (self.cache_eh, cache_eh),\\n (self.cache_hh, cache_hh),\\n (self.cache_ho, cache_ho),\\n (self.cache_bias, cache_bias),\\n (self.cache_out_bias, cache_out_bias)]\\n )\\n\\n self.x_train = None\\n self.y_train = None\",\n \"def add_word_embedding_op(self):\\n if self.pos:\\n print(\\\"adding pos embeddings\\\")\\n with tf.variable_scope(\\\"pos\\\"):\\n _pos_embeddings = tf.Variable(self.pos_embeddings,\\n name=\\\"la_pos_embeddings\\\",\\n dtype=tf.float32, trainable=False)\\n pos_embeddings = tf.nn.embedding_lookup(_pos_embeddings, self.pos_ids,\\n name=\\\"pos_embeddings\\\")\\n self.pos_vecs = pos_embeddings\\n print(\\\"adding word_embeddings\\\")\\n with tf.variable_scope(\\\"words\\\"):\\n _word_embeddings = tf.Variable(self.embeddings, name=\\\"_word_embeddings\\\",\\n dtype=tf.float32, trainable=False)\\n word_embeddings = tf.nn.embedding_lookup(_word_embeddings,\\n self.word_ids,\\n name=\\\"word_embeddings\\\")\\n if self.use_window:\\n print(\\\"Concatenating word vectors of context words\\\")\\n word_embeddings_sl = tf.nn.embedding_lookup(_word_embeddings,\\n self.word_ids_sl,\\n name=\\\"word_embeddings_sl\\\")\\n word_embeddings_sr = tf.nn.embedding_lookup(_word_embeddings,\\n self.word_ids_sr,\\n name=\\\"word_embeddings_sr\\\")\\n word_embeddings = tf.concat([word_embeddings_sr, word_embeddings,\\n word_embeddings_sl], axis=-1)\\n if self.use_char_embeddings:\\n print(\\\"adding CNN for char embeddings\\\")\\n with tf.variable_scope(\\\"chars\\\"):\\n _char_embeddings = tf.get_variable(name=\\\"_char_embeddings\\\",\\n dtype=tf.float32,\\n shape=[self.char_count, \\n self.c_dim_input])\\n char_embeddings = tf.nn.embedding_lookup(_char_embeddings, \\n self.char_ids, \\n name=\\\"char_embeddings\\\")\\n s = char_embeddings.shape\\n # the shape of our char_embeddings is now (batch_size, max number of words\\n # in each sentence, max number of chars in each word, self.c_dim )\\n char_filter = tf.get_variable(\\\"char_filter\\\", dtype=tf.float32,\\n shape=[self.c_filter_width, \\n self.c_filter_height,\\n self.c_dim_input,\\n self.c_dim_output])\\n print(\\\"adding 2d convolution layer\\\")\\n char_conv_layer = tf.nn.conv2d(char_embeddings, char_filter, \\n strides=[1, 1, 1, 1], \\n padding=\\\"SAME\\\")\\n char_conv_layer = tf.nn.tanh(char_conv_layer)\\n print(\\\"adding 2d pooling layer\\\")\\n char_conv_layer = tf.layers.max_pooling2d(char_conv_layer, \\n 1, \\n strides=1)\\n char_output = tf.reshape(char_conv_layer, shape=[-1, self.max_len, \\n self.max_word_length*\\n self.c_dim_output])\\n word_embeddings = tf.concat([word_embeddings, char_output], axis=-1)\\n if self.pos and self.concat_pos:\\n print(\\\"concatenating pos with word_embeddings\\\")\\n word_embeddings = tf.concat([word_embeddings, pos_embeddings], axis=-1)\\n self.word_embeddings = word_embeddings\\n if self.use_additional and self.hybrid:\\n print(\\\"using additional embeddings\\\")\\n _word_embeddings_2 = tf.Variable(self.additional_embeddings,\\n name=\\\"two_word_embeddings\\\",\\n dtype=tf.float32, trainable=False)\\n word_embeddings_2 = tf.nn.embedding_lookup(_word_embeddings_2,\\n self.word_ids,\\n name=\\\"two_word_embeddings\\\")\\n self.word_embeddings_2 = word_embeddings_2\",\n \"def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, neu1=None, learn_doctags=True,\\n learn_words=True, learn_hidden=True, word_vectors=None, word_locks=None,\\n doctag_vectors=None, doctag_locks=None):\\n if word_vectors is None:\\n word_vectors = model.wv.syn0\\n if word_locks is None:\\n word_locks = model.syn0_lockf\\n if doctag_vectors is None:\\n doctag_vectors = model.docvecs.doctag_syn0\\n if doctag_locks is None:\\n doctag_locks = model.docvecs.doctag_syn0_lockf\\n\\n word_vocabs = [model.wv.vocab[w] for w in doc_words if w in model.wv.vocab and\\n model.wv.vocab[w].sample_int > model.random.rand() * 2 ** 32]\\n doctag_len = len(doctag_indexes)\\n if doctag_len != model.dm_tag_count:\\n return 0 # skip doc without expected number of doctag(s) (TODO: warn/pad?)\\n\\n null_word = model.wv.vocab['\\\\0']\\n pre_pad_count = model.window\\n post_pad_count = model.window\\n padded_document_indexes = (\\n (pre_pad_count * [null_word.index]) # pre-padding\\n + [word.index for word in word_vocabs if word is not None] # elide out-of-Vocabulary words\\n + (post_pad_count * [null_word.index]) # post-padding\\n )\\n\\n for pos in range(pre_pad_count, len(padded_document_indexes) - post_pad_count):\\n word_context_indexes = (\\n padded_document_indexes[(pos - pre_pad_count): pos] # preceding words\\n + padded_document_indexes[(pos + 1):(pos + 1 + post_pad_count)] # following words\\n )\\n predict_word = model.wv.vocab[model.wv.index2word[padded_document_indexes[pos]]]\\n # numpy advanced-indexing copies; concatenate, flatten to 1d\\n l1 = concatenate((doctag_vectors[doctag_indexes], word_vectors[word_context_indexes])).ravel()\\n neu1e = train_cbow_pair(model, predict_word, None, l1, alpha,\\n learn_hidden=learn_hidden, learn_vectors=False)\\n\\n # filter by locks and shape for addition to source vectors\\n e_locks = concatenate((doctag_locks[doctag_indexes], word_locks[word_context_indexes]))\\n neu1e_r = (neu1e.reshape(-1, model.vector_size)\\n * np_repeat(e_locks, model.vector_size).reshape(-1, model.vector_size))\\n\\n if learn_doctags:\\n np_add.at(doctag_vectors, doctag_indexes, neu1e_r[:doctag_len])\\n if learn_words:\\n np_add.at(word_vectors, word_context_indexes, neu1e_r[doctag_len:])\\n\\n return len(padded_document_indexes) - pre_pad_count - post_pad_count\",\n \"def train(self, examples):\\n print(examples)\\n # first we will do gensim to get word embeddings\\n tokens = []\\n for example in examples:\\n for tuple in example:\\n tokens.append([tuple[0]])\\n self.model = Word2Vec(tokens, min_count=1, size=100).wv\\n # shuffle the examples so that they are gone through 'randomly'\\n #print(examples)\\n random.shuffle(examples)\\n #print(examples)\\n # iterate through our examples\\n for j in range(len(examples)):\\n # the stored label for the previous token\\n prev_label = None\\n prev_word = None\\n # iterate through our tokens for the example\\n for i in range(len(examples[j])):\\n # store our token and its label\\n token = examples[j][i][0]\\n y = examples[j][i][1]\\n # get the features for our current token\\n next_word = None\\n if i <= (len(examples)-1):\\n next_word = examples[j][i+1][0]\\n features = self.featurize(prev_label, prev_word, token, next_word)\\n # set our previous label to our current since\\n # we are done featurizing and need to store it for\\n # the next iteration\\n prev_label = y\\n # a dictionary that will store our z values\\n z = {}\\n # calculate our z value for every state for\\n # the example we are on\\n # z(state) = features * weights\\n # z[state] = np.dot(features, weights[state])\\n for state in self.states:\\n z[state] = np.dot(features, self.weights[state])\\n # store our max\\n max = -1\\n # store our y_hat\\n y_hat = None\\n # store our probabilities\\n prob = {}\\n # this runs softmax on our z's\\n # y_hat = softmax(z)\\n denom = sum(np.exp(np.array(list(z.values()))))\\n for state in self.states:\\n # softmax = p(state) = e^z[state] / (sum[e^z for all z's)\\n # making sure this works the way I want it to, should\\n # be three values\\n #print(np.array(list(z.values())))\\n #print(np.exp(np.array(list(z.values()))))\\n prob[state] = np.exp(z[state]) / denom\\n # if our current prob is greater than the others then it is our boy\\n if prob[state] > max:\\n # save the new prob as the max\\n max = prob[state]\\n # save the state as our prediction y_hat\\n y_hat = state\\n # this will hold our gradients for all the states\\n gradients = {}\\n for state in self.states:\\n # gradient[state] = ((y_hat == state) - prob[state]) * features\\n gradients[state] = ((y_hat == state) - prob[state]) * features\\n # weights[state] -= loss * gradients\\n self.weights[state] -= self.loss * gradients[state]\",\n \"def __init__(self, vocab_size, embedding_size, context_size, hid_dim, out_dim):\\n super(Net, self).__init__()\\n self.E = nn.Embedding(vocab_size, embedding_size) # Embedding matrix\\n self.after_embed_size = embedding_size * context_size\\n self.lin = nn.Linear(self.after_embed_size, hid_dim)\\n self.lin2 = nn.Linear(hid_dim, out_dim)\",\n \"def pretrained_embedding_layer(word_to_vec_map, word_to_index):\\n vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\\n emb_dim = word_to_vec_map[\\\"cucumber\\\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\\n emb_matrix = np.zeros((vocab_len, emb_dim)) # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\\n for word, index in word_to_index.items(): # Set each row \\\"index\\\" of the embedding matrix to be the word vector representation of the \\\"index\\\"th word of the vocabulary\\n emb_matrix[index, :] = word_to_vec_map[word]\\n embedding_layer = Embedding(vocab_len, emb_dim, trainable = False) # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \\n embedding_layer.build((None,)) # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \\\"None\\\".\\n embedding_layer.set_weights([emb_matrix]) # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\\n return embedding_layer\",\n \"def sample_beam(model, input_embedding, char2idx, idx2char, k=5, maxlen=30,\\n start='START', use_head=True):\\n with torch.no_grad():\\n device = input_embedding.device\\n softmax = nn.Softmax(dim=1)\\n if use_head:\\n input_embedding = input_embedding.view(1, -1)\\n\\n inp = [torch.LongTensor([char2idx[start]]).to(device)]\\n inp = nn.utils.rnn.pack_sequence(inp)\\n out, hidden = model(input_embedding, inp, use_head=use_head)\\n\\n out = softmax(out.data).view(-1).cpu().numpy()\\n max_k = np.argsort(out)[-k:][::-1]\\n oldprobs = out[max_k]\\n words = [[i] for i in max_k]\\n inp = pack([torch.LongTensor([j]).to(device) for j in max_k])\\n\\n if model.mode == 'LSTM':\\n hidden0 = torch.cat([hidden[0] for i in range(k)], dim=1)\\n hidden1 = torch.cat([hidden[1] for i in range(k)], dim=1)\\n hidden = hidden0, hidden1\\n else:\\n hidden = torch.cat([hidden for i in range(k)], dim=1)\\n WORDS = []\\n for c in range(maxlen):\\n out, hidden = model(hidden, inp, use_head=False)\\n out = softmax(out.data).cpu().numpy()\\n\\n #print(out.shape)\\n inpnp = inp.data.detach().cpu().numpy()\\n done = np.where(inpnp == char2idx['END'])\\n out[done] = 0\\n if len(out[done]) != 0:\\n #print(out[done].shape)\\n for d in done[0]:\\n out[d][char2idx['END']] = 1\\n #print(done)\\n\\n #print(out)\\n #print(out[done])\\n out = (oldprobs.reshape(-1, 1)*out)\\n max_k = np.argsort(out)[:, -k:][:, ::-1]\\n\\n #print(max_k)\\n probs = np.array([out[i][max_k[i]] for i in range(k)])\\n #print(probs)\\n flat = probs.reshape(-1)\\n max_k2 = np.argsort(flat)[::-1][:k]\\n word_inds = max_k2//k\\n next_chars_inds = max_k2%k\\n\\n oldprobs = flat[max_k2]\\n #print(oldprobs)\\n\\n new_words = []\\n new_inp = []\\n for i, word_ind in enumerate(word_inds):\\n next_char = max_k[word_ind][next_chars_inds[i]]\\n if next_char == char2idx['END']:\\n #print(\\\"HIT AN END at word {}\\\".format(word_ind))\\n WORDS.append((words[word_ind], oldprobs[i]))\\n #the_word = words[word_ind]\\n #return ''.join([idx2char[i] for i in the_word])\\n new_inp.append(torch.LongTensor([next_char]).to(device))\\n word = words[word_ind][:]\\n word = word + [next_char]\\n new_words.append(word)\\n words = new_words[:]\\n\\n if model.mode == 'LSTM':\\n h1, h2 = hidden\\n h1, h2 = h1[0][word_inds].view(1, k, -1), h2[0][word_inds].view(1, k, -1)\\n hidden = h1, h2\\n else:\\n hidden = hidden[0][word_inds].view(1, k, -1)\\n\\n\\n inp = pack(new_inp)\\n\\n return [''.join([idx2char[i] for i in word if i != char2idx['END']]) for word in words], oldprobs\",\n \"def generate_conll2003_embeddings():\\n glove_embedding = get_glove_embedding()\\n\\n word2index = {}\\n idx2word = {}\\n embed_array = []\\n\\n word2index[\\\"<pad>\\\"] = 1\\n embed_array.append(init_embedding())\\n\\n word2index[\\\"<unk>\\\"] = 0\\n embed_array.append(init_embedding())\\n\\n data = []\\n with open(TRAIN_DATA_PATH, \\\"r\\\") as f:\\n for line in f:\\n data.append(json.loads(line))\\n\\n idx = 2\\n\\n for sample in tqdm(data, total=len(data)):\\n words = sample[\\\"tokens\\\"]\\n\\n for w in words:\\n w = w.lower()\\n\\n # if word is not present in dictionary, add to dictionary and append embedding vector\\n if w not in word2index.keys():\\n word2index[w] = idx\\n idx += 1\\n if w not in glove_embedding.keys():\\n ev = init_embedding()\\n else:\\n ev = glove_embedding[w]\\n\\n embed_array.append(ev)\\n\\n else:\\n continue\\n\\n # save embeddings\\n embed_array = np.vstack(embed_array)\\n np.save(EMBD_OUTPUT_PATH, embed_array)\\n\\n # save dictionary\\n print(\\\"Dicitionary Size: \\\", len(word2index))\\n with open(DICTIONARY_OUTPUT_PATH, \\\"w\\\") as f:\\n json.dump(word2index, f)\",\n \"def _embed(self):\\n with tf.variable_scope('word_embedding'):\\n self.pretrained_word_mat = tf.get_variable(\\\"word_emb_mat\\\",\\n [self.vocab.word_size() - 2, self.vocab.word_embed_dim],\\n dtype=tf.float32,\\n initializer=tf.constant_initializer(\\n self.vocab.word_embeddings[2:],\\n dtype=tf.float32),\\n trainable=False)\\n self.word_pad_unk_mat = tf.get_variable(\\\"word_unk_pad\\\",\\n [2, self.pretrained_word_mat.get_shape()[1]],\\n dtype=tf.float32,\\n initializer=tf.constant_initializer(\\n self.vocab.word_embeddings[:2],\\n dtype=tf.float32),\\n trainable=True)\\n\\n self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)\\n self.p_emb = tf.nn.embedding_lookup(self.word_mat, self.p)\\n self.q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)\",\n \"def make_idx2word():\\n idx2word = {}\\n d = train_data.shared['word2idx']\\n for word, idx in d.items():\\n print(word)\\n idx2word[idx] = word\\n if config.use_glove_for_unk:\\n d2 = train_data.shared['new_word2idx']\\n for word, idx in d2.items():\\n print(word)\\n idx2word[idx+len(d)] = word\\n return idx2word\",\n \"def build_doc_sense_vec(self):\\n\\t\\twith codecs.open(self.vocab_file, encoding='utf-8', mode='r') as infile:\\n\\t\\t\\tline = infile.readline()\\n\\t\\t\\ti = 0\\n\\t\\t\\twhile line:\\n\\t\\t\\t\\tword = line.split()[0]\\n\\t\\t\\t\\tif not self.word2IdVocabulary.has_key(word):\\n\\t\\t\\t\\t\\t# print i, word\\n\\t\\t\\t\\t\\t# else:\\n\\t\\t\\t\\t\\tself.word2IdVocabulary[word] = i\\n\\t\\t\\t\\tif not self.id2WordVocabulary.has_key(i):\\n\\t\\t\\t\\t\\tself.id2WordVocabulary[i] = word\\n\\t\\t\\t\\tline = infile.readline()\\n\\t\\t\\t\\ti += 1\\n\\t\\t\\tself.vocab_num = len(self.word2IdVocabulary)\\n\\t\\t\\tprint \\\"vocabulary number:\\\" + str(self.vocab_num)\\n\\n\\t\\twith codecs.open(self.vec_file, encoding='utf-8', mode='r') as vecfile:\\n\\t\\t\\twith codecs.open(self.vec_out_file, encoding='utf-8', mode='a+') as vec_outfile:\\n\\n\\t\\t\\t\\tfor i, line in enumerate(vecfile):\\n\\t\\t\\t\\t\\tif i % 10000 == 0:\\n\\t\\t\\t\\t\\t\\tprint i\\n\\t\\t\\t\\t\\t# if i > 72:\\n\\t\\t\\t\\t\\t# \\tbreak\\n\\t\\t\\t\\t\\tif i == 0:\\n\\t\\t\\t\\t\\t\\ta, b, c = map(int, line.split()[:3])\\n\\t\\t\\t\\t\\t\\tprint('Number of sememes: {}\\\\n'\\n\\t\\t\\t\\t\\t\\t\\t 'Number of words: {}\\\\n'\\n\\t\\t\\t\\t\\t\\t\\t 'Dimension of vectors: {}'.format(a, b, c))\\n\\t\\t\\t\\t\\telif i > 462667:\\n\\t\\t\\t\\t\\t\\tsline = line.strip('\\\\n').split()\\n\\t\\t\\t\\t\\t\\tword = sline[0]\\n\\t\\t\\t\\t\\t\\tvector_list = []\\n\\t\\t\\t\\t\\t\\tvector_list.append(sline[1:])\\n\\t\\t\\t\\t\\t\\tvector_array = np.array(vector_list)\\n\\t\\t\\t\\t\\t\\tword_id = self.word2IdVocabulary[word]\\n\\t\\t\\t\\t\\t\\tif not self.vectors.has_key(word_id):\\n\\t\\t\\t\\t\\t\\t\\tself.vectors[word_id] = vector_array\\n\\t\\t\\t\\t\\t\\t# vector_mean = np.mean(vector_array, axis=0)\\n\\t\\t\\t\\t\\t\\tif not self.vector_mean.has_key(word_id):\\n\\t\\t\\t\\t\\t\\t\\tself.vector_mean[word_id] = vector_array\\n\\t\\t\\t\\t\\t\\t# vec_outfile.write(line)\\n\\t\\t\\t\\t\\telif i > 462887:\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tsline = line.strip('\\\\n').split()\\n\\t\\t\\t\\t\\t\\tword = sline[0]\\n\\t\\t\\t\\t\\t\\tsense_num = int(sline[1])\\n\\t\\t\\t\\t\\t\\tvectors = sline[2:sense_num*c+2] # (sense_num*c+2)\\n\\t\\t\\t\\t\\t\\tvector_list = []\\n\\t\\t\\t\\t\\t\\tfor start in range(0, len(vectors), c):\\n\\t\\t\\t\\t\\t\\t\\tvector_list.append(list(map(float, vectors[start: start+c])))\\n\\t\\t\\t\\t\\t\\tvector_array = np.array(vector_list)\\n\\t\\t\\t\\t\\t\\tword_id = self.word2IdVocabulary[word]\\n\\t\\t\\t\\t\\t\\tif not self.vectors.has_key(word_id):\\n\\t\\t\\t\\t\\t\\t\\tself.vectors[word_id] = vector_array\\n\\t\\t\\t\\t\\t\\tvector_mean = np.mean(vector_array, axis=0)\\n\\t\\t\\t\\t\\t\\tif not self.vector_mean.has_key(word_id):\\n\\t\\t\\t\\t\\t\\t\\tself.vector_mean[word_id] = vector_mean\\n\\t\\t\\t\\t\\t\\t'''j = 0\\n\\t\\t\\t\\t\\t\\tfor each_sense_vec in vector_array:\\n\\t\\t\\t\\t\\t\\t\\tif len(vector_array) > 1:\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_line = word + '_' + str(j) + ' ' + np.array2string(each_sense_vec, max_line_width=2000,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tformatter={'float_kind': lambda x: '%6f' % x})[1:-1] + '\\\\n'\\n\\t\\t\\t\\t\\t\\t\\t\\tj += 1\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_line = word + ' ' + np.array2string(each_sense_vec, max_line_width=2000,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t formatter={'float_kind': lambda\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t x: '%6f' % x})[1:-1] + '\\\\n'\\n\\n\\t\\t\\t\\t\\t\\t\\tvec_outfile.write(new_line)'''\\n\\n\\t\\twith codecs.open(self.doc_file, encoding='utf-8', mode='r') as docfile:\\n\\t\\t\\twith codecs.open(self.doc_out_file, encoding='utf-8', mode='a+') as doc_outfile:\\n\\t\\t\\t\\twith codecs.open(self.vec_out_file_bydoc, encoding='utf-8', mode='a+') as vec_outfile_bydoc:\\n\\t\\t\\t\\t\\tprint \\\"Processing document file......\\\"\\n\\t\\t\\t\\t\\tline = docfile.readline().strip('\\\\n')\\n\\t\\t\\t\\t\\twhile line:\\n\\t\\t\\t\\t\\t\\twords = line.split()\\n\\t\\t\\t\\t\\t\\tnew_words = [x for x in words]\\n\\t\\t\\t\\t\\t\\tfor i in range(len(words)):\\n\\t\\t\\t\\t\\t\\t\\tword_id = self.word2IdVocabulary[words[i]]\\n\\t\\t\\t\\t\\t\\t\\tsense_vecs = self.vectors[word_id]\\n\\t\\t\\t\\t\\t\\t\\tsense_num = len(sense_vecs)\\n\\t\\t\\t\\t\\t\\t\\tif sense_num > 1:\\n\\t\\t\\t\\t\\t\\t\\t\\tcontext_words = []\\n\\t\\t\\t\\t\\t\\t\\t\\tfor x in range(i-int(self.context_num), i+int(self.context_num)+1):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif x != i and 0 <= x < len(words):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcontext_words.append(words[x])\\n\\t\\t\\t\\t\\t\\t\\t\\tsense_index = self.select_attention(context_words, sense_vecs)\\n\\t\\t\\t\\t\\t\\t\\t\\tword_vec_i = sense_vecs[sense_index]\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_wordi = words[i] + '_' + str(sense_index)\\n\\t\\t\\t\\t\\t\\t\\t\\tself.vector_word_doc[new_wordi.encode('utf-8')] = word_vec_i\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_words[i] = new_wordi\\n\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tword_vec_i = sense_vecs[0]\\n\\t\\t\\t\\t\\t\\t\\t\\tself.vector_word_doc[words[i].encode('utf-8')] = word_vec_i\\n\\t\\t\\t\\t\\t\\t\\tvec_outfile_bydoc.write(new_words[i] + ' ' + np.array2string(word_vec_i, max_line_width=2000,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t formatter={'float_kind': lambda x: '%6f' % x})[1:-1] + '\\\\n')\\n\\n\\t\\t\\t\\t\\t\\tdoc_outfile.write(' '.join(new_words) + '\\\\n')\\n\\n\\t\\t\\t\\t\\t\\tline = docfile.readline()\\n\\n\\t\\treturn self.vector_word_doc\",\n \"def visualise_embedding(embedding_dir, perplexity, learning_rate, image_path_name, label_mapping):\\n tsne = TSNE(\\n n_components=2,\\n random_state=0,\\n perplexity=perplexity,\\n learning_rate=learning_rate,\\n n_iter=20000\\n )\\n\\n with open(embedding_dir, 'r') as emb_file:\\n embedding_list = emb_file.readlines()\\n\\n print('Number of subword units: {}'.format(len(embedding_list)))\\n\\n embedding_dict = {}\\n vector_emb = []\\n subword_labels = []\\n # TODO: Make this a clean solution\\n # Start at 2 to skip the random </s> character which is coming through (may need to be 3 for Georgian)\\n for embedding in embedding_list[2:]:\\n segmented_embedding = embedding.split()\\n subword_labels.append(label_mapping[segmented_embedding[0]])\\n embedding_vector = [float(dim) for dim in segmented_embedding[1:]]\\n vector_emb.append(embedding_vector)\\n embedding_dict[segmented_embedding[0]] = embedding_vector\\n\\n emb_2d = tsne.fit_transform(vector_emb)\\n\\n datapoint_indices = range(len(emb_2d))\\n fig, ax = plt.subplots()\\n for i, subword_label in zip(datapoint_indices, subword_labels):\\n ax.scatter(emb_2d[i, 0], emb_2d[i, 1], c='c', label=subword_label)\\n\\n\\n for i, subword_label in enumerate(subword_labels):\\n ax.annotate(subword_label, (emb_2d[i, 0], emb_2d[i, 1]))\\n\\n plt.savefig(image_path_name)\\n return embedding_dict\",\n \"def load_word2vec(emb_path, id_to_word, word_dim, old_weights):\\n new_weights = old_weights\\n print('Loading pretrained embeddings from {}...'.format(emb_path))\\n pre_trained = {}\\n emb_invalid = 0\\n for i, line in enumerate(codecs.open(emb_path, 'r', 'utf-8')):\\n line = line.rstrip().split()\\n if len(line) == word_dim + 1:\\n pre_trained[line[0]] = np.array(\\n [float(x) for x in line[1:]]\\n ).astype(np.float32)\\n else:\\n emb_invalid += 1\\n if emb_invalid > 0:\\n print('WARNING: %i invalid lines' % emb_invalid)\\n c_found = 0\\n c_lower = 0\\n c_zeros = 0\\n n_words = len(id_to_word)\\n # Lookup table initialization\\n for i in range(n_words):\\n word = id_to_word[i]\\n if word in pre_trained:\\n new_weights[i] = pre_trained[word]\\n c_found += 1\\n elif word.lower() in pre_trained:\\n new_weights[i] = pre_trained[word.lower()]\\n c_lower += 1\\n elif re.sub('\\\\d', '0', word.lower()) in pre_trained:\\n new_weights[i] = pre_trained[\\n re.sub('\\\\d', '0', word.lower())\\n ]\\n c_zeros += 1\\n print('Loaded %i pretrained embeddings.' % len(pre_trained))\\n print('%i / %i (%.4f%%) words have been initialized with '\\n 'pretrained embeddings.' % (\\n c_found + c_lower + c_zeros, n_words,\\n 100. * (c_found + c_lower + c_zeros) / n_words)\\n )\\n print('%i found directly, %i after lowercasing, '\\n '%i after lowercasing + zero.' % (\\n c_found, c_lower, c_zeros\\n ))\\n return new_weights\",\n \"def __init__(self, vocab, embed_size=512, dropout_rate=0.1, max_len=200):\\n super(DecoderEmbeddings, self).__init__()\\n pad_token_idx = 0 #vocab.tokenizer.ids_to_tokens[0]\\n assert vocab.tokenizer.ids_to_tokens[0] == '[PAD]'\\n self.embeddings = nn.Embedding(len(vocab.tokenizer.ids_to_tokens), embed_size, padding_idx=pad_token_idx)\\n self.positional_encoding = PositionalEncoding(d_model=embed_size, dropout=dropout_rate, max_len=max_len)\",\n \"def process_input_sentence(sess, char_dict, model_settings, model_vars, sentence):\\n chars = list(sentence.strip())\\n cids = char_dict.chars2cids(chars)\\n \\n in_embedding = np.eye(model_settings['input_classes'])\\n one_hot = in_embedding[cids]\\n one_hot_by_t = np.expand_dims(one_hot, 1)\\n\\n probs = process_sentence(sess, model_settings, model_vars, one_hot_by_t)\\n labels = viterbi(probs)\\n words = char_dict.chars2words(chars, labels)\\n print('|'.join(words))\",\n \"def _documents2vectors(\\n self,\\n dm: int = 1,\\n vector_size: int = 2 ** 7,\\n window: int = 2 ** 3,\\n alpha: float = 2 ** -6,\\n min_alpha: float = 2 ** -11,\\n min_count: int = 3,\\n workers: int = 2 ** 2,\\n epochs: int = 2 ** 7,\\n dm_mean: int = 0,\\n dm_concat: int = 0,\\n dm_tag_count: int = 2 ** 5,\\n dbow_words: int = 1,\\n ):\\n\\n file_name = join(PARENT_BASE_DIR, '.files/model.model')\\n if isfile(file_name):\\n model = gensim_document_to_vector.load(file_name)\\n else:\\n data = self.data\\n data = data[0]\\n tags = self.tags\\n tags = tags[0]\\n corpus_keywords_list = []\\n\\n for doc in data:\\n corpus_keywords_list.append(self.kywrds.by_frequency(doc))\\n _data = []\\n for doc in data:\\n _doc = []\\n for _sent in doc:\\n for wrd in _sent:\\n _doc.append(wrd)\\n _data.append(_doc)\\n data = _data\\n\\n documents = []\\n for i in range(len(data)):\\n keywords_list = corpus_keywords_list[i]\\n if self.exist_tags:\\n for tag in tags[i]:\\n keywords_list.append(tag)\\n documents.append(TaggedDocument(data[i], keywords_list))\\n\\n model = gensim_document_to_vector(\\n documents=documents,\\n dm=dm,\\n vector_size=vector_size,\\n window=window,\\n alpha=alpha,\\n min_alpha=min_alpha,\\n min_count=min_count,\\n workers=workers,\\n epochs=epochs,\\n dm_mean=dm_mean,\\n dm_concat=dm_concat,\\n dm_tag_count=dm_tag_count,\\n dbow_words=dbow_words,\\n )\\n model.save(file_name)\\n return model\",\n \"def __glove_embed__(sequence, model):\\n embedded = []\\n for word in sequence:\\n embedded.append(model[word])\\n return embedded\",\n \"def model(input_shape, model,model2,model3, word_to_index):\\n \\n ### START CODE HERE ###\\n # Define sentence_indices as the input of the graph.\\n # It should be of shape input_shape and dtype 'int32' (as it contains indices, which are integers).\\n sentence_indices = Input(input_shape,dtype='int32')\\n \\n # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\\n embedding_layer, ignored_words = pretrained_embedding_layer(model,model2,model3,word_to_index,300)\\n \\n # Propagate sentence_indices through your embedding layer\\n # (See additional hints in the instructions).\\n embeddings = embedding_layer(sentence_indices)\\n \\n # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\\n # The returned output should be a batch of sequences.\\n X = LSTM(units=128,input_shape=input_shape,return_sequences=True)(embeddings)\\n # Add dropout with a probability of 0.5\\n X = Dropout(rate=0.5)(X)\\n # Propagate X trough another LSTM layer with 128-dimensional hidden state\\n # The returned output should be a single hidden state, not a batch of sequences.\\n X = LSTM(units=128,input_shape=input_shape,return_sequences=False)(X)\\n # Add dropout with a probability of 0.5\\n X = Dropout(rate=0.5)(X)\\n # Propagate X through a Dense layer with 5 units\\n X = Dense(units=num_classes)(X)\\n# X = Dense(6, activation='softmax')\\n # Add a softmax activation\\n# print(X)\\n# print(type(X))\\n# print(X.shape)\\n# print(sum(X))\\n X = Activation('softmax')(X)\\n \\n # Create Model instance which converts sentence_indices into X.\\n model = Model(inputs=sentence_indices,outputs=X)\\n \\n return model\",\n \"def input_dim(use_word_emb, embedding_dim, word_emb_enc_dim):\\n return embedding_dim + use_word_emb * word_emb_enc_dim\",\n \"def sentences_2_idxs(self):\\n fo_pos = open(self.config.parsed_train_file_pos, 'w')\\n fo_neg = open(self.config.parsed_train_file_neg, 'w')\\n self.load_dicts()\\n labels = pd.read_csv(self.config.train_file, usecols=[\\\"target\\\"])\\n\\n labels = list(labels.values[:, 0])\\n questions = pd.read_csv(self.config.train_file,\\n usecols=[\\\"question_text\\\"], index_col=False)\\n unk_idx = self.word2idx.get(self.config.unknown_token)\\n\\n for label, quest in zip(labels, questions.question_text):\\n tokens = preprocess_text(quest)\\n\\n if self.config.include_unknown:\\n idxs = [self.word2idx.get(token, unk_idx) for token in\\n tokens]\\n else:\\n idxs = [self.word2idx.get(token) for token in tokens]\\n idxs = [idx for idx in idxs if idx]\\n out_line = (str(\\\" \\\".join(str(num) for num in idxs)) + \\\"\\\\n\\\")\\n if label == 1:\\n fo_pos.write(out_line)\\n else:\\n fo_neg.write(out_line)\",\n \"def parse_input(input_data, dictionary, model):\\n vec_text = TextBlob(input_data).words.lower().lemmatize()\\n vec_bow = dictionary.doc2bow(vec_text)\\n return model[vec_bow]\",\n \"def forward(self, input):\\n \\n ## A4 code\\n # print(\\\"vocab_len\\\",len(self.vocab.char2id))\\n # print(\\\"input_size\\\",input.size()) \\n \\n char_embeddings = self.embedding(input)\\n max_sent_lenth,batch_size,max_word_lenth,char_e_size = char_embeddings.size() \\n char_embeddings=char_embeddings.view(max_sent_lenth*batch_size,max_word_lenth,char_e_size)\\n conv_out = self.CNN(char_embeddings.permute(0,2,1))\\n highway_out = self.Highway(conv_out)\\n word_emb = self.dropout(highway_out)\\n output = word_emb.contiguous().view(max_sent_lenth, batch_size, self.embed_size)\\n return output\\n ## End A4 code\\n\\n ### YOUR CODE HERE for part 1j\\n\\n\\n ### END YOUR CODE\",\n \"def build_seq_embeddings(self):\\n with tf.variable_scope(\\\"seq_embedding\\\"), tf.device(\\\"/cpu:0\\\"):\\n embedding_map = tf.get_variable(\\n name=\\\"map\\\",\\n shape=[self.config.vocab_size, self.config.word_embedding_size],\\n initializer=self.initializer)\\n \\n # We need to store the normalized lookup table for efficient mapping of embedding vectors to closest words\\n self.normed_embedding_map = tf.nn.l2_normalize(embedding_map, dim=1)\\n \\n seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs) \\n # seq_embeddings has the shape (batch_size, sequence_length, sentence_length, embedding_size)\\n # meaning, for each index in input_seqs (with shape (batch_size, sequence_length, sentence_length)) it stores an embedding vector\\n\\n #print('Shape seq_embeddings: ' + str(seq_embeddings.get_shape()))\\n\\n self.seq_embeddings = seq_embeddings\",\n \"def _add_pre_trained_embedding(self):\\n\\n if self.embedding_type['type'] == 'glove':\\n self.logging.info('use pre-trained glove word2vec')\\n # a. load pre trained glove\\n GLOVE_DIR = '../data/glove_pretrained/glove.6B'\\n glove_suffix_name = 'glove.6B.' + str(self.embedding_size) + 'd.txt'\\n import os\\n import numpy as np\\n\\n embeddings_index = {}\\n f = open(os.path.join(GLOVE_DIR, glove_suffix_name)) # 'glove.6B.100d.txt'))\\n for line in f:\\n values = line.split()\\n word = values[0]\\n coefs = np.asarray(values[1:], dtype='float32')\\n embeddings_index[word] = coefs\\n f.close()\\n\\n self.logging.info('')\\n self.logging.info('Found %s word vectors.' % len(embeddings_index))\\n\\n # b. compute embedding matrix\\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\\n cnt = 0\\n for word, i in self.word_index.items():\\n embedding_vector = embeddings_index.get(word)\\n if embedding_vector is not None:\\n embedding_matrix[i] = embedding_vector # words not found in embedding index will be all-zeros.\\n else:\\n # self.logging.info('token in train missing in word2vec: ' + str(word))\\n cnt += 1\\n self.logging.info('total tokens missing: ' + str(cnt) + ' / ' + str(len(self.word_index)))\\n\\n # c. build embedding layer\\n from keras.layers import Embedding\\n embedding_layer = Embedding(len(self.word_index) + 1,\\n self.embedding_size,\\n weights=[embedding_matrix],\\n input_length=self.maxlen,\\n trainable=False)\\n\\n elif self.embedding_type['type'] == 'gensim':\\n self.logging.info('use pre-trained gensim word2vec')\\n\\n import gzip\\n import gensim\\n from keras.layers import Embedding\\n import numpy as np\\n\\n # fname = '../data/word2vec_pretrained/motors/d_300_k_712904_w_6_e_60_v_motors'\\n # fname = '../data/word2vec_pretrained/fashion/d_300_k_1341062_w_6_e_70_v_fashion'\\n\\n self.logging.info('load word2vec path: ' + str(self.embedding_type['path']))\\n model = gensim.models.Word2Vec.load(self.embedding_type['path'])\\n pretrained_weights = model.wv.syn0\\n vocab_size, vector_dim = pretrained_weights.shape\\n\\n method = 3\\n if method == 1:\\n self.logging.info('word2vec attempt to fit into embedding layer - middle complex')\\n # convert the wv word vectors into a numpy matrix that is suitable for insertion\\n # into our TensorFlow and Keras models\\n\\n embedding_matrix = np.zeros((len(model.wv.vocab), vector_dim))\\n for i in range(len(model.wv.vocab)):\\n embedding_vector = model.wv[model.wv.index2word[i]]\\n if embedding_vector is not None:\\n embedding_matrix[i] = embedding_vector\\n\\n embedding_layer = Embedding(input_dim=embedding_matrix.shape[0],\\n output_dim=embedding_matrix.shape[1],\\n # input_length=self.maxlen,\\n weights=[embedding_matrix],\\n trainable=False)\\n elif method == 2:\\n self.logging.info('word2vec simple embedding matching - simple complex')\\n embedding_layer = Embedding(input_dim=vocab_size,\\n output_dim=vector_dim,\\n input_length=self.maxlen,\\n weights=[pretrained_weights],\\n trainable=False)\\n elif method == 3:\\n\\n self.logging.info('word2vec match using word_index from keras tokenizer - as used in glove match above')\\n # b. compute embedding matrix\\n\\n # sd = 1 / np.sqrt(len(self.word_index) + 1)\\n # embedding_matrix = np.random.normal(0, scale=sd, size=(len(self.word_index) + 1, self.embedding_size))\\n\\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\\n cnt = 0\\n for word, i in self.word_index.items():\\n if word in model.wv:\\n embedding_vector = model.wv[word]\\n embedding_matrix[i] = embedding_vector\\n else:\\n # self.logging.info('token in train missing in word2vec: ' + str(word))\\n cnt += 1\\n self.logging.info('total tokens missing: ' + str(cnt))\\n\\n\\n # c. build embedding layer\\n from keras.layers import Embedding\\n embedding_layer = Embedding(len(self.word_index) + 1,\\n self.embedding_size,\\n weights=[embedding_matrix],\\n input_length=self.maxlen,\\n trainable=False)\\n else:\\n raise ValueError('unknown method value')\\n\\n else:\\n raise ValueError('unknown embedding type')\\n self.logging.info('create glove pre-trained embedding: ' + str(self.embedding_size))\\n return embedding_layer\",\n \"def load_embeddings(emb_file, oovs=[], pads=[], sep=' ', lower=False, case_dim=True):\\n word2emb = {}\\n word2idx = {}\\n\\n # read and store all word vectors\\n for line in open(emb_file, errors = 'ignore', encoding = 'utf-8'):\\n try:\\n fields = line.strip().split(sep)\\n word = fields[0]\\n vec = np.asarray(fields[1:], dtype='float32')\\n if case_dim:\\n is_upper = float(word[0].isupper())\\n vec = np.insert(vec, 0, is_upper, axis=0)\\n if lower:\\n word = word.lower()\\n word2emb[word] = vec\\n if word not in word2idx:\\n word2idx[word] = len(word2idx)\\n except Exception as e:\\n print('[WARNING] Exception in `load_embeddings`:', e)\\n\\n # get dimensions from the last vector added\\n emb_dim = word2emb[word].shape[0]\\n\\n # add custom embeddings for special characters\\n mu = 0\\n sigma = 0.01\\n\\n for word in pads:\\n if word not in word2idx:\\n if case_dim:\\n vec = npr.normal(mu, sigma, emb_dim-1)\\n vec = np.insert(vec, 0, 0., axis=0)\\n else:\\n vec = npr.normal(mu, sigma, emb_dim)\\n word2emb[word] = vec\\n word2idx[word] = len(word2idx)\\n else:\\n print('[WARNING] Padding item ' + word + ' has an embedding vector')\\n\\n for word in oovs:\\n if word not in word2idx:\\n if case_dim:\\n vec = npr.normal(mu, sigma, emb_dim-1)\\n is_upper = float(word[0].isupper())\\n vec = np.insert(vec, 0, is_upper, axis=0)\\n else:\\n vec = npr.normal(mu, sigma, emb_dim)\\n word2emb[word] = vec\\n word2idx[word] = len(word2idx)\\n else:\\n print('[WARNING] OOV alias ' + word + ' has an embedding vector')\\n\\n # create an embedding matrix\\n vocab_size = len(word2emb)\\n emb_matrix = np.zeros((vocab_size, emb_dim))\\n for word, idx in word2idx.items():\\n if word in word2emb:\\n vec = word2emb[word]\\n if vec is not None and vec.shape[0] == emb_dim:\\n emb_matrix[idx] = np.asarray(vec)\\n\\n # print feedback data and return mappings\\n print('[INFO] Embedding vocabulary:', emb_matrix.shape[0], '(lowercase: ' + str(lower) + ')')\\n print('[INFO] OOV aliases:', oovs)\\n print('[INFO] Padding items:', pads)\\n print('[INFO] Embedding dimensions:', emb_dim, '(extra case dimension: ' + str(case_dim) + ')')\\n return word2idx, np.asarray(emb_matrix), emb_dim\",\n \"def get_embed(input_data, vocab_size, embed_dim):\\n # todo 需要编程：\\n # 1、构建嵌入矩阵的查找表\\n lookup_w = tf.Variable(\\n initial_value=tf.random_uniform([vocab_size, embed_dim], -1.0, 1.0)\\n )\\n # 2、获得嵌入输出\\n embed = tf.nn.embedding_lookup(params=lookup_w, ids=input_data)\\n # [N, n_steps, embed_size]\\n return embed\",\n \"def get_dis_xy(self, volatile):\\n # select random word IDs\\n bs = 32\\n mf = 50\\n #mf = 75000 #only choose top mf number of most frequent words\\n assert mf <= min(len(self.src_dic), len(self.tgt_dic))\\n src_ids = torch.LongTensor(bs).random_(mf)\\n tgt_ids = torch.LongTensor(bs).random_(mf)\\n\\n #use cuda..\\n useGPU = True\\n if useGPU:\\n src_ids = src_ids.cuda()\\n tgt_ids = tgt_ids.cuda()\\n\\n # get word embeddings\\n src_emb = self.src_emb(Variable(src_ids, volatile=True))\\n tgt_emb = self.tgt_emb(Variable(tgt_ids, volatile=True))\\n src_emb = self.mapping(Variable(src_emb.data, volatile=volatile))\\n tgt_emb = Variable(tgt_emb.data, volatile=volatile)\\n\\n # input / target\\n x = torch.cat([src_emb, tgt_emb], 0)\\n y = torch.FloatTensor(2 * bs).zero_()\\n dis_smooth = 0.1 #discriminator smooth predication\\n y[:bs] = 1 - dis_smooth\\n y[bs:] = dis_smooth\\n y = Variable(y.cuda() if useGPU else y)\\n\\n return x, y\",\n \"def build_sense_embedding(target_sense_to_id, word_freq, EMBEDDING_DIM):\\r\\n res = {}\\r\\n wordvecs = load_glove(EMBEDDING_DIM)\\r\\n \\r\\n for target_sense_list in target_sense_to_id:\\r\\n for key, _ in target_sense_list.items():\\r\\n sense_vector = np.zeros(EMBEDDING_DIM)\\r\\n senses = key.split(',')\\r\\n n = 0\\r\\n for sensekey in senses:\\r\\n #print(sensekey) \\r\\n if '/' in sensekey:\\r\\n continue\\r\\n sense_synset = sc2ss(sensekey)\\r\\n if sense_synset:\\r\\n sense_vector += build_sense_vector(sense_synset, word_freq, wordvecs)\\r\\n n += 1\\r\\n if n != 0:\\r\\n res[key] = sense_vector/n\\r\\n return res\",\n \"def glove_embedding(self, word_index, padded, embedding_matrix, part='q'):\\n print(\\\"*\\\" * 50, \\\"Start Glove embedding process\\\", \\\"*\\\" * 50)\\n start_time = time()\\n\\n\\n MAX_SEQ_LEN = None\\n if part == 'q':\\n MAX_SEQ_LEN = self.MAX_Q_SEQ_LEN\\n elif part == 'a':\\n MAX_SEQ_LEN = self.MAX_A_SEQ_LEN\\n else:\\n print(f\\\"Please indicate you want embedding question part or answer part\\\")\\n\\n\\n input_layer = Input(shape=(MAX_SEQ_LEN,), dtype='int32')\\n embedding_layer = Embedding(input_dim = len(word_index) + 1,\\n output_dim = self.EMBEDDING_DIM,\\n weights=[embedding_matrix],\\n input_length=MAX_SEQ_LEN,\\n trainable=False)(input_layer)\\n # (number of sample, MAX_SEQ_LEN, EMBEDING_DIM)\\n model = Model(inputs=input_layer, outputs=embedding_layer)\\n model.compile('rmsprop', 'mse')\\n output_array = model.predict(padded)\\n\\n cost_time = round((time() - start_time), 4)\\n print(\\\"*\\\" * 40, \\\"End Glove embedding() with {} seconds\\\".format(cost_time), \\\"*\\\" * 40, end='\\\\n\\\\n')\\n\\n return output_array, embedding_layer\",\n \"def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=None,\\n learn_doctags=True, learn_words=True, learn_hidden=True,\\n word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None):\\n if word_vectors is None:\\n word_vectors = model.wv.syn0\\n if word_locks is None:\\n word_locks = model.syn0_lockf\\n if doctag_vectors is None:\\n doctag_vectors = model.docvecs.doctag_syn0\\n if doctag_locks is None:\\n doctag_locks = model.docvecs.doctag_syn0_lockf\\n\\n word_vocabs = [model.wv.vocab[w] for w in doc_words if w in model.wv.vocab and\\n model.wv.vocab[w].sample_int > model.random.rand() * 2 ** 32]\\n\\n for pos, word in enumerate(word_vocabs):\\n reduced_window = model.random.randint(model.window) # `b` in the original doc2vec code\\n start = max(0, pos - model.window + reduced_window)\\n window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start)\\n word2_indexes = [word2.index for pos2, word2 in window_pos if pos2 != pos]\\n l1 = np_sum(word_vectors[word2_indexes], axis=0) + np_sum(doctag_vectors[doctag_indexes], axis=0)\\n count = len(word2_indexes) + len(doctag_indexes)\\n if model.cbow_mean and count > 1:\\n l1 /= count\\n neu1e = train_cbow_pair(model, word, word2_indexes, l1, alpha,\\n learn_vectors=False, learn_hidden=learn_hidden)\\n if not model.cbow_mean and count > 1:\\n neu1e /= count\\n if learn_doctags:\\n for i in doctag_indexes:\\n doctag_vectors[i] += neu1e * doctag_locks[i]\\n if learn_words:\\n for i in word2_indexes:\\n word_vectors[i] += neu1e * word_locks[i]\\n\\n return len(word_vocabs)\",\n \"def train_sentence_dbow(model, sentence, lbls, alpha, work=None, train_words=True, train_lbls=True):\\n neg_labels = []\\n if model.negative:\\n # precompute negative labels\\n neg_labels = zeros(model.negative + 1)\\n neg_labels[0] = 1.0\\n\\n for label in lbls:\\n if label is None:\\n continue # OOV word in the input sentence => skip\\n for word in sentence:\\n if word is None:\\n continue # OOV word in the input sentence => skip\\n train_sg_pair(model, word, label, alpha, neg_labels, train_words, train_lbls)\\n\\n return len([word for word in sentence if word is not None])\",\n \"def get_model_bicond_sepembed(batch_size, max_seq_length, input_size, hidden_size, target_size,\\n vocab_size, pretrain, tanhOrSoftmax, dropout):\\n\\n # batch_size x max_seq_length\\n inputs = tf.placeholder(tf.int32, [batch_size, max_seq_length])\\n inputs_cond = tf.placeholder(tf.int32, [batch_size, max_seq_length])\\n\\n cont_train = True\\n if pretrain == \\\"pre\\\": # continue training embeddings or not. Currently works better to continue training them.\\n cont_train = False\\n embedding_matrix = tf.Variable(tf.random_uniform([vocab_size, input_size], -0.1, 0.1), # input_size is embeddings size\\n name=\\\"embedding_matrix\\\", trainable=cont_train)\\n embedding_matrix_cond = tf.Variable(tf.random_uniform([vocab_size, input_size], -0.1, 0.1),\\n name=\\\"embedding_matrix\\\", trainable=cont_train)\\n\\n\\n # batch_size x max_seq_length x input_size\\n embedded_inputs = tf.nn.embedding_lookup(embedding_matrix, inputs)\\n embedded_inputs_cond = tf.nn.embedding_lookup(embedding_matrix_cond, inputs_cond)\\n\\n\\n # [batch_size x inputs_size] with max_seq_length elements\\n # fixme: possibly inefficient\\n # inputs_list[0]: batch_size x input[0] <-- word vector of the first word\\n inputs_list = [tf.squeeze(x) for x in\\n tf.split(1, max_seq_length, embedded_inputs)]\\n inputs_cond_list = [tf.squeeze(x) for x in\\n tf.split(1, max_seq_length, embedded_inputs_cond)]\\n\\n drop_prob = None\\n if dropout:\\n drop_prob = 0.1\\n lstm_encoder = Encoder(rnn_cell.BasicLSTMCell, input_size, hidden_size, drop_prob, drop_prob)\\n\\n start_state = tf.zeros([batch_size, lstm_encoder.state_size])\\n\\n ### FORWARD\\n\\n # [h_i], [h_i, c_i] <-- LSTM\\n # [h_i], [h_i] <-- RNN\\n fw_outputs, fw_states = lstm_encoder(inputs_list, start_state, \\\"LSTM\\\")\\n\\n # running a second LSTM conditioned on the last state of the first\\n fw_outputs_cond, fw_states_cond = lstm_encoder(inputs_cond_list, fw_states[-1],\\n \\\"LSTMcond\\\")\\n\\n fw_outputs_fin = fw_outputs_cond[-1]\\n\\n ### BACKWARD\\n bw_outputs, bw_states = lstm_encoder(inputs_list[::-1], start_state, \\\"LSTM_bw\\\")\\n bw_outputs_cond, bw_states_cond = lstm_encoder(inputs_cond_list[::-1], bw_states[-1],\\n \\\"LSTMcond_bw\\\")\\n bw_outputs_fin = bw_outputs_cond[-1]\\n\\n outputs_fin = tf.concat(1, [fw_outputs_fin, bw_outputs_fin])\\n\\n\\n if tanhOrSoftmax == \\\"tanh\\\":\\n model = Projector(target_size, non_linearity=tf.nn.tanh, bias=True)(outputs_fin) # tf.nn.softmax\\n else:\\n model = Projector(target_size, non_linearity=tf.nn.softmax, bias=True)(outputs_fin) # tf.nn.softmax\\n\\n return model, [inputs, inputs_cond]\",\n \"def make_embedding(path, words, indices):\\n #root = '/'.join(path.split('/')[0:-1])\\n #all_paths = [root+'/'+x for x in os.listdir(root)] #'/'.join(path.split('/')[0:-1]))\\n #for path in all_paths:\\n vec_path = 'data/'+path.split('/')[-1]+'_'+mode\\n print(vec_path)\\n if os.path.exists(vec_path+'.npy'):\\n np_vecs = np.load(vec_path+'.npy')\\n else:\\n words_len = len(words)\\n vecs = []\\n if mode == 'word':\\n f = load_model('wiki.en.bin')\\n for i, w in enumerate(words):\\n if mode == 'word':\\n vec = f.get_word_vector(w)\\n else:\\n vec = eye[indices[w]]\\n vecs.append(vec) \\n if i % 10000 == 0:\\n print(\\\"{} / {}\\\".format(i, words_len))\\n np_vecs = np.asarray(vecs, dtype=np.int8)\\n np.save(vec_path, np_vecs)\\n return np_vecs\",\n \"def model_extract_document_embedding(self):\\n input_ids = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\\\"ids\\\")\\n attention_mask = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\\\"att\\\")\\n token = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\\\"tok\\\")\\n\\n # Embedding :\\n if self.method_embedding == 'CamemBERT':\\n Camembert_model = transformers.TFCamembertModel.from_pretrained(\\\"jplu/tf-camembert-base\\\")\\n x = Camembert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'FlauBERT':\\n # lr = 0.00001\\n Flaubert_model = transformers.TFFlaubertModel.from_pretrained(\\\"jplu/tf-flaubert-base-uncased\\\")\\n x = Flaubert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'XLM-RoBERTa':\\n # lr = 0.00001\\n XLMRoBERTa_model = transformers.TFXLMRobertaModel.from_pretrained(\\\"jplu/tf-xlm-roberta-base\\\")\\n x = XLMRoBERTa_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'RoBERTa':\\n # Experience Test path weights :\\n PATH = '/kaggle/input/tf-roberta/'\\n config = transformers.RobertaConfig.from_pretrained(PATH + 'config-roberta-base.json')\\n Roberta_model = transformers.TFRobertaModel.from_pretrained(PATH + 'pretrained-roberta-base.h5',\\n config=config)\\n # Sinon :\\n # Roberta_model = transformers.TFRobertaModel.from_pretrained('roberta-base')\\n x = Roberta_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'BERT':\\n BERT_model = transformers.TFBertModel.from_pretrained('bert-base-uncased')\\n x = BERT_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n else:\\n logger.critical(\\\"unknown embedding method name : '{}'\\\".format(self.method_embedding))\\n\\n # word vectors shape : (None, maxlen, 768)\\n x = x[0]\\n cls_token = x[:, 0, :]\\n\\n model = tf.keras.models.Model(inputs=[input_ids, attention_mask, token], outputs=cls_token)\\n return model\",\n \"def get_vector(word, model):\\n return model.wv[word]\",\n \"def get_embeddings(self, in_data):\\n context, da = in_data\\n if self.fixed_divide:\\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=True)\\n else:\\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=False)\\n\\n # Shubhangi: what this step essentially does is it replaces the context words by their token, with UNK as default.\\n # again , we don't need this since our context data is essentially vectors therefore commenting this out\\n # similary we don't need context embedding , that's exactly what context is already .\\n\\n # context_emb = []\\n context_emb = [float(parameter[0]) for parameter in context]\\n\\n # for tok in context[-max_context_len:]:\\n # context_emb.append(self.dict_token.get(tok, self.UNK_TOKEN))\\n\\n # Shubhangi: padding is needed because each context sentence could be of different length ,\\n # we don't need to include context in padding as we're going to have a fixed size\\n # (max_context_len - len(context)) = 0\\n\\n\\n # padding = [self.UNK_TOKEN] * (max_context_len - len(context))\\n\\n # Shubhangi: padding might be harmless for now therefore not removing ,\\n # essentially what this is doing is concatenating the arrays and sending\\n if self.use_div_token:\\n return context_emb + [self.DIV_TOKEN] + da_emb\\n # return padding + context_emb + [self.DIV_TOKEN] + da_emb\\n # return padding + context_emb + da_emb\\n return context_emb + da_emb\",\n \"def id2vec_preprocess(args):\\n log = logging.getLogger(\\\"preproc\\\")\\n log.info(\\\"Loading docfreq model from %s\\\", args.docfreq_in)\\n df_model = DocumentFrequencies(log_level=args.log_level).load(source=args.docfreq_in)\\n coocc_model = Cooccurrences().load(args.input)\\n if numpy.any(coocc_model.matrix.data < 0):\\n raise ValueError((\\\"Co-occurrence matrix %s contains negative elements. \\\"\\n \\\"Please check its correctness.\\\") % args.input)\\n if numpy.any(numpy.isnan(coocc_model.matrix.data)):\\n raise ValueError((\\\"Co-occurrence matrix %s contains nan elements. \\\"\\n \\\"Please check its correctness.\\\") % args.input)\\n\\n try:\\n df_meta = coocc_model.get_dep(DocumentFrequencies.NAME)\\n if df_model.meta != df_meta:\\n raise ValueError((\\n \\\"Document frequency model you provided does not match dependency inside \\\"\\n \\\"Cooccurrences model:\\\\nargs.docfreq.meta:\\\\n%s\\\\ncoocc_model.get_dep\\\"\\n \\\"(\\\\\\\"docfreq\\\\\\\")\\\\n%s\\\\n\\\") % (df_model.meta, df_meta))\\n except KeyError:\\n pass # There is no docfreq dependency\\n\\n vs = args.vocabulary_size\\n if len(df_model) < vs:\\n vs = len(df_model)\\n sz = args.shard_size\\n if vs < sz:\\n raise ValueError(\\n \\\"vocabulary_size=%s is less than shard_size=%s. You should specify a smaller \\\"\\n \\\"shard_size (e.g. shard_size=%s).\\\" % (vs, sz, vs))\\n vs -= vs % sz\\n log.info(\\\"Effective vocabulary size: %d\\\", vs)\\n df_model = df_model.greatest(vs)\\n log.info(\\\"Sorting the vocabulary...\\\")\\n chosen_words = sorted(df_model.tokens())\\n word_indices = {w: i for i, w in enumerate(chosen_words)}\\n\\n if not os.path.exists(args.output):\\n os.makedirs(args.output)\\n with open(os.path.join(args.output, \\\"row_vocab.txt\\\"), \\\"w\\\") as out:\\n out.write(\\\"\\\\n\\\".join(chosen_words))\\n log.info(\\\"Saved row_vocab.txt\\\")\\n shutil.copyfile(os.path.join(args.output, \\\"row_vocab.txt\\\"),\\n os.path.join(args.output, \\\"col_vocab.txt\\\"))\\n log.info(\\\"Saved col_vocab.txt\\\")\\n del chosen_words\\n\\n ccmatrix = extract_coocc_matrix((vs, vs), word_indices, coocc_model)\\n\\n log.info(\\\"Planning the sharding...\\\")\\n bool_sums = ccmatrix.indptr[1:] - ccmatrix.indptr[:-1]\\n reorder = numpy.argsort(-bool_sums)\\n with open(os.path.join(args.output, \\\"row_sums.txt\\\"), \\\"w\\\") as out:\\n out.write(\\\"\\\\n\\\".join(map(str, bool_sums.tolist())))\\n log.info(\\\"Saved row_sums.txt\\\")\\n shutil.copyfile(os.path.join(args.output, \\\"row_sums.txt\\\"),\\n os.path.join(args.output, \\\"col_sums.txt\\\"))\\n log.info(\\\"Saved col_sums.txt\\\")\\n\\n log.info(\\\"Writing the shards...\\\")\\n os.makedirs(args.output, exist_ok=True)\\n nshards = vs // args.shard_size\\n for row in progress_bar(range(nshards), log, expected_size=nshards):\\n for col in range(nshards):\\n indices_row = reorder[row::nshards]\\n indices_col = reorder[col::nshards]\\n shard = ccmatrix[indices_row][:, indices_col].tocoo()\\n\\n example = tf.train.Example(features=tf.train.Features(feature={\\n \\\"global_row\\\": _int64s(indices_row),\\n \\\"global_col\\\": _int64s(indices_col),\\n \\\"sparse_local_row\\\": _int64s(shard.row),\\n \\\"sparse_local_col\\\": _int64s(shard.col),\\n \\\"sparse_value\\\": _floats(shard.data)}))\\n\\n with open(os.path.join(args.output,\\n \\\"shard-%03d-%03d.pb\\\" % (row, col)),\\n \\\"wb\\\") as out:\\n out.write(example.SerializeToString())\\n log.info(\\\"Success\\\")\",\n \"def embed(self, loader, model):\\n print(\\\" ** Embedding words\\\")\\n\\n words = loader.words\\n vectors = [model.get_word_vector(word) for word in words]\\n\\n return [(w, *v) for w, v in zip(words, vectors)]\",\n \"def data_word2vec_one_label(input_file,\\n word2vec_model):\\n vocab = dict([(k, v.index) for (k, v) in word2vec_model.wv.vocab.items()])\\n\\n def _token_to_index(content):\\n result = []\\n for item in content:\\n word2id = vocab.get(item)\\n if word2id is None:\\n word2id = 0\\n result.append(word2id)\\n return result\\n\\n\\n with open(input_file) as fin:\\n raw_tokens_list_gov = []\\n raw_tokens_list_art = []\\n test_id_list = []\\n content_index_list_gov = []\\n content_index_list_art = []\\n # labels_list = []\\n onehot_labels_list = []\\n labels_num_list = []\\n total_line = 0\\n\\n for each_line in fin:\\n data = json.loads(each_line)\\n ds_art = data['testid']\\n ds = ds_art.split(\\\"_\\\")[0]\\n art = ds_art.split(\\\"_\\\")[1][1:-1]\\n test_id = ds + \\\"_\\\" + art\\n features_content_gov = data['gov']\\n features_content_art = data['art']\\n label = data['label']\\n\\n test_id_list.append(test_id)\\n content_index_list_gov.append(_token_to_index(\\n features_content_gov))\\n content_index_list_art.append(_token_to_index(\\n features_content_art))\\n\\n raw_tokens_list_gov.append(features_content_gov)\\n raw_tokens_list_art.append(features_content_art)\\n\\n # labels_list.append(label)\\n # onehot_labels_list.append(_create_onehot_labels(labels_index,\\n # num_labels))\\n onehot_labels_list.append(label)\\n labels_num = 1\\n labels_num_list.append(labels_num)\\n total_line += 1\\n\\n\\n class _Data:\\n def __init__(self):\\n pass\\n\\n @property\\n def number(self):\\n return total_line\\n\\n @property\\n def testid(self):\\n return test_id_list\\n\\n @property\\n def raw_tokens_gov(self):\\n return raw_tokens_list_gov\\n\\n @property\\n def raw_tokens_art(self):\\n return raw_tokens_list_art\\n\\n @property\\n def tokenindex_gov(self):\\n return content_index_list_gov\\n\\n @property\\n def tokenindex_art(self):\\n return content_index_list_art\\n\\n # @property\\n # def labels(self):\\n # return labels_list\\n\\n @property\\n def onehot_labels(self):\\n return onehot_labels_list\\n\\n @property\\n def labels_num(self):\\n return labels_num_list\\n\\n return _Data()\",\n \"def __init__(self, directory, dataset, B_SIZE = 32):\\n \\n torch.backends.cudnn.deterministic = True\\n torch.backends.cudnn.benchmark = False\\n\\n \\n self.directory = directory\\n self.dataset = dataset\\n self.batch_size = B_SIZE\\n self.hidden_dim = 64\\n self.embedding_dim = 300\\n \\n all_data = pickle.load(open(directory + dataset + \\\"/data.p\\\", \\\"rb\\\"))\\n \\n self.w2ix = all_data.w2ix\\n self.vocab_size = len(self.w2ix) \\n \\n self.mask_list = []\\n self.mask_tokens = [\\\"<PAD>\\\", \\\"<SOS>\\\", \\\"<EOS>\\\", \\\".\\\"]\\n \\n for item in self.mask_tokens:\\n \\n if item in self.w2ix:\\n \\n self.mask_list.append(self.w2ix[item])\\n \\n self.pretrained_embeds = all_data.pretrained_embeds\\n \\n \\n # In[4]:\\n \\n \\n x_train, y_train = zip(*all_data.train)\\n x_dev, y_dev = zip(*all_data.dev)\\n x_test, y_test = zip(*all_data.test)\\n \\n print(\\\"\\\\nVocab size:\\\", len(self.w2ix),\\n \\\"\\\\nTraining size:\\\", len(y_train),\\n \\\"\\\\nDev size:\\\", len(y_dev),\\n \\\"\\\\nTest size:\\\", len(y_test))\\n \\n # In[5]:\\n \\n self.output_size= len(np.unique(y_train))\\n \\n print(\\\"\\\\nOutput dimension: \\\", self.output_size, \\\"\\\\n\\\")\\n \\n \\n self.sequence_length = all_data.sequence_length()\\n \\n if dataset == \\\"mimicanemia\\\":\\n \\n \\tself.sequence_length = 2200\\n \\n print(\\\"--Sequence length :\\\", self.sequence_length, \\\"\\\\n\\\")\\n \\n # In[10]:\\n \\n from modules.utils import padder\\n \\n x_train_pad, train_lengths = padder(x_train, pad_len = self.sequence_length)\\n x_dev_pad, dev_lengths = padder(x_dev, pad_len = self.sequence_length)\\n x_test_pad, test_lengths = padder(x_test, pad_len = self.sequence_length)\\n \\n \\n # In[11]:\\n \\n x_train_pad = torch.LongTensor(x_train_pad)#.to(device)\\n x_dev_pad = torch.LongTensor(x_dev_pad)#.to(device)\\n x_test_pad = torch.LongTensor(x_test_pad)#.to(device)\\n train_lengths = torch.LongTensor(train_lengths)#.to(device)\\n dev_lengths = torch.LongTensor(dev_lengths)#.to(device)\\n test_lengths = torch.LongTensor(test_lengths)#.to(device)\\n y_train = torch.LongTensor(y_train)#.to(device)\\n y_dev = torch.LongTensor(y_dev)#.to(device)\\n y_test = torch.LongTensor(y_test)#.to(device)\\n \\n \\n # In[12]:\\n \\n \\n training_prebatch = list(zip(x_train_pad, train_lengths, y_train))\\n dev_prebatch = list(zip(x_dev_pad, dev_lengths, y_dev))\\n testing_prebatch = list(zip(x_test_pad, test_lengths, y_test))\\n \\n \\n training_prebatch = sorted(training_prebatch, key = lambda x : x[1], reverse = False)\\n dev_prebatch = sorted(dev_prebatch, key = lambda x : x[1], reverse = False)\\n testing_prebatch = sorted(testing_prebatch, key = lambda x : x[1], reverse = False)\\n \\n # In[13]:\\n \\n ### removing sos and eos only sentences\\n \\n train_prebatch = [x for x in training_prebatch if x[1] > 2]\\n dev_prebatch = [x for x in dev_prebatch if x[1] > 2]\\n test_prebatch = [x for x in testing_prebatch if x[1] > 2]\\n \\n \\n self.training = DataLoader(train_prebatch, batch_size = self.batch_size, \\n shuffle = True, pin_memory = False)\\n \\n self.development = DataLoader(dev_prebatch, batch_size = self.batch_size, \\n shuffle = False, pin_memory = False)\\n \\n \\n self.testing = DataLoader(test_prebatch, batch_size = self.batch_size, \\n shuffle = False, pin_memory = False)\",\n \"def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size, vocab_file):\\n print(\\\"Generating word embedding...\\\")\\n # load word embeddings\\n embedding_dict = {}\\n with open(emb_file, \\\"r\\\", encoding=\\\"utf-8\\\") as fh:\\n for line in tqdm(fh, total=emb_size):\\n array = line.split()\\n word = \\\"\\\".join(array[0:-vec_size])\\n vector = list(map(float, array[-vec_size:]))\\n embedding_dict[word] = vector\\n\\n TRANSLATE = {\\n \\\"-lsb-\\\": \\\"[\\\", \\\"-rsb-\\\": \\\"]\\\", \\\"-lrb-\\\": \\\"(\\\", \\\"-rrb-\\\": \\\")\\\", \\\"-lcb-\\\": \\\"{\\\",\\n \\\"-rcb-\\\": \\\"}\\\", \\\"-LSB-\\\": \\\"[\\\", \\\"-RSB-\\\": \\\"]\\\", \\\"-LRB-\\\": \\\"(\\\", \\\"-RRB-\\\": \\\")\\\",\\n \\\"-LCB-\\\": \\\"{\\\", \\\"-RCB-\\\": \\\"}\\\"\\n }\\n SPECIAL_TOKENS = [\\\"<NULL>\\\", \\\"<UNK>\\\", \\\"<S>\\\", \\\"</S>\\\"]\\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))\\n words = SPECIAL_TOKENS + words\\n if vocab_size > 0:\\n words = words[:vocab_size]\\n with open(vocab_file, 'w') as f:\\n f.write('\\\\n'.join(words[1:]))\\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\\n word2idx_dict = {}\\n unknown_count = 0\\n for i, word in enumerate(words):\\n word2idx_dict[word] = i\\n if word in TRANSLATE:\\n word = TRANSLATE[word]\\n done = False\\n for w in (word, word.lower(), word.upper(), word.capitalize()):\\n if w in embedding_dict:\\n embedding[i] = embedding_dict[w]\\n done = True\\n break\\n if not done:\\n unknown_count += 1\\n return embedding, word2idx_dict, unknown_count\",\n \"def e_step(self, wordids, wordcts):\\n # Declare theta (topic mixtures) of minibatch and list of non-zero indexes\\n batch_size = len(wordids)\\n theta = np.zeros((batch_size, self.num_topics))\\n index = [{} for d in range(batch_size)]\\n # Inference\\n for d in range(batch_size):\\n (thetad, indexd) = self.infer_doc(wordids[d], wordcts[d])\\n theta[d, :] = thetad\\n index[d] = indexd\\n return (theta, index)\",\n \"def load_embedding(self, glove_dir='glove.6B/'):\\n\\n f = open(os.path.join(glove_dir, 'glove.6B.100d.txt'))\\n for line in f:\\n values = line.split()\\n word = values[0]\\n self.embeddings_index[word] = np.asarray(values[1:], dtype='float32')\\n f.close()\",\n \"def __init__(self, docs, dict_path= 'wordindex.npy'):\\n super(NNModel, self).__init__()\\n self.stopwords += self.additional_stopwords\\n self.words = set(['OOB', 'UNK']) # OOB for out of boundary, UNK for unknown words\\n self.docs = []\\n\\n for doc in docs:\\n datum = []\\n for word in self.cut_words(doc):\\n self.words.add(word)\\n datum.append(word)\\n self.docs.append(datum)\\n\\n self.words = list(self.words)\\n self.word2idx = dict([(self.words[i], i) for i in range(len(self.words))])\\n logging.info(f'{len(docs)} articles loaded, with word bag length: {len(self.words)}')\\n if dict_path != '': # save dict\\n np.save(DATA_DIR + dict_path, self.word2idx)\",\n \"def build_model(options,worddicts):\\n opt_ret=dict()\\n params=dict()\\n word_xr1_mask=tf.reverse(word_x1_mask,[1])\\n word_xr2_mask = tf.reverse(word_x2_mask, [1])\\n\\n\\n\\n #embedding layer\\n word_embedding = norm_weight(options['n_words'], options['dim_word'])\\n if options['embedding']:\\n with open(options['embedding'], 'r',encoding='iso-8859-1') as f:\\n for line in f:\\n temp=line.split()\\n word=temp[0]\\n vector=temp[1:]\\n if word in worddicts and worddicts[word]<options['n_words']:\\n word_embedding[worddicts[word],:]=vector\\n\\n word_embedding_layer=tf.Variable(word_embedding,name='word_embedding')\\n\\n emb1=tf.nn.embedding_lookup(word_embedding_layer,word_x1,name='embedding_word_lookup1')\\n emb2=tf.nn.embedding_lookup(word_embedding_layer,word_x2,name='embedding_word_lookup2')\\n\\n if options['use_dropout']:\\n emb1=tf.cond(use_noise,lambda :tf.nn.dropout(emb1,0.5),lambda :emb1)\\n emb2 = tf.cond(use_noise, lambda: tf.nn.dropout(emb2, 0.5), lambda: emb2)\\n\\n #1-layer LSTM\\n print('LSTM result')\\n for l in range(1):\\n #param_init_lstm\\n prefix = 'encoder_{}'.format(str(l + 1))\\n if l==0:\\n nin=options['dim_word']\\n else:\\n nin = options['dim_word']+2*options['dim']\\n dim=options['dim']\\n\\n W = numpy.concatenate([norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim)], axis=1)\\n params[_p(prefix, 'W')] = tf.Variable(W)\\n\\n # for the previous hidden activation\\n U = numpy.concatenate([ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim)], axis=1)\\n params[_p(prefix, 'U')] = tf.Variable(U)\\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\\n\\n #param_init_rlstm\\n prefix = 'encoder_r_{}'.format(str(l + 1))\\n if l==0:\\n nin=options['dim_word']\\n else:\\n nin = options['dim_word'] +2*options['dim']\\n dim=options['dim']\\n\\n W = numpy.concatenate([norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim)], axis=1)\\n params[_p(prefix, 'W')] = tf.Variable(W)\\n\\n # for the previous hidden activation\\n U = numpy.concatenate([ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim)], axis=1)\\n params[_p(prefix, 'U')] = tf.Variable(U)\\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\\n\\n\\n\\n if l==0:\\n ctx1=emb1\\n ctx2=emb2\\n else:\\n ctx1=tf.concat([ctx1,emb1],axis=2)\\n ctx2=tf.concat([ctx2,emb2],axis=2)\\n\\n print(ctx1)\\n\\n ctxr1=tf.reverse(ctx1,axis=[1])\\n ctxr2=tf.reverse(ctx2,axis=[1])\\n\\n proj1=RNN_layer(ctx1,word_x1_mask,options,params,prefix='encoder_{}'.format(str(l+1)))\\n projr1=RNN_layer(ctxr1,word_xr1_mask,options,params,prefix='encoder_r_{}'.format(str(l+1)))\\n proj2=RNN_layer(ctx2,word_x2_mask,options,params,prefix='encoder_{}'.format(str(l+1)))\\n projr2=RNN_layer(ctxr2,word_xr2_mask,options,params,prefix='encoder_r_{}'.format(str(l+1)))\\n\\n ctx1=tf.concat([proj1[0],projr1[0][::-1]],axis=len(projr1[0].shape)-1)\\n ctx2 = tf.concat([proj2[0], projr2[0][::-1]], axis=len(projr2[0].shape) - 1)\\n ctx1 = tf.transpose(ctx1, [1, 0, 2])\\n ctx2 = tf.transpose(ctx2, [1, 0, 2])\\n print(ctx1)\\n\\n ctx1=ctx1*word_x1_mask[:,:,None]\\n ctx2 = ctx2 * word_x2_mask[:, :, None]\\n def _step(h,x):\\n return tf.matmul(x[0],x[1])\\n temp=tf.zeros((tf.shape(ctx1)[1],tf.shape(ctx2)[1]))\\n weight_martrix=tf.scan(_step,[ctx1,tf.transpose(ctx2,[0,2,1])],temp)\\n weight_martrix_1=tf.exp(weight_martrix)*word_x2_mask[:,None,:]\\n weight_martrix_2=tf.transpose(tf.exp(weight_martrix)*word_x1_mask[:,:,None],[0,2,1])\\n weight_martrix_1=weight_martrix_1/tf.reduce_sum(weight_martrix_1,axis=2)[:,:,None]\\n weight_martrix_2 = weight_martrix_2 / tf.reduce_sum(weight_martrix_2, axis=2)[:,:,None]\\n\\n ctx1_=tf.reduce_sum(weight_martrix_1[:,:,:,None]*ctx2[:,None,:,:],axis=2)\\n ctx2_ = tf.reduce_sum(weight_martrix_2[:, :, :, None] * ctx1[:, None, :, :],axis=2)\\n inp1=tf.concat([ctx1, ctx1_, ctx1*ctx1_, ctx1-ctx1_],axis=2)\\n inp2 = tf.concat([ctx2, ctx2_, ctx2 * ctx2_, ctx2 - ctx2_], axis=2)\\n params = param_init_fflayer(options, params, prefix='projection',\\n nin=options['dim'] * 8, nout=options['dim'], ortho=False)\\n\\n\\n s=tf.shape(inp1)\\n inp1 = tf.nn.relu(tf.matmul(tf.reshape(inp1,[-1,int(inp1.shape[-1])]), params[_p('projection', 'W')]) + params[_p('projection', 'b')])\\n inp1=tf.reshape(inp1,tf.concat([s[:2],[-1]],0))\\n s=tf.shape(inp2)\\n inp2 = tf.nn.relu(tf.matmul(tf.reshape(inp2,[-1,int(inp2.shape[-1])]), params[_p('projection', 'W')]) + params[_p('projection', 'b')])\\n inp2=tf.reshape(inp2,tf.concat([s[:2],[-1]],0))\\n if options['use_dropout']:\\n inp1=tf.cond(use_noise,lambda :tf.nn.dropout(inp1,0.5),lambda :inp1)\\n inp2 = tf.cond(use_noise, lambda: tf.nn.dropout(inp2, 0.5), lambda: inp2)\\n\\n\\n for l in range(1):\\n #param_init_lstm\\n prefix = 'decoder_{}'.format(str(l + 1))\\n if l==0:\\n nin=options['dim']\\n else:\\n nin = options['dim']+2*options['dim']\\n dim=options['dim']\\n\\n W = numpy.concatenate([norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim)], axis=1)\\n params[_p(prefix, 'W')] = tf.Variable(W)\\n\\n # for the previous hidden activation\\n U = numpy.concatenate([ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim)], axis=1)\\n params[_p(prefix, 'U')] = tf.Variable(U)\\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\\n\\n #param_init_rlstm\\n prefix = 'decoder_r_{}'.format(str(l + 1))\\n if l==0:\\n nin=options['dim']\\n else:\\n nin = options['dim'] +2*options['dim']\\n dim=options['dim']\\n\\n W = numpy.concatenate([norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim),\\n norm_weight(nin, dim)], axis=1)\\n params[_p(prefix, 'W')] = tf.Variable(W)\\n\\n # for the previous hidden activation\\n U = numpy.concatenate([ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim),\\n ortho_weight(dim)], axis=1)\\n params[_p(prefix, 'U')] = tf.Variable(U)\\n params[_p(prefix, 'b')] = tf.Variable(numpy.zeros((4 * dim,)).astype('float32'))\\n\\n\\n\\n if l==0:\\n ctx1=inp1\\n ctx2=inp2\\n else:\\n ctx1=tf.concat([ctx1,inp1],axis=2)\\n ctx2=tf.concat([ctx2,inp2],axis=2)\\n\\n print(ctx1)\\n\\n ctxr1=tf.reverse(ctx1,axis=[1])\\n ctxr2=tf.reverse(ctx2,axis=[1])\\n\\n proj1=RNN_layer(ctx1,word_x1_mask,options,params,prefix='decoder_{}'.format(str(l+1)))\\n projr1=RNN_layer(ctxr1,word_xr1_mask,options,params,prefix='decoder_r_{}'.format(str(l+1)))\\n proj2=RNN_layer(ctx2,word_x2_mask,options,params,prefix='decoder_{}'.format(str(l+1)))\\n projr2=RNN_layer(ctxr2,word_xr2_mask,options,params,prefix='decoder_r_{}'.format(str(l+1)))\\n\\n ctx1=tf.concat([proj1[0],projr1[0][::-1]],axis=len(projr1[0].shape)-1)\\n ctx2 = tf.concat([proj2[0], projr2[0][::-1]], axis=len(projr2[0].shape) - 1)\\n ctx1 = tf.transpose(ctx1, [1, 0, 2])\\n ctx2 = tf.transpose(ctx2, [1, 0, 2])\\n print(ctx1)\\n\\n mean_1=tf.reduce_sum(ctx1*word_x1_mask[:,:,None],axis=1)/tf.reduce_sum(word_x1_mask,axis=1)[:,None]\\n max_1=tf.reduce_max(ctx1*word_x1_mask[:,:,None],axis=1)\\n\\n mean_2=tf.reduce_sum(ctx2*word_x2_mask[:,:,None],axis=1)/tf.reduce_sum(word_x2_mask,axis=1)[:,None]\\n max_2=tf.reduce_max(ctx2*word_x2_mask[:,:,None],axis=1)\\n\\n #represention and MLP layer\\n logit=tf.concat([mean_1,mean_2,max_1,max_2],axis=1)\\n if options['use_dropout']:\\n logit=tf.cond(use_noise,lambda :tf.nn.dropout(logit,0.5),lambda :logit)\\n\\n\\n params = param_init_fflayer(options, params, prefix='ff_layer_1',\\n nin=options['dim'] * 8, nout=options['dim'], ortho=False)\\n params = param_init_fflayer(options, params, prefix='ff_layer_output',\\n nin=options['dim'], nout=3, ortho=False)\\n logit=tf.nn.tanh(tf.matmul(logit,params[_p('ff_layer_1','W')])+params[_p('ff_layer_1','b')])\\n if options['use_dropout']:\\n logit=tf.cond(use_noise,lambda :tf.nn.dropout(logit,0.5),lambda :logit)\\n\\n logit=tf.matmul(logit, params[_p('ff_layer_output', 'W')]) + params[_p('ff_layer_output', 'b')]\\n probs=tf.nn.softmax(logit)\\n pred=tf.argmax(probs,1)\\n cost=tf.losses.sparse_softmax_cross_entropy(y,logit)\\n return opt_ret,cost,pred,probs\",\n \"def forward(self, idx, targets=None):\\n B, T = idx.shape\\n tok_emb = self.token_embedding_table(idx) # (B,T,C)\\n pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\\n x = tok_emb + pos_emb # (B,T,C)\\n x = self.blocks(x)\\n logits = self.lm_head(x) # (B,T,vocab_size) \\n\\n if targets is None:\\n loss = None\\n else:\\n B, T, C = logits.shape\\n logits = logits.view(B*T, C)\\n targets = targets.view(B*T)\\n loss = F.cross_entropy(logits, targets)\\n return logits, loss\",\n \"def add_word_embeddings_op(self):\\n with tf.variable_scope(\\\"words\\\"):\\n if self.config.embeddings is None:\\n self.logger.info(\\\"WARNING: randomly initializing word vectors\\\")\\n _word_embeddings = tf.get_variable(\\n name=\\\"_word_embeddings\\\",\\n dtype=tf.float32,\\n shape=[self.config.nwords, self.config.dim_word])\\n else:\\n _word_embeddings = tf.Variable(\\n self.config.embeddings,\\n name=\\\"_word_embeddings\\\",\\n dtype=tf.float32,\\n trainable=self.config.train_embeddings)\\n\\n word_embeddings = tf.nn.embedding_lookup(_word_embeddings,\\n self.word_ids, name=\\\"word_embeddings\\\")\\n\\n with tf.variable_scope(\\\"chars\\\"):\\n if self.config.use_chars:\\n # get char embeddings matrix\\n _char_embeddings = tf.get_variable(\\n name=\\\"_char_embeddings\\\",\\n dtype=tf.float32,\\n shape=[self.config.nchars, self.config.dim_char])\\n char_embeddings = tf.nn.embedding_lookup(_char_embeddings,\\n self.char_ids, name=\\\"char_embeddings\\\")\\n\\n # put the time dimension on axis=1\\n s = tf.shape(char_embeddings)\\n char_embeddings = tf.reshape(char_embeddings,\\n shape=[s[0]*s[1], s[-2], self.config.dim_char])\\n word_lengths = tf.reshape(self.word_lengths, shape=[s[0]*s[1]])\\n\\n # bi lstm on chars\\n cell_fw = tf.contrib.rnn.LSTMCell(self.config.hidden_size_char,\\n state_is_tuple=True)\\n cell_bw = tf.contrib.rnn.LSTMCell(self.config.hidden_size_char,\\n state_is_tuple=True)\\n _output = tf.nn.bidirectional_dynamic_rnn(\\n cell_fw, cell_bw, char_embeddings,\\n sequence_length=word_lengths, dtype=tf.float32)\\n\\n # read and concat output\\n _, ((_, output_fw), (_, output_bw)) = _output\\n output = tf.concat([output_fw, output_bw], axis=-1)\\n\\n # shape = (batch size, max sentence length, char hidden size)\\n output = tf.reshape(output,\\n shape=[s[0], s[1], 2*self.config.hidden_size_char])\\n word_embeddings = tf.concat([word_embeddings, output], axis=-1)\\n\\n self.word_embeddings = tf.nn.dropout(word_embeddings, self.dropout)\",\n \"def get_embed(input_data, vocab_size, embed_dim):\\n embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))\\n embed = tf.nn.embedding_lookup(embedding, input_data)\\n\\n return embed\",\n \"def load_pretrain_embedding(vocab, embed_size, embedding_path):\\n model = KeyedVectors.load_word2vec_format(embedding_path)\\n\\n print('{} {}'.format(vocab.size(), embed_size))\\n for token, id in vocab.token2id.items():\\n if token in model:\\n print('{} {}'.format(token, ' '.join(map(str, model[token]))))\\n else:\\n emb = np.random.random((embed_size,)) - 0.5\\n print('{} {}'.format(token, ' '.join(map(str, emb))))\",\n \"def predict_sentences_2_idxs(self):\\n fo = open(self.config.parsed_predict_file, 'w')\\n self.load_dicts()\\n\\n questions = pd.read_csv(self.config.predict_file,\\n usecols=[\\\"question_text\\\"], index_col=False)\\n unk_idx = self.word2idx[self.config.unknown_token]\\n\\n for quest in questions.question_text:\\n tokens = preprocess_text(quest)\\n if self.config.include_unknown:\\n idxs = [self.word2idx.get(token, unk_idx) for token in\\n tokens]\\n else:\\n idxs = [self.word2idx.get(token) for token in tokens]\\n idxs = [idx for idx in idxs if idx]\\n fo.write((str(\\\" \\\".join(str(num) for num in idxs)) + \\\"\\\\n\\\"))\",\n \"def get_docs_embedding(docs_tok, model, dim=300):\\n all_docs_embedding = []\\n for doc in docs_tok:\\n all_docs_embedding.append(text2vec(doc, model, dim))\\n cols = [str(i) for i in range(dim)]\\n embeddings = pd.DataFrame(data=all_docs_embedding)\\n embeddings.columns = cols\\n embeddings.to_parquet('../model/docs_embeddings.parquet', index=False)\\n return np.array(all_docs_embedding)\",\n \"def Emojify_V2(input_shape, word_to_vec_map, word_to_index):\\n \\n ### START CODE HERE ###\\n # Define sentence_indices as the input of the graph.\\n # It should be of shape input_shape and dtype 'int32' (as it contains indices, which are integers).\\n sentence_indices = Input(shape = input_shape, dtype = 'int32')\\n \\n # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\\n # def pretrained_embedding_layer(word_to_vec_map, word_to_index): # return embedding_layer\\n embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\\n \\n # Propagate sentence_indices through your embedding layer\\n # (See additional hints in the instructions).\\n embeddings = embedding_layer(sentence_indices) \\n \\n # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\\n # The returned output should be a batch of sequences.\\n X = LSTM(units = 128, return_sequences = True)(embeddings)\\n # Add dropout with a probability of 0.5\\n X = Dropout(rate = 0.5)(X)\\n # Propagate X trough another LSTM layer with 128-dimensional hidden state\\n # The returned output should be a single hidden state, not a batch of sequences.\\n X = LSTM(units = 128, return_sequences = False)(X)\\n # Add dropout with a probability of 0.5\\n X = Dropout(rate = 0.5)(X) \\n # Propagate X through a Dense layer with 5 units\\n X = Dense(units = 5)(X)\\n # Add a softmax activation\\n X = Activation(activation = 'softmax')(X)\\n \\n # Create Model instance which converts sentence_indices into X.\\n model = Model(inputs = sentence_indices, outputs = X)\\n \\n ### END CODE HERE ###\\n \\n return model\",\n \"def generateByWord(model, voc, maxlen=20, diversity=0.5, numwords=42):\\n\\n text, sym_indices, indices_sym = voc\\n syms = set(text)\\n start_index = random.randint(0, len(text) - maxlen - 1) \\n generated = ''\\n sentence = text[start_index: start_index + maxlen]\\n \\n #generated += sentence\\n generated += ' '.join(sentence)\\n print('----- Generating with seed: \\\"' + ' '.join(sentence) + '\\\"')\\n sys.stdout.write(generated)\\n\\n for i in range(numwords):\\n x = np.zeros((1, maxlen, len(syms)))\\n for t, sym in enumerate(sentence):\\n x[0, t, sym_indices[sym]] = 1.\\n \\n preds = model.predict(x, verbose=0)[0]\\n next_index = sample(preds, diversity)\\n next_sym = indices_sym[next_index]\\n generated += ' '+next_sym\\n sentence.append(next_sym)\\n tmpsentence = sentence[1:]\\n sentence = tmpsentence\\n sys.stdout.write(next_sym+' ')\\n sys.stdout.flush()\\n print()\",\n \"def _embed(self):\\n batch_size = tf.shape(self.p)[0]\\n with tf.variable_scope(\\\"emb\\\"):\\n with tf.variable_scope(\\\"char\\\"):\\n pc_emb = tf.reshape(tf.nn.embedding_lookup(\\n self.char_embed, self.pc), \\n [batch_size * self.max_p_len, self.max_w_len, self.vocab.char_embed_dim])\\n qc_emb = tf.reshape(tf.nn.embedding_lookup(\\n self.char_embed, self.qc), \\n [batch_size * self.max_q_len, self.max_w_len, self.vocab.char_embed_dim])\\n cell_fw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\\n cell_bw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\\n cell_fw, cell_bw, pc_emb, self.pc_length, dtype=tf.float32)\\n pc_emb = tf.concat([state_fw, state_bw], axis=1)\\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\\n cell_fw, cell_bw, qc_emb, self.qc_length, dtype=tf.float32)\\n qc_emb = tf.concat([state_fw, state_bw], axis=1)\\n pc_emb = tf.reshape(pc_emb, [batch_size, self.max_p_len, 2 * self.char_hidden_size])\\n qc_emb = tf.reshape(qc_emb, [batch_size, self.max_q_len, 2 * self.char_hidden_size])\\n\\n with tf.name_scope(\\\"word\\\"):\\n p_emb = tf.nn.embedding_lookup(self.word_embed, self.p)\\n q_emb = tf.nn.embedding_lookup(self.word_embed, self.q)\\n\\n with tf.name_scope(\\\"pos\\\"):\\n p_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.p_pos)\\n q_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.q_pos)\\n \\n with tf.name_scope(\\\"em\\\"):\\n sh = tf.shape(self.p_em)\\n resh = [sh[0], sh[1], 1]\\n p_em_feat = tf.reshape(tf.cast(self.p_em, dtype=tf.float32), shape=resh)\\n\\n self.p_emb = tf.concat([p_emb, pc_emb, p_pos_emb, p_em_feat], axis=2)\\n self.q_emb = tf.concat([q_emb, qc_emb, q_pos_emb], axis=2)\",\n \"def get_word_embeddings(self):\\n embedding_index = {}\\n with open('./glove/glove.6B.100d.txt', encoding=\\\"utf8\\\") as f:\\n for line in f:\\n values = line.split()\\n word = values[0]\\n coefs = np.asarray(values[1:], dtype='float32')\\n embedding_index[word] = coefs\\n return embedding_index\",\n \"def sentence_to_embedding(sent, word_to_vec, seq_len, embedding_dim):\\n embedding_vec = np.zeros((seq_len,embedding_dim))\\n for i in range(min(len(sent),seq_len)):\\n embedding_vec[i,:] = word_to_vec.get(sent[i])\\n return embedding_vec\",\n \"def forward(self, sentence, p_sentence, pos_tags, lengths, target_idx_in, region_marks,\\n local_roles_voc, frames, local_roles_mask,\\n sent_pred_lemmas_idx, dep_tags, dep_heads, targets, predicate_identification, all_l_ids,\\n Predicate_link, Predicate_Labels_nd, Predicate_Labels,\\n unlabeled_sentence_in=False, p_unlabeled_sentence_in=False, unlabeled_sen_lengths=False, test=False,\\n cvt_train=False):\\n #contruct input for DEP\\n embeds_DEP = self.word_embeddings_DEP(sentence)\\n add_zero = torch.zeros((self.batch_size, 1, self.word_emb_dim)).to(device)\\n embeds_DEP = embeds_DEP.view(self.batch_size, len(sentence[0]), self.word_emb_dim)\\n embeds_DEP_cat = torch.cat((self.VR_word_embedding_random + add_zero, embeds_DEP), 1)\\n\\n\\n fixed_embeds_DEP = self.word_fixed_embeddings(p_sentence)\\n add_zero = torch.zeros((self.batch_size, 1, self.word_emb_dim)).to(device)\\n fixed_embeds_DEP_cat = torch.cat((self.VR_word_embedding + add_zero, fixed_embeds_DEP), 1)\\n\\n embeds_forDEP = torch.cat((embeds_DEP_cat, fixed_embeds_DEP_cat), 2)\\n\\n embeds_forDEP = self.DEP_input_dropout(embeds_forDEP)\\n\\n\\n #first layer\\n embeds_sort, lengths_sort, unsort_idx = self.sort_batch(embeds_forDEP, lengths+1)\\n embeds_sort = rnn.pack_padded_sequence(embeds_sort, lengths_sort, batch_first=True)\\n # hidden states [time_steps * batch_size * hidden_units]\\n hidden_states, self.hidden = self.BiLSTM_0(embeds_sort, self.hidden)\\n # it seems that hidden states is already batch first, we don't need swap the dims\\n # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(self.batch_size, -1, )\\n hidden_states, lens = rnn.pad_packed_sequence(hidden_states, batch_first=True)\\n # hidden_states = hidden_states.transpose(0, 1)\\n hidden_states_0 = hidden_states[unsort_idx]\\n\\n # second_layer\\n embeds_sort, lengths_sort, unsort_idx = self.sort_batch(hidden_states_0, lengths+1)\\n embeds_sort = rnn.pack_padded_sequence(embeds_sort, lengths_sort, batch_first=True)\\n # hidden states [time_steps * batch_size * hidden_units]\\n hidden_states, self.hidden_2 = self.BiLSTM_1(embeds_sort, self.hidden_2)\\n # it seems that hidden states is already batch first, we don't need swap the dims\\n # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(self.batch_size, -1, )\\n hidden_states, lens = rnn.pad_packed_sequence(hidden_states, batch_first=True)\\n #hidden_states = hidden_states.transpose(0, 1)\\n hidden_states_1 = hidden_states[unsort_idx]\\n\\n ###########################################\\n\\n Head_hidden = self.DEP_link_H_dropout(F.relu(self.hidLayerFOH_link(hidden_states_1)))\\n Dependent_hidden = self.DEP_link_M_dropout(F.relu(self.hidLayerFOM_link(hidden_states_1)))\\n\\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\\n Head_hidden = torch.cat((Head_hidden, Variable(bias_one)), 2)\\n\\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\\n Dependent_hidden = torch.cat((Dependent_hidden, Variable(bias_one)), 2)\\n\\n left_part = torch.mm(Dependent_hidden.view(self.batch_size * (len(sentence[0]) + 1), -1), self.W_R_link)\\n left_part = left_part.view(self.batch_size, (len(sentence[0]) + 1), -1)\\n Head_hidden = Head_hidden.view(self.batch_size, (len(sentence[0]) + 1), -1).transpose(1, 2)\\n tag_space = torch.bmm(left_part, Head_hidden).view(\\n (len(sentence[0]) + 1) * self.batch_size, len(sentence[0]) + 1)\\n\\n heads = np.argmax(tag_space.cpu().data.numpy(), axis=1)\\n\\n nums = 0.0\\n wrong_nums = 0.0\\n for a, b in zip(heads, dep_heads.flatten()):\\n if b== -1:\\n continue\\n nums += 1\\n if a != b:\\n wrong_nums += 1\\n\\n loss_function = nn.CrossEntropyLoss(ignore_index=-1)\\n DEPloss_link = loss_function(tag_space, torch.from_numpy(dep_heads).to(device).view(-1))\\n\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++\\n Head_hidden_tag = self.DEP_tag_H_dropout(F.relu(self.hidLayerFOH_tag(hidden_states_1)))\\n Dependent_hidden_tag = self.DEP_tag_M_dropout(F.relu(self.hidLayerFOM_tag(hidden_states_1)))\\n\\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\\n Head_hidden_tag = torch.cat((Head_hidden_tag, Variable(bias_one)), 2)\\n\\n bias_one = torch.ones((self.batch_size, len(sentence[0]) + 1, 1)).to(device)\\n Dependent_hidden_tag = torch.cat((Dependent_hidden_tag, Variable(bias_one)), 2)\\n\\n left_part = torch.mm(Dependent_hidden_tag.view(self.batch_size * (len(sentence[0]) + 1), -1), self.W_R_tag)\\n left_part = left_part.view(self.batch_size, (len(sentence[0]) + 1) * self.dep_size, -1)\\n Head_hidden_tag = Head_hidden_tag.view(self.batch_size, (len(sentence[0]) + 1), -1).transpose(1, 2)\\n tag_space_tag = torch.bmm(left_part, Head_hidden_tag).view(\\n (len(sentence[0]) + 1) * self.batch_size, self.dep_size, len(sentence[0]) + 1).transpose(1, 2)\\n\\n tag_space_tag = tag_space_tag[np.arange(0, (len(sentence[0]) + 1) * self.batch_size), dep_heads.flatten()]\\n tag_space_tag = tag_space_tag.view((len(sentence[0]) + 1) * self.batch_size, -1)\\n heads_tag = np.argmax(tag_space_tag.cpu().data.numpy(), axis=1)\\n\\n nums_tag = 0.0\\n wrong_nums_tag = 0.0\\n for a, b in zip(heads_tag, dep_tags.view(-1).cpu().data.numpy()):\\n if b == -1 or b == 0:\\n continue\\n nums_tag += 1\\n if a != b:\\n wrong_nums_tag += 1\\n\\n loss_function = nn.CrossEntropyLoss(ignore_index=0)\\n DEPloss_tag = loss_function(tag_space_tag, dep_tags.view(-1))\\n\\n #####################################################\\n h_layer_0 = hidden_states_0[:, 1:] # .detach()\\n h_layer_1 = hidden_states_1[:, 1:] # .detach()\\n\\n w = F.softmax(self.elmo_w, dim=0)\\n SRL_composer = self.elmo_gamma * (w[0] * h_layer_0 + w[1] * h_layer_1)\\n SRL_composer = self.elmo_mlp(SRL_composer)\\n\\n\\n fixed_embeds = self.word_fixed_embeddings(p_sentence)\\n fixed_embeds = fixed_embeds.view(self.batch_size, len(sentence[0]), self.word_emb_dim)\\n embeds_SRL = self.word_embeddings_SRL(sentence)\\n embeds_SRL = embeds_SRL.view(self.batch_size, len(sentence[0]), self.word_emb_dim)\\n sent_pred_lemmas_embeds = self.p_lemma_embeddings(sent_pred_lemmas_idx)\\n pos_embeds = self.pos_embeddings(pos_tags)\\n region_marks = self.region_embeddings(region_marks).view(self.batch_size, len(sentence[0]), 16)\\n\\n SRL_hidden_states = torch.cat((embeds_SRL, fixed_embeds, pos_embeds, sent_pred_lemmas_embeds, region_marks, SRL_composer), 2)\\n SRL_hidden_states = self.SRL_input_dropout(SRL_hidden_states)\\n\\n\\n # SRL layer\\n embeds_sort, lengths_sort, unsort_idx = self.sort_batch(SRL_hidden_states, lengths)\\n embeds_sort = rnn.pack_padded_sequence(embeds_sort, lengths_sort.cpu().numpy(), batch_first=True)\\n # hidden states [time_steps * batch_size * hidden_units]\\n hidden_states, self.hidden_4 = self.BiLSTM_SRL(embeds_sort, self.hidden_4)\\n # it seems that hidden states is already batch first, we don't need swap the dims\\n # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(self.batch_size, -1, )\\n hidden_states, lens = rnn.pad_packed_sequence(hidden_states, batch_first=True)\\n # hidden_states = hidden_states.transpose(0, 1)\\n hidden_states = hidden_states[unsort_idx]\\n #hidden_states = self.hidden_state_dropout_SRL(hidden_states)\\n\\n # B * H\\n hidden_states_3 = hidden_states\\n hidden_states_word = self.dropout_1_SRL(F.relu(self.Non_Predicate_Proj(hidden_states_3)))\\n predicate_embeds = hidden_states_3[np.arange(0, hidden_states_3.size()[0]), target_idx_in]\\n hidden_states_predicate = self.dropout_2_SRL(F.relu(self.Predicate_Proj(predicate_embeds)))\\n\\n bias_one = torch.ones((self.batch_size, len(sentence[0]), 1)).to(device)\\n hidden_states_word = torch.cat((hidden_states_word, Variable(bias_one)), 2)\\n\\n bias_one = torch.ones((self.batch_size, 1)).to(device)\\n hidden_states_predicate = torch.cat((hidden_states_predicate, Variable(bias_one)), 1)\\n\\n left_part = torch.mm(hidden_states_word.view(self.batch_size * len(sentence[0]), -1), self.W_R)\\n left_part = left_part.view(self.batch_size, len(sentence[0]) * self.tagset_size, -1)\\n hidden_states_predicate = hidden_states_predicate.view(self.batch_size, -1, 1)\\n tag_space = torch.bmm(left_part, hidden_states_predicate).view(\\n len(sentence[0]) * self.batch_size, -1)\\n SRLprobs = F.softmax(tag_space, dim=1)\\n\\n targets = targets.view(-1)\\n\\n loss_function = nn.CrossEntropyLoss(ignore_index=0)\\n SRLloss = loss_function(tag_space, targets)\\n\\n\\n\\n return SRLloss, DEPloss_tag, DEPloss_link, SRLloss, SRLprobs, wrong_nums, nums, wrong_nums_tag, nums_tag, \\\\\\n nums - wrong_nums, nums, nums, \\\\\\n nums_tag - wrong_nums_tag, nums_tag, nums_tag,\",\n \"def load_word2vect(self, file_path):\\n self.embeddings = []\\n self.word_to_idx = {'<pad>' : 0}\\n self.vocab = ['<pad>']\\n\\n model = w2v.load(file_path)\\n self.embedding_size = model.vectors.shape[1]\\n pad_embedding = np.zeros(self.embedding_size, \\\"float32\\\")\\n self.embeddings.append(pad_embedding)\\n\\n train_words_set = set([word for text in self.train_data for word in\\n text[1].split(\\\" \\\")])\\n\\n for w in model.vocab:\\n if w in train_words_set:\\n self.word_to_idx[w] = len(self.vocab)\\n self.vocab.append(w)\\n self.embeddings.append(model[w])\\n\\n del model\",\n \"def visualize_attention(test_seq, i,\\n model,\\n id2wrd, # word_index.\\n n):\\n \\n id2wrd = dict((v, k) for k, v in id2wrd.items()) # Exchange key with values.\\n\\n get_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[4].output])\\n out = get_layer_output([test_seq, ])[0] # test mode\\n\\n att_w = model.layers[5].get_weights() # The attention layer is the sixth layer.\\n\\n eij = np.tanh(np.dot(out[i], att_w[0]))\\n# print(\\\"1 eij is ............\\\")\\n# print(eij)\\n ListToCSV(eij, 'eij.csv') # added by Shigang\\n ai = np.exp(eij)\\n# print(\\\"1 ai is ............\\\")\\n# print(ai)\\n ListToCSV(ai, 'ai.csv') # added by Shigang\\n weights = ai/np.sum(ai)\\n# print(\\\"1 weights is ............\\\")\\n# print(weights)\\n ListToCSV(weights, '1weights.csv') # added by Shigang\\n weights = np.sum(weights,axis=1)\\n# print(\\\"2 weights is ............\\\")\\n# print(weights)\\n ListToCSV(weights, '2weights.csv') # added by Shigang\\n topKeys = np.argpartition(weights,-n)[-n:]\\n\\n print (' '.join([id2wrd[wrd_id] for wrd_id in test_seq[i] if wrd_id != 0.]))\\n print ('--------------------------Attentive Words start: --------------------------------------')\\n \\n for k in test_seq[i][topKeys]:\\n if k != 0.:\\n print (id2wrd[k])\\n print ('--------------------------Attentive Words end: --------------------------------------')\\n \\n return\",\n \"def getEmbeddings(model, words):\\n\\tembeddings = {}\\n\\tfor word in words:\\n\\t\\tembeddings[word] = model[word]\\n\\treturn embeddings\",\n \"def convert_data_to_model_input(self, origin_datas, add_unknow_words=True):\\n def _word_to_id(word):\\n if word in self.word_to_idx:\\n return self.word_to_idx[word]\\n elif add_unknow_words:\\n self.word_to_idx[word] = len(self.vocab)\\n self.vocab.append(word)\\n self.embeddings.append(np.random.uniform(-0.25, 0.25,\\n self.embedding_size))\\n return self.word_to_idx[word]\\n else:\\n # padding id\\n return 0\\n\\n x_inputs = []\\n y_inputs = []\\n\\n for data in origin_datas:\\n y_inputs.append(self.labels_to_idx[data[0]])\\n\\n words = data[1].split(\\\" \\\")\\n ids = list(map(_word_to_id, words))\\n\\n x_inputs.append(ids)\\n\\n return x_inputs, y_inputs\",\n \"def __init__(self, word_embed_size, vocab):\\n super(ModelEmbeddings, self).__init__()\\n\\n ### YOUR CODE HERE for part 1h\\n dropout_rate = 0.3\\n n_chars = len(vocab.char2id)\\n self.char_embed_size = 50\\n self.word_embed_size = word_embed_size\\n self.vocab = vocab\\n self.char_embed = nn.Embedding(n_chars, self.char_embed_size)\\n self.conv = CNN(self.char_embed_size, word_embed_size)\\n self.highway = Highway(word_embed_size)\\n self.dropout = nn.Dropout(dropout_rate)\\n ### END YOUR CODE\",\n \"def model(mtf_features, other_features, params, mesh, variable_dtype, context=None):\\n\\n x, batch_dim, sequence_dim, embd_dim, vocab_dim, embed_sequence_dim = parse_inputs(mtf_features, other_features)\\n\\n if is_incremental_inference(context):\\n # reshape inputs if in inference mode\\n x = mtf.gather(x, context.position - 1, sequence_dim)\\n x = mtf.reshape(x, [batch_dim])\\n\\n use_axial_pos_emb = params[\\\"axial_pos_emb\\\"] is not None\\n\\n if not use_axial_pos_emb:\\n # Use standard position encoding\\n wpe = mtf.get_variable(mesh, \\\"wpe\\\", mtf.Shape([embed_sequence_dim, embd_dim]),\\n initializer=tf.random_normal_initializer(stddev=0.01),\\n master_dtype=variable_dtype.master_dtype,\\n slice_dtype=variable_dtype.slice_dtype,\\n activation_dtype=variable_dtype.activation_dtype)\\n else:\\n wpe = axial_positional_emb(embd_dim, mesh, params, variable_dtype)\\n\\n # Text encoding\\n wte = mtf.get_variable(mesh, \\\"wte\\\", mtf.Shape([vocab_dim, embd_dim]),\\n initializer=tf.random_normal_initializer(stddev=0.02),\\n master_dtype=variable_dtype.master_dtype,\\n slice_dtype=variable_dtype.slice_dtype,\\n activation_dtype=variable_dtype.activation_dtype)\\n\\n with tf.variable_scope(\\\"token_embd\\\"):\\n # Text embedding\\n h = mtf.gather(wte, x, vocab_dim)\\n if params[\\\"embed_dropout\\\"] > 0 and params[\\\"mode\\\"] == \\\"train\\\":\\n h = mtf.dropout(h, rate=params[\\\"embed_dropout\\\"], name=\\\"wte_dropout\\\")\\n\\n with tf.variable_scope(\\\"pos_embd\\\"):\\n # Positional embedding\\n position_indices = mtf.range(mesh, sequence_dim, tf.int64) if not is_incremental_inference(context) else (\\n context.position - 1)\\n pos_emb = mtf.gather(wpe, position_indices, wpe.shape[0])\\n if params[\\\"embed_dropout\\\"] > 0 and params[\\\"mode\\\"] == \\\"train\\\":\\n pos_emb = mtf.dropout(pos_emb, rate=params[\\\"embed_dropout\\\"], name=\\\"wte_dropout\\\")\\n h += pos_emb\\n\\n aux_losses = 0 # instantiate auxiliary losses (for MOE models)\\n\\n for layer in range(params[\\\"n_layer\\\"]):\\n # attn blocks\\n share_parameters = exists(params[\\\"share_parameters\\\"]) and params[\\\"share_parameters\\\"] == True\\n block_scope = f\\\"h{layer}\\\" if not share_parameters else \\\"\\\"\\n\\n block_fn = block(params=params, scope=block_scope, layer_num=layer,\\n bias=other_features[\\\"attn_bias\\\"],\\n sequence_dim=sequence_dim,\\n memory_length_dim=other_features[\\\"memory_length_dim\\\"],\\n variable_dtype=variable_dtype,\\n context=context)\\n\\n # If true and in train mode, enable gradient checkpointing\\n recompute_grad = params[\\\"recompute_grad\\\"] and (params[\\\"mode\\\"] == \\\"train\\\") == True\\n h, loss = block_fn(h) if not recompute_grad else mtf.recompute_grad(block_fn, [h])\\n aux_losses += loss\\n\\n no_weight_tie_emb = params[\\\"no_weight_tie\\\"] == True\\n if no_weight_tie_emb:\\n with tf.variable_scope(\\\"wte_final_linear\\\"):\\n logits = linear(h, \\\"linear_out\\\", vocab_dim, variable_dtype=variable_dtype, params=params)\\n else:\\n # Layer normalize & affine transform\\n h = layer_norm(h, \\\"ln_f\\\", variable_dtype=variable_dtype)\\n seq_dim = sequence_dim if not is_incremental_inference(context) else mtf.Dimension(\\\"sequence\\\", 1)\\n with tf.variable_scope(\\\"wte_final_einsum\\\"):\\n # Equivalent to tf.matmul\\n logits = mtf.einsum([h, wte], output_shape=[batch_dim, seq_dim, vocab_dim])\\n\\n if params[\\\"mode\\\"] in [\\\"train\\\", \\\"eval\\\"]:\\n labels = mtf_features[\\\"labels\\\"]\\n z_loss = params.get(\\\"z_loss\\\", 1e-4) # an auxiliary loss used to stabilize mtf xentropy\\n\\n # Go to full precision for the logits \\n logits = mtf.cast(logits, tf.float32)\\n\\n with tf.variable_scope(\\\"xentropy_final\\\"):\\n loss_batch = mtf.layers.softmax_cross_entropy_with_logits(logits=logits, targets=labels,\\n vocab_dim=logits.shape[-1], z_loss=z_loss)\\n\\n # For non-autoregressive models (masked language modeling training)\\n # Make sure labels with padding tokens are not counted in the loss\\n if not params[\\\"causal\\\"]:\\n padding_id = params.get(\\\"padding_id\\\", 0)\\n loss_batch = mtf.where(mtf.not_equal(labels, padding_id), loss_batch, mtf.zeros_like(loss_batch))\\n\\n with tf.variable_scope(\\\"reduce_mean_final\\\"):\\n loss = mtf.reduce_mean(loss_batch)\\n\\n loss += aux_losses # Add on auxiliary losses (currently only used for MoE)\\n loss /= params[\\\"num_microbatches\\\"]\\n # Convert to train dtype\\n loss = mtf.cast(loss, variable_dtype.slice_dtype)\\n else:\\n loss = None\\n loss_batch = None\\n\\n # Cast back to checkpoint dtype\\n logits = mtf.cast(logits, variable_dtype.master_dtype)\\n return logits, loss, loss_batch\",\n \"def skipgram(current_center_word, outside_words, word2ind,\\n center_word_vectors, outside_vectors, dataset,\\n word2vec_loss_and_gradient=naive_softmax_loss_and_gradient):\\n loss = 0.0\\n grad_center_vecs = np.zeros(center_word_vectors.shape)\\n grad_outside_vectors = np.zeros(outside_vectors.shape)\\n\\n ### YOUR CODE HERE\\n center_word_idx = word2ind[current_center_word]\\n center_word_vec = center_word_vectors[center_word_idx] \\n \\n for outside_word in outside_words:\\n outside_word_idx = word2ind[outside_word]\\n _loss_sk, _grad_center_vecs, _grad_outside_vectors = \\\\\\n word2vec_loss_and_gradient(\\n center_word_vec, outside_word_idx, outside_vectors, dataset)\\n loss += _loss_sk\\n grad_center_vecs[center_word_idx] += _grad_center_vecs\\n grad_outside_vectors += _grad_outside_vectors \\n ### END YOUR CODE\\n\\n return loss, grad_center_vecs, grad_outside_vectors\",\n \"def _add_seq2seq(self):\\n hps = self._hps\\n vsize = self._vocab.size() # size of the vocabulary\\n \\n with tf.variable_scope('seq2seq'):\\n # Some initializers\\n self.rand_unif_init = tf.random_uniform_initializer(-hps.rand_unif_init_mag, hps.rand_unif_init_mag, seed=123)\\n self.trunc_norm_init = tf.truncated_normal_initializer(stddev=hps.trunc_norm_init_std)\\n\\n\\n with tf.variable_scope('embedding'):\\n if hps.pretrained_embeddings:\\n word2vec = load_embeddings(hps.embeddings_path, self._vocab.word2id, hps.rand_unif_init_mag)\\n self.embedding = tf.get_variable('embedding', [vsize, hps.emb_dim],\\n dtype=tf.float32, initializer=tf.constant_initializer(word2vec))\\n # self.assign_embedding = tf.assign(self.embedding, word2vec)\\n else:\\n self.embedding = tf.get_variable('embedding', [vsize, hps.emb_dim],\\n dtype=tf.float32, initializer=self.trunc_norm_init)\\n if hps.mode==\\\"train\\\": self._add_emb_vis(self.embedding) # add to tensorboard\\n\\n # tensor with shape (batch_size, max_enc_steps, emb_size)\\n emb_enc_inputs = tf.nn.embedding_lookup(self.embedding, self._enc_batch)\\n if self._hps.hier:\\n enc_batch_sections = tf.unstack(self._enc_batch_sections, axis=1)\\n sec_emb_enc_inputs = [tf.nn.embedding_lookup(self.embedding, section)\\n for section in enc_batch_sections]\\n # list length max_dec_steps containing shape (batch_size, emb_size)\\n emb_dec_inputs = [tf.nn.embedding_lookup(self.embedding, x)\\n for x in tf.unstack(self._dec_batch, axis=1)]\\n\\n\\n # Hierarchical attention model\\n if self._hps.hier:\\n with tf.variable_scope('encoder'), tf.device(self._next_device()):\\n sec_enc_outs = []\\n states_fw = []\\n states_bw = []\\n states = []\\n\\n # level 1, encode words to sections\\n with tf.variable_scope(\\\"word_level_encoder\\\", reuse=tf.AUTO_REUSE) as scope:\\n encoder_outputs_words = []\\n cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n fw_st, bw_st = None, None\\n if self._hps.use_do: # DropOut\\n cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=1.0 - self._hps.do_prob)\\n cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=1.0 - self._hps.do_prob)\\n for i in range(self._hps.num_sections):\\n encoder_tmp_output, (fw_st, bw_st) = tf.nn.bidirectional_dynamic_rnn(\\n cell_fw, cell_bw, inputs=sec_emb_enc_inputs[i], dtype=tf.float32,\\n sequence_length=self._batch_sections_len[:,i], swap_memory=True, initial_state_bw=bw_st, initial_state_fw=fw_st)\\n # concatenate the forwards and backwards states\\n encoder_tmp_output = tf.concat(axis=2, values=encoder_tmp_output) #shape=[batch x seq_len x hidden_size]\\n \\n encoder_outputs_words.append(encoder_tmp_output)\\n # instead of concating the fw and bw states, we use a ff network\\n combined_state = self._reduce_states(fw_st, bw_st)\\n states.append(combined_state)\\n scope.reuse_variables()\\n \\n # level 2, encode sections to doc\\n encoder_outputs_words = tf.stack(encoder_outputs_words, axis=1) # shape [batch x num_sections x seq_len x hidden_size]\\n shapes = encoder_outputs_words.shape\\n encoder_outputs_words = tf.reshape(encoder_outputs_words, (shapes[0].value, -1, shapes[-1].value)) #shape=[batch x (seq_len * num_sections) x hidden_size]\\n\\n doc_sections_h = tf.stack([s.h for s in states], axis=1) # [batch x num_sections x hidden_size]\\n doc_sections_c = tf.stack([s.c for s in states], axis=1) # [batch x num_sections x hidden_size]\\n\\n with tf.variable_scope(\\\"section_level_encoder\\\"):\\n if FLAGS.section_level_encoder == 'RNN':\\n cell_fw_1 = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n cell_bw_1 = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n if self._hps.use_do:\\n cell_fw_1 = tf.contrib.rnn.DropoutWrapper(cell_fw_1, output_keep_prob=1.0 - self._hps.do_prob)\\n cell_bw_1 = tf.contrib.rnn.DropoutWrapper(cell_bw_1, output_keep_prob=1.0 - self._hps.do_prob)\\n encoder_output_sections, (fw_st_2, bw_st_2) =\\\\\\n tf.nn.bidirectional_dynamic_rnn(cell_fw_1, cell_bw_1, inputs=doc_sections_h, sequence_length=self._doc_sec_lens, dtype=tf.float32, swap_memory=True)\\n encoder_output_sections = tf.concat(axis=2, values=encoder_output_sections)\\n doc_sections_state = self._reduce_states(fw_st_2, bw_st_2)\\n else:\\n if FLAGS.section_level_encoder == 'AVG': # average section cells\\n doc_sections_state_h = tf.reduce_mean(doc_sections_h, axis=1)\\n doc_sections_state_c = tf.reduce_mean(doc_sections_c, axis=1)\\n elif FLAGS.section_level_encoder == 'FF': # use a feedforward network to combine section cells\\n doc_sections_state_h = tf.reshape([doc_sections_h.shape[0].eval(), -1])\\n doc_sections_state_h = tf.layers.dense(\\n inputs=doc_sections_state_h,\\n units=self._hps.hidden,\\n activation=tf.nn.relu) \\n doc_sections_state_c = tf.reshape([doc_sections_c.shape[0].eval(), -1])\\n doc_sections_state_c = tf.layers.dense(\\n inputs=doc_sections_state_c,\\n units=self._hps.hidden,\\n activation=tf.nn.relu)\\n else:\\n raise AttributeError('FLAGS.section_level_encoder={} is not a valid option'.format(FLAGS.section_level_encoder))\\n doc_sections_state = tf.contrib.rnn.LSTMStateTuple(doc_sections_state_c, doc_sections_state_h)\\n encoder_output_sections = doc_sections_h \\n \\n elif not self._hps.multi_layer_encoder:\\n with tf.variable_scope('encoder'):\\n with tf.variable_scope('word_level_encoder'):\\n cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n (encoder_outputs, (fw_st, bw_st)) =\\\\\\n tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs=emb_enc_inputs, dtype=tf.float32, sequence_length=self._enc_lens, swap_memory=True)\\n # concatenate the forwards and backwards states\\n encoder_outputs = tf.concat(axis=2, values=encoder_outputs)\\n \\n # stack n layers of lstms for encoder\\n elif self._hps.multi_layer_encoder:\\n # TODO: check\\n for layer_i in xrange(self._hps.enc_layers):\\n with tf.variable_scope('encoder%d'%layer_i), tf.device(\\n self._next_device()):\\n cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n cell_bw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init, state_is_tuple=True)\\n if self._hps.use_do: # add dropout\\n cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, output_keep_prob=1.0 - self._hps.do_prob)\\n cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, output_keep_prob=1.0 - self._hps.do_prob)\\n emb_enc_inputs, (fw_st, bw_st) =\\\\\\n tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs=emb_enc_inputs, dtype=tf.float32, sequence_length=self._enc_lens, swap_memory=True)\\n emb_enc_inputs = tf.concat(axis=2, values=emb_enc_inputs)\\n encoder_outputs = emb_enc_inputs\\n \\n if self._hps.hier:\\n self._enc_sec_states = encoder_output_sections\\n self._enc_states = encoder_outputs_words \\n else:\\n self._enc_states = encoder_outputs\\n self._enc_sec_states = None\\n \\n # convert the encoder bidirectional hidden state to the decoder state\\n # (unidirectional) by an MLP\\n if self._hps.hier:\\n self._dec_in_state = doc_sections_state\\n else:\\n with tf.variable_scope('encoder'):\\n with tf.variable_scope('word_level_encoder'):\\n self._dec_in_state = self._reduce_states(fw_st, bw_st) \\n \\n # Add the decoder\\n\\n with tf.variable_scope('decoder'), tf.device(self._next_device()):\\n cell = tf.contrib.rnn.LSTMCell(\\n self._hps.hidden_dim,\\n state_is_tuple=True,\\n initializer=self.rand_unif_init)\\n \\n # We need to pass in the previous step's coverage vector each time\\n prev_coverage = self.prev_coverage\\\\\\n if hps.mode==\\\"decode\\\" and self._hps.coverage \\\\\\n else None \\n \\n \\n if self._hps.hier:\\n decoder_outputs, self._dec_out_state, self.attn_dists, self.p_gens, self.coverage, self.attn_dists_sec =\\\\\\n self.attn_decoder(emb_dec_inputs,\\n self._dec_in_state,\\n self._enc_states,\\n cell,\\n self._enc_sec_states,\\n num_words_section=self._batch_sections_len,\\n enc_padding_mask=self._enc_padding_mask,\\n enc_section_padding_mask=self._enc_section_padding_mask,\\n initial_state_attention=(self._hps.mode==\\\"decode\\\"),\\n pointer_gen=self._hps.pointer_gen,\\n use_coverage=self._hps.coverage,\\n prev_coverage=prev_coverage,\\n temperature=self._hps.temperature\\n )\\n \\n else:\\n decoder_outputs, self._dec_out_state, self.attn_dists, self.p_gens, self.coverage, _ =\\\\\\n self.attn_decoder(emb_dec_inputs,\\n self._dec_in_state,\\n self._enc_states,\\n cell,\\n encoder_section_states=None,\\n num_words_section=None,\\n enc_padding_mask=self._enc_padding_mask,\\n initial_state_attention=(self._hps.mode==\\\"decode\\\"),\\n pointer_gen=self._hps.pointer_gen,\\n use_coverage=self._hps.coverage,\\n prev_coverage=prev_coverage,\\n ) \\n \\n\\n # Project decoder output to vocabulary\\n with tf.variable_scope('output_projection'), tf.device(self._next_device()):\\n if self._hps.output_weight_sharing:\\n # share weights of embedding layer with projection\\n # self.embedding is in shape [vsize, hps.emb_dim]\\n w_proj = tf.get_variable('w_proj', [self._hps.emb_dim, self._hps.hidden_dim],\\n dtype=tf.float32, initializer=self.trunc_norm_init)\\n w = tf.tanh(tf.transpose(tf.matmul(self.embedding, w_proj))) # shape = [vsize, hps.hidden_dim]\\n \\n # w_t = tf.transpose(w)\\n b = tf.get_variable('b', [vsize],\\n dtype=tf.float32, initializer=self.trunc_norm_init)\\n else: \\n w = tf.get_variable('w', [self._hps.hidden_dim, vsize],\\n dtype=tf.float32, initializer=self.trunc_norm_init)\\n # w_t = tf.transpose(w)\\n b = tf.get_variable('b', [vsize],\\n dtype=tf.float32, initializer=self.trunc_norm_init)\\n # vocabulary score at each decoder step\\n vocab_scores = []\\n for i,output in enumerate(decoder_outputs):\\n if i > 0:\\n tf.get_variable_scope().reuse_variables()\\n vocab_scores.append(tf.nn.xw_plus_b(output, w, b)) # apply the linear layer\\n\\n # the final vocab distribution for each decoder time step\\n # shape of each element is [batch_size, vsize]\\n vocab_dists = [tf.nn.softmax(s) for s in vocab_scores] \\n\\n \\n # pointing / generating\\n if FLAGS.pointer_gen:\\n final_dists = self._calc_final_dist(vocab_dists, self.attn_dists)\\n# log_dists = [tf.log(dist) for dist in final_dists]\\n else:\\n# log_dists = [tf.log(dist) for dist in vocab_dists]\\n final_dists = vocab_dists\\n \\n\\n # Calculate Losses:\\n \\n if self._hps.mode in ['train', 'eval']:\\n # Calculate the loss\\n with tf.variable_scope('loss'), tf.device(self._next_device()):\\n if FLAGS.pointer_gen:\\n # Calculate the loss per step\\n # This is fiddly; we use tf.gather_nd to pick out the gold target words\\n # will be list length max_dec_steps containing shape (batch_size)\\n loss_per_step = [] \\n batch_nums = tf.range(0, limit=hps.batch_size) # shape (batch_size)\\n for dec_step, dist in enumerate(final_dists):\\n # The indices of the target words. shape (batch_size)\\n targets = self._target_batch[:,dec_step] \\n indices = tf.stack( (batch_nums, targets), axis=1) # shape (batch_size, 2)\\n # shape (batch_size). loss on this step for each batch\\n gold_probs = tf.gather_nd(dist, indices)\\n losses = -tf.log(gold_probs)\\n loss_per_step.append(losses)\\n\\n # Apply dec_padding_mask mask and get loss\\n self._loss = _mask_and_avg(loss_per_step, self._dec_padding_mask)\\n \\n\\n else: # baseline model\\n # this applies softmax internally\\n self._loss = tf.contrib.seq2seq.sequence_loss(\\n tf.stack(vocab_scores, axis=1), self._target_batch, self._dec_padding_mask) # this applies softmax internally\\n\\n tf.summary.scalar('loss', self._loss)\\n\\n # Calculate coverage loss from the attention distributions\\n if self._hps.coverage:\\n with tf.variable_scope('coverage_loss'):\\n self._coverage_loss = _coverage_loss(self.attn_dists, self._dec_padding_mask)\\n tf.summary.scalar('coverage_loss', self._coverage_loss)\\n self._total_loss = self._loss + self._hps.cov_loss_wt * self._coverage_loss\\n tf.summary.scalar('total_loss', self._total_loss)\\n \\n # ---------------------------/\\n\\n\\n if self._hps.mode == \\\"decode\\\":\\n assert len(final_dists) == 1 # final_dists is a singleton list containing shape (batch_size, extended_vsize)\\n final_dists = final_dists[0]\\n topk_probs, self._topk_ids = tf.nn.top_k(final_dists, hps.batch_size*2) # take the k largest probs. note batch_size=beam_size in decode mode\\n self._topk_log_probs = tf.log(topk_probs)\",\n \"def forward(self, input):\\n ### YOUR CODE HERE for part 1h\\n # sentence_length x batch_size x max_word_length\\n orig_size = input.size()\\n merged_input = input.reshape(-1, orig_size[-1])\\n # s*b x max_word_length\\n x_char_embed = self.char_embed(merged_input)\\n # s*b x max_word_length x char_embed_size\\n x_reshaped = x_char_embed.transpose(-1, -2)\\n # s*b x char_embed_size x max_word_length\\n x_conv = self.conv(x_reshaped)\\n # out s*b x word_embed_size\\n x_emb = self.highway(x_conv)\\n x_drop = self.dropout(x_emb)\\n split_output = x_drop.reshape(list(orig_size[:-1]) + [self.word_embed_size])\\n return split_output\\n ### END YOUR CODE\",\n \"def word2vec_main(df: pd.DataFrame,\\n window_size: int = WINDOW_SIZE,\\n layer_size: int = VECTOR_DEPTH,\\n negative_sample_size: int = NEG_SAMPLE_SIZE,\\n num_epochs: int = NUM_EPOCHS, \\n learn_rate: float = LEARN_RATE,\\n dist_alpha: float = DIST_ALPHA,\\n verbose: bool = VERBOSE\\n ) -> tuple: \\n \\n # Separate the keywords and the sentences from the DataFrame\\n keywords, corpus = separate_keywords_corpus(df)\\n \\n # Preprocess the corpus\\n print(\\\"Commencing data preprocessing...\\\")\\n w2vp = W2VPreprocessor(keywords, corpus)\\n \\n # Obtain a noise distribution containing vocabulary for use within the\\n # negative sampling\\n noise_distribution = get_noise_distribution(\\n w2vp.preprocess_corpus, w2vp.vocabulary, dist_alpha\\n )\\n \\n # Encode the words such that they correspond with matrix indices\\n word2onehot = one_hot_vocab_encoding(w2vp)\\n \\n # Initialise the embedding and contex matrices\\n matrices = get_matrices((w2vp.vocab_size, layer_size))\\n\\n # Initialise the dataset via the skipgrams methodology\\n skipgram_data = obtain_skipgram_dataset(\\n w2vp.preprocess_corpus, \\n window_size\\n )\\n # print(skipgram_data.head(15))\\n \\n if verbose:\\n print(\\n f\\\"\\\\nTraining Word2vec, via skipgrams and negative sampling, \\\" \\n f\\\"using the following parameters. \\\\n\\\"\\n f\\\"Vocabulary size: {w2vp.vocab_size} \\\\n\\\"\\n f\\\"Window size: {window_size} \\\\n\\\"\\n f\\\"Word vector depth: {layer_size} \\\\n\\\"\\n f\\\"Negative sample size: {negative_sample_size} \\\\n\\\"\\n f\\\"Distribution parameter: {dist_alpha} \\\\n\\\"\\n f\\\"Number of epochs: {num_epochs} \\\\n\\\"\\n f\\\"Learning rate (alpha): {learn_rate} \\\\n\\\"\\n )\\n \\n # Train the model to obtain the final embedding and context matrices. The\\n # embedding matrix will contain the final word vectors, which can be \\n # extracted using the one hot encodings of the original vocabulary\\n matrices = train_w2v(\\n skipgram_data, \\n matrices, \\n word2onehot, \\n w2vp.vocabulary, \\n noise_distribution,\\n negative_sample_size, \\n num_epochs,\\n learn_rate,\\n verbose\\n )\\n \\n return word2onehot, w2vp, matrices\",\n \"def embedding_sentence_with_model(input_file, save_path, max_length, model_path):\\n # load glove model\\n model = gensim.models.KeyedVectors.load_word2vec_format(model_path)\\n lines = _read_csv(input_file)\\n split_lines = []\\n label_list = []\\n for line in lines:\\n split_lines.append(sentence_split(line[1], max_length))\\n label_list.append(int(line[2]))\\n del lines\\n\\n writer = tf.python_io.TFRecordWriter(save_path)\\n for index, line in enumerate(split_lines):\\n bytes_words = []\\n for word in line:\\n if word in model:\\n bytes_words.extend(model[word])\\n else:\\n bytes_words.extend([0] * 300)\\n example = tf.train.Example(features=tf.train.Features(feature={\\n \\\"label\\\":\\n tf.train.Feature(int64_list=tf.train.Int64List(value=[label_list[index]])),\\n \\\"features\\\":\\n tf.train.Feature(float_list=tf.train.FloatList(value=bytes_words))\\n }))\\n writer.write(example.SerializeToString())\",\n \"def train_model(self, d=0.7):\\n #eg: model = spammy.train_model()\\n count_dict = self.freq_count()\\n N = sum(count_dict.values())\\n n_plus = len(count_dict)\\n alpha = (d * n_plus) / N\\n normalizer = alpha * (1/len(self._vocab_set))\\n model = defaultdict(lambda:normalizer)\\n \\n for word in set(self._vocab_set):\\n prob = normalizer\\n if word in count_dict:\\n prob += (count_dict[word] - d) / N\\n prob = math.log(prob)\\n model[word] = prob\\n return model\",\n \"def update_embedding_layer(line_tokens, model, dictionary):\\n\\n global elmo_embedder\\n\\n out_of_corpus_vocab = [word for word in line_tokens if word not in dictionary.word2idx]\\n if len(out_of_corpus_vocab) == 0:\\n return\\n\\n print(\\\"OOV words found:\\\", out_of_corpus_vocab, file=sys.stderr)\\n\\n if model.using_pretrained == \\\"fasttext\\\":\\n pretrained_emb_model = vc.FastText()\\n elif model.using_pretrained == \\\"glove\\\":\\n pretrained_emb_model = vc.GloVe()\\n elif model.using_pretrained == \\\"elmo_top\\\" or model.using_pretrained == \\\"elmo_avg\\\":\\n dirname = os.path.dirname(__file__)\\n options_file = os.path.join(dirname, '../elmo-config/elmo_2x4096_512_2048cnn_2xhighway_options.json')\\n weight_file = os.path.join(dirname, '../elmo-config/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5')\\n # Retrieve cached version if already loaded, saves a lot of time if this code path is executed multiple times\\n if elmo_embedder is None:\\n elmo_embedder = ElmoEmbedder(options_file, weight_file)\\n else:\\n raise Exception(\\\"Unsupported embedding model:\\\", model.using_pretrained)\\n\\n print(\\\"Using Pretrained embedding:\\\", model.using_pretrained)\\n\\n if model.using_pretrained in ['fasttext', 'glove']:\\n\\n pretrained_vectors = pretrained_emb_model.vectors\\n pretrained_stoi = pretrained_emb_model.stoi\\n\\n elif 'elmo' in model.using_pretrained:\\n # We get the embeddings for all line_tokens which includes both known and unknown, if any.\\n # As elmo is character level we can find embedding of any word\\n reduced_vocab = [token for token in line_tokens if token not in\\n [\\\"#\\\", \\\"<EOT>\\\", \\\"<EOL>\\\", \\\"</s>\\\", \\\"<eos>\\\", \\\"<P>\\\", \\\"<unk>\\\"]]\\n pretrained_stoi = {v: k for k, v in enumerate(reduced_vocab)}\\n elmo_embeddings = elmo_embedder.embed_sentence(reduced_vocab)\\n if 'top' in model.using_pretrained:\\n pretrained_vectors = elmo_embeddings[-1]\\n elif 'avg' in model.using_pretrained:\\n pretrained_vectors = np.average(elmo_embeddings, axis=0)\\n pretrained_vectors = torch.from_numpy(pretrained_vectors)\\n out_of_corpus_vocab = [word for word in line_tokens if word not in dictionary.word2idx and word in pretrained_stoi]\\n\\n if len(out_of_corpus_vocab) == 0:\\n return\\n\\n # Update for only unknown/new word\\n new_vectors = []\\n for word in out_of_corpus_vocab:\\n dictionary.add_word(word)\\n new_vectors.append(pretrained_stoi[word])\\n\\n new_vectors = torch.index_select(pretrained_vectors, 0, torch.LongTensor(new_vectors))\\n\\n model.embedder = torch.nn.Embedding.from_pretrained(torch.cat([model.embedder.weight, new_vectors]))\\n if model.tie_weights:\\n model.decoder.weight = model.encoder.weight\",\n \"def load_char_embeddings(self, embedding_dim):\\n self.char_embeddings = np.random.rand(self.char_size(), embedding_dim)\\n self.word_char_embeddings = np.random.rand(self.size(), embedding_dim)\\n for term in [self.pad_term, self.unk_term, self.eos_term]:\\n self.word_char_embeddings[self.get_id(term)] = np.zeros([self.embed_dim])\\n # self.word_char_embeddings = self.embeddings\\n char_emb = CharEmbedding(embedding_dim)\\n for term in self.term2id.keys():\\n if term not in self.initial_terms:\\n char_ids = [self.char_embeddings[self.char2id[char]] for char in term]\\n chars = torch.FloatTensor(char_ids)\\n chars = torch.unsqueeze(chars, 1)\\n\\n hidden = char_emb(chars)\\n self.word_char_embeddings[self.get_id(term)] = hidden.data.numpy()\\n # self.embeddings[self.get_id(term)] = np.zeros([self.embed_dim])\\n # self.embeddings = np.random.rand(self.size(), embedding_dim)\",\n \"def makeD2VLabels(sequences, **kargs): # refactored from seqAnalzyer \\n # from collections import namedtuple # can customize your own attributes (instead of using gensim's attributes such as words and tags)\\n import gensim\\n def index_label(i): \\n return '%s_%s' % (label_prefix, i)\\n\\n # [params] redundant? \\n # cohort_name = kargs.get('cohort', 'diabetes')\\n # seq_ptype = kargs.get('seq_ptype', 'regular') # values: regular, random, diag, med, lab ... default: regular\\n\\n # attributes = D2V.label_attributes # ['codes', 'labels', ] \\n\\n # [old] use gensim.models.doc2vec.TaggedDocument\\n # LabelDoc = namedtuple('LabelDoc', attributes) # a namedtuple with 2 attributes words and tags\\n # LabelDoc = namedtuple('LabelDoc', ['words', 'labels'])\\n label_prefix = seqparams.TDoc.doc_label_prefix \\n exclude = set(string.punctuation)\\n all_docs = []\\n\\n # [input]\\n assert sequences is not None and len(sequences) > 0\\n\\n labels = kargs.get('labels', []) # precomputed sentence labels \\n if not labels: \\n # df_ldoc = labelDoc(sequences, load_=load_label, seqr='full', sortby='freq', seq_ptype=seq_ptype)\\n raise ValueError, \\\"No user-defined labels given.\\\"\\n \\n # [note] below is for generating surrogate class labels \\n # labeling_routine = kargs.get('labeler', labelDocByFreqDiag) # any labelDoc*\\n # assert hasattr(labeling_routine, '__call__'), \\\"Invalid labeler: %s\\\" % labeling_routine\\n # labels = mlabels = labeling_routine(sequences, **kargs)\\n # labelx = labelize()\\n else: \\n assert len(labels) == len(sequences)\\n\\n # label normalization: ensure that each label is a list \\n labelx = TDocTag.labelAsIs(labels) # TDocTag.canonicalize(labels)\\n print('makeD2VLabels> doc tag examples:\\\\n%s\\\\n' % labelx[:10])\\n # each element in tagx should be a list\\n\\n for i, sen in enumerate(sequences):\\n if isinstance(sen, str): \\n word_list = sen.split() \\n else: \\n word_list = sen # split is already done\\n\\n # For every sentences, if the length is less than 3, we may want to discard it\\n # as it seems too short. \\n # if len(word_list) < 3: continue # filter short sentences\\n \\n tagl = labelx[i] # condition tagl is in the list (multilabel) format\\n assert isinstance(tagl, list)\\n if isinstance(sen, str): \\n sen = ''.join(ch for ch in sen if ch not in exclude) # filter excluded characters\\n\\n all_docs.append(gensim.models.doc2vec.TaggedDocument(sen.split(), tagl))\\n # all_docs.append(LabelDoc(sen.split(), tagl)) # format: sequence (list of tokens) + labels (a list of labels)\\n else: \\n\\n all_docs.append(gensim.models.doc2vec.TaggedDocument(sen, tagl))\\n # all_docs.append(LabelDoc(sen, tagl)) # assuming unwanted char already filetered \\n\\n # Print out a sample for one to view what the structure is looking like \\n # print all_docs[0:10]\\n for i, doc in enumerate(all_docs[0:5]+all_docs[-5:]): \\n print('> doc #%d: %s' % (i, doc))\\n # [log] e.g. doc #3: LabelDoc(words=['583.81', '250.41', 'V45.81', ... , '48003'], tags=['362.01_599.0_250.51'])\\n\\n return all_docs\",\n \"def get_weibo_data(vocab_file, vector_file):\\n if os.path.exists(\\\"word_misc.pkl\\\"):\\n return cPickle.load(open(\\\"word_misc.pkl\\\", \\\"rb\\\"))\\n\\n word_misc, word2id, id2word = {}, {}, {}\\n word_count = 0\\n\\n # vocab file\\n print \\\"Building vocabulary ...\\\"\\n for lines in open(vocab_file).readlines():\\n word = lines.split()[0]\\n if not is_unwanted_words(word, ['', '\\\\n']):\\n word2id[word] = word_count\\n id2word[word_count] = word\\n word_count += 1\\n word2id['_START'] = word_count\\n id2word[word_count] = '_START'\\n word_count += 1\\n word2id['_END'] = word_count\\n id2word[word_count] = '_END'\\n word_count += 1\\n word2id['_UNK'] = word_count\\n id2word[word_count] = '_UNK'\\n word_count += 1\\n word2id['_MASK'] = word_count\\n id2word[word_count] = '_MASK'\\n word_count += 1\\n print \\\"Vocabulary size:\\\", word_count\\n\\n # Initialization is refered to in https://www.tensorflow.org/versions/r0.7/tutorials/word2vec/index.html\\n word_emb = (1/np.sqrt(word_count)*(2*np.random.rand(word_count, options['embedding_size']) - 1)).tolist()\\n\\n # load word vectors\\n for lines in open(vector_file).readlines()[1:]:\\n word = lines.split()[0]\\n #if word == '</s>' or word not in word2id.keys():\\n # continue\\n if word not in word2id.keys():\\n continue\\n ids = word2id[word]\\n #print ids, lines, len(word_emb)\\n word_emb[ids] = [float(w) for w in lines.split()[1:]]\\n\\n print len(word_emb), \\\"words have been loaded with\\\", len(word_emb[0]), \\\"dimensions\\\"\\n\\n # load word misc\\n word_misc['id2word'] = id2word\\n word_misc['word2id'] = word2id\\n word_misc['word_count'] = word_count\\n word_misc['word_emb'] = word_emb\\n cPickle.dump(word_misc, open(\\\"word_misc.pkl\\\", \\\"wb\\\"))\\n print \\\"Dump complete.\\\"\\n return word_misc\",\n \"def __init__(self, lstm_step=80, input_d=300, vocab_size=2196018, embedding=None):\\n self.raw_premise = tf.placeholder(shape=[None, lstm_step], dtype=tf.int32, name='premise')\\n self.premise_length = tf.placeholder(shape=[None], dtype=tf.int32, name='premise_length')\\n\\n self.raw_hypothesis = tf.placeholder(shape=[None, lstm_step], dtype=tf.int32, name='hypothesis')\\n self.hypothesis_length = tf.placeholder(shape=[None], dtype=tf.int32, name='hypothesis_length')\\n\\n self.label = tf.placeholder(shape=[None], dtype=tf.int32)\\n # Those operations take too many memory\\n # Use cpu for those operations (deprecated when using truncate embedding)\\n if embedding is not None:\\n self.input_embedding = tf.placeholder(dtype=tf.float32, shape=embedding.shape, name='word_embedding')\\n self.embedding = tf.Variable(tf.zeros(embedding.shape, dtype=tf.float32))\\n else:\\n \\\"\\\"\\\"\\n If embedding is not provided, then use random number as embedding\\n \\\"\\\"\\\"\\n self.embedding = tf.Variable(tf.random_uniform([vocab_size, input_d], minval=-0.05, maxval=0.05))\\n \\\"\\\"\\\"\\n This is the embedding operation. It will be invoked by loading embedding function in the actual model\\n \\\"\\\"\\\"\\n self.load_embedding_op = self.embedding.assign(self.input_embedding)\\n\\n self.premise = tf.nn.embedding_lookup(self.embedding, self.raw_premise)\\n self.hypothesis = tf.nn.embedding_lookup(self.embedding, self.raw_hypothesis)\",\n \"def process_decoder_input(target_data, target_vocab_to_int, batch_size):\\n # Create a constant tensor with the 'go id'.\\n go_id = tf.constant(target_vocab_to_int['<GO>'], shape=(batch_size,1), dtype=tf.int32)\\n # Concatenate the vector without the last word id with the go ids vector\\n processed_input = tf.concat([go_id,target_data[:,:-1]],1)\\n return processed_input\",\n \"def shape_datasets(built_eps, word2id_dict, max_l, n_relation_types):\\r\\n # min_pf = -n_pf/2\\r\\n # max_pf = n_pf/2 # 左闭右开\\r\\n # assert pf_pad < max_pf\\r\\n #\\r\\n # pf_pad_dict = dict() # 为了复用idx_pad_sentence()\\r\\n # for i in range(min_pf, max_pf):\\r\\n # pf_pad_dict[str(i)] = i\\r\\n\\r\\n train_x, train_y, test_x, test_y = [], [], [], []\\r\\n for ep in built_eps: # 每个ep一个list. list中每个元素是ep对应的每个mention sentence对应的word list\\r\\n ep_sent_withPF = []\\r\\n for i in range(len(ep[\\\"sentences\\\"])):\\r\\n sentence = ep[\\\"sentences\\\"][i]\\r\\n indexed_sent = idx_sentence(sentence, word2id_dict)\\r\\n padded_idx_sent = pad_indexed_sentence(indexed_sent, max_l, 0)\\r\\n # pf1_str = \\\" \\\".join([str(pfp[0]) for pfp in pf_sent]) # 为了复用idx_pad_sentence, 来给pf打pad.\\r\\n # pf2_str = \\\" \\\".join([str(pfp[1]) for pfp in pf_sent])\\r\\n # padded_pf1 = pad_indexed_sentence(pf1_sent, max_l, pf_pad_mark)\\r\\n # padded_pf2 = pad_indexed_sentence(pf2_sent, max_l, pf_pad_mark)\\r\\n sent_withPF = [padded_idx_sent, ep[\\\"PFinfo\\\"][i]]\\r\\n ep_sent_withPF.append(sent_withPF)\\r\\n # word_indexed_sents = [idx_pad_sentence(sentence, word2idx_dict, max_l) for sentence in ep[\\\"sentences\\\"]]\\r\\n # labels转vec\\r\\n labels_vec = [0] * n_relation_types\\r\\n for label_idx in ep['labels']: # 所以如果这时0s_as_NA=True, ep['labels']为空dict, label_vec全0.\\r\\n labels_vec[label_idx] = 1\\r\\n\\r\\n if ep[\\\"ds_split\\\"] == \\\"test\\\":\\r\\n test_x.append(ep_sent_withPF)\\r\\n test_y.append(labels_vec)\\r\\n elif ep[\\\"ds_split\\\"] == \\\"train\\\":\\r\\n train_x.append(ep_sent_withPF)\\r\\n train_y.append(labels_vec)\\r\\n else:\\r\\n logging.error('ds_split is {0}, not the required(train or test).'.format(ep[\\\"ds_split\\\"]))\\r\\n return train_x, train_y, test_x, test_y\",\n \"def add_positional_embedding_nd(x, max_length, name=None):\\n with tf.name_scope(\\\"add_positional_embedding_nd\\\"):\\n x_shape = x.shape.as_list()\\n num_dims = len(x_shape) - 2\\n depth = x_shape[-1]\\n base_shape = [1] * (num_dims + 1) + [depth]\\n base_start = [0] * (num_dims + 2)\\n base_size = [-1] + [1] * num_dims + [depth]\\n for i in range(num_dims):\\n shape = base_shape[:]\\n start = base_start[:]\\n size = base_size[:]\\n shape[i + 1] = max_length\\n size[i + 1] = x_shape[i + 1]\\n var = tf.get_variable(\\n name + \\\"_%d\\\" % i,\\n shape,\\n initializer=tf.random_normal_initializer(0, depth ** -0.5))\\n var = var * depth ** 0.5\\n x += tf.slice(var, start, size)\\n return x\",\n \"def train(self, tagged_sentences: Iterator[Tuple[TokenSeq, PosSeq]]) -> Tuple[NDArray, NDArray]:\\n #add tokens\\n for sentence in tagged_sentences:\\n tokens, pos_tags = sentence\\n for pos in pos_tags:\\n self.pos_tags.append(pos)\\n pos_tags.insert(0, \\\"<s>\\\")\\n pos_tags.pop(len(pos_tags) - 1)\\n for i in range(0, len(tokens)):\\n temp_dict = {}\\n temp_dict = add_features(tokens,pos_tags[i],i, temp_dict)\\n self.features.append(temp_dict)\\n #print(self.features)\\n feature_matrix = self.vectorizer.fit_transform(self.features)\\n label_vector = self.le.fit_transform(self.pos_tags)\\n for i in range(0, len(label_vector)):\\n self.l[self.pos_tags[i]] = i\\n \\n self.feature_matrix = feature_matrix\\n self.label_vector = label_vector\\n self.clf.fit(self.feature_matrix, self.label_vector)\\n\\n return (self.feature_matrix, label_vector)\",\n \"def glove():\\n import numpy as np\\n \\\"\\\"\\\" Read from Gluons embedding pickle files\\\"\\\"\\\"\\n with np.load(glove_model_path) as f:\\n matrix = f['idx_to_vec']\\n matrix.setflags(write=0)\\n return matrix, f['idx_to_token'], token_to_idx(f['idx_to_token'])\",\n \"def build_embedding_layer(inputs_, vocab_size, embed_size):\\n embedding = tf.Variable(tf.random_uniform((vocab_size, embed_size), -1, 1))\\n embed = tf.nn.embedding_lookup(embedding, inputs_)\\n \\n return embed\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65528405","0.644471","0.62645143","0.6260667","0.6213953","0.605486","0.6034496","0.5999264","0.59976137","0.59722745","0.595033","0.59265804","0.592424","0.58655846","0.58250475","0.58209103","0.5806985","0.5793572","0.5783189","0.5767571","0.57570547","0.5756751","0.5744018","0.57422787","0.5741897","0.5732505","0.5724516","0.57062614","0.57050306","0.5703484","0.57027686","0.5695758","0.5670036","0.566177","0.5643794","0.5636599","0.5634675","0.5613637","0.5601024","0.55985546","0.5596352","0.559172","0.5584834","0.55847585","0.5571387","0.55707467","0.55566585","0.5555973","0.55557334","0.55537724","0.5547746","0.5545619","0.5536018","0.55331147","0.55324507","0.5519588","0.55166996","0.5512932","0.5506893","0.5506089","0.55002654","0.5499632","0.5498704","0.54985154","0.5496705","0.5493745","0.54906523","0.5486585","0.54820776","0.5480642","0.5478198","0.5478126","0.54730856","0.5463521","0.545854","0.54535824","0.5453038","0.5450447","0.5439581","0.54347163","0.54338455","0.5432726","0.54312575","0.5422454","0.54113257","0.541041","0.5409964","0.54096293","0.5409421","0.54079247","0.53987765","0.5394034","0.5386513","0.53863025","0.53851026","0.5382956","0.53811574","0.53797966","0.53763413","0.53747386","0.5374243"],"string":"[\n \"0.65528405\",\n \"0.644471\",\n \"0.62645143\",\n \"0.6260667\",\n \"0.6213953\",\n \"0.605486\",\n \"0.6034496\",\n \"0.5999264\",\n \"0.59976137\",\n \"0.59722745\",\n \"0.595033\",\n \"0.59265804\",\n \"0.592424\",\n \"0.58655846\",\n \"0.58250475\",\n \"0.58209103\",\n \"0.5806985\",\n \"0.5793572\",\n \"0.5783189\",\n \"0.5767571\",\n \"0.57570547\",\n \"0.5756751\",\n \"0.5744018\",\n \"0.57422787\",\n \"0.5741897\",\n \"0.5732505\",\n \"0.5724516\",\n \"0.57062614\",\n \"0.57050306\",\n \"0.5703484\",\n \"0.57027686\",\n \"0.5695758\",\n \"0.5670036\",\n \"0.566177\",\n \"0.5643794\",\n \"0.5636599\",\n \"0.5634675\",\n \"0.5613637\",\n \"0.5601024\",\n \"0.55985546\",\n \"0.5596352\",\n \"0.559172\",\n \"0.5584834\",\n \"0.55847585\",\n \"0.5571387\",\n \"0.55707467\",\n \"0.55566585\",\n \"0.5555973\",\n \"0.55557334\",\n \"0.55537724\",\n \"0.5547746\",\n \"0.5545619\",\n \"0.5536018\",\n \"0.55331147\",\n \"0.55324507\",\n \"0.5519588\",\n \"0.55166996\",\n \"0.5512932\",\n \"0.5506893\",\n \"0.5506089\",\n \"0.55002654\",\n \"0.5499632\",\n \"0.5498704\",\n \"0.54985154\",\n \"0.5496705\",\n \"0.5493745\",\n \"0.54906523\",\n \"0.5486585\",\n \"0.54820776\",\n \"0.5480642\",\n \"0.5478198\",\n \"0.5478126\",\n \"0.54730856\",\n \"0.5463521\",\n \"0.545854\",\n \"0.54535824\",\n \"0.5453038\",\n \"0.5450447\",\n \"0.5439581\",\n \"0.54347163\",\n \"0.54338455\",\n \"0.5432726\",\n \"0.54312575\",\n \"0.5422454\",\n \"0.54113257\",\n \"0.541041\",\n \"0.5409964\",\n \"0.54096293\",\n \"0.5409421\",\n \"0.54079247\",\n \"0.53987765\",\n \"0.5394034\",\n \"0.5386513\",\n \"0.53863025\",\n \"0.53851026\",\n \"0.5382956\",\n \"0.53811574\",\n \"0.53797966\",\n \"0.53763413\",\n \"0.53747386\",\n \"0.5374243\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11598,"cells":{"query":{"kind":"string","value":"Construct a layernorm module in the TF style (epsilon inside the square root)."},"document":{"kind":"string","value":"def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps\n self.bias.data.zero_()\n self.weight.data.fill_(1.0)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\n f=lambda y,t : gamma*y*(1-y/K)\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y","def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\n\n f=lambda y, t : gamma*y\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y","def layernorm(w_in):\n return nn.LayerNorm(w_in, eps=cfg.LN.EPS)","def _DefaultLN(self, name):\n p = self.params\n return p.layernorm_tpl.Copy().Set(\n name=name,\n input_dim=p.model_dim,\n use_fused_layernorm=p.use_fused_layernorm,\n fprop_dtype=p.fprop_dtype)","def _define_epsilon(n,T,a=1):\n\n return np.sqrt(np.log(n)/T)*a","def __init__(self, epsilon=1e-5, momentum = 0.9, name=\"batch_norm\"):\n \n with tf.variable_scope(name):\n self.epsilon = epsilon\n self.momentum = momentum\n\n self.name = name","def getWavelet(nf):\n harr_wav_L = 1 / np.sqrt(2) * np.ones((1, 2))\n harr_wav_H = 1 / np.sqrt(2) * np.ones((1, 2))\n harr_wav_H[0, 0] = -1 * harr_wav_H[0, 0]\n harr_wav_LL = np.transpose(harr_wav_L) * harr_wav_L\n harr_wav_LH = np.transpose(harr_wav_L) * harr_wav_H\n harr_wav_HL = np.transpose(harr_wav_H) * harr_wav_L\n harr_wav_HH = np.transpose(harr_wav_H) * harr_wav_H\n kLL = makeKernel(harr_wav_LL,nf)\n kLH = makeKernel(harr_wav_LH,nf)\n kHL = makeKernel(harr_wav_HL,nf)\n kHH = makeKernel(harr_wav_HH,nf)\n kLL = tf.convert_to_tensor(kLL,dtype=tf.float32)\n kLH = tf.convert_to_tensor(kLH,dtype=tf.float32)\n kHL = tf.convert_to_tensor(kHL,dtype=tf.float32)\n kHH = tf.convert_to_tensor(kHH,dtype=tf.float32)\n return kLL,kLH,kHL,kHH","def l2_reg_create_layer(prev, n, activation, lambtha):\n regulizer = tf.contrib.layers.l2_regularizer(lambtha)\n init = tf.contrib.layers.variance_scaling_initializer(mode=\"FAN_AVG\")\n tensor = tf.layers.Dense(units=n, activation=activation,\n kernel_initializer=init,\n kernel_regularizer=regulizer)\n return tensor(prev)","def __init__(self, sess, max_iter=50001, optim='adagrad', learning_rate=1e-2,\n d_per_iter=1, g_per_iter=2, d_update=True, g_update=True,\n real_n=1000, real_dim=2, fake_n=1000, z_dim=3, g_out_dim=2,\n g_layers_depth=5, g_layers_width=None, g_activations=None,\n d_out_dim=1, d_layers_depth=5, d_layers_width=5,\n d_activations=None, d_batch_size=25, x_lims=None, y_lims=None,\n grid_gran=21, grid_n=None, dataset='gaussian', expt='test_low_alpha'):\n self.sess = sess\n self.max_iter = max_iter\n self.optim = optim\n self.learning_rate = learning_rate\n\n self.d_per_iter = d_per_iter \n self.g_per_iter = g_per_iter\n self.d_update = d_update\n self.g_update = not d_update \n\n self.real_n = real_n \n self.real_dim = real_dim \n self.fake_n = fake_n\n\n self.z_dim = z_dim\n self.g_out_dim = g_out_dim\n self.g_layers_depth = g_layers_depth\n self.g_layers_width = [[5]] * (g_layers_depth - 1) + [[g_out_dim]]\n self.g_activations = [tf.nn.tanh, tf.nn.elu]\n\n self.d_out_dim = d_out_dim\n self.d_layers_depth = d_layers_depth\n self.d_layers_width = d_layers_width\n self.d_activations = [tf.nn.tanh, tf.nn.relu]\n self.d_batch_size = d_batch_size\n\n self.x_lims = [-6., 2.]\n self.y_lims = [-2., 6.]\n self.grid_gran = grid_gran\n self.grid_n = grid_gran ** 2\n self.grid, self.x_grid, self.y_grid = self.make_grid()\n\n self.dataset = dataset \n self.real_points = load_2d_data(dataset, real_n, real_dim)\n\n self.expt = expt\n\n self.build_model()","def create_kernel(\n self,\n ) -> pyabc.StochasticKernel:\n def kernel_fun(x, x_0, t, par) -> float:\n \"\"\"The kernel function.\"\"\"\n # the kernel value is computed by amici already\n return x['llh']\n\n # create a kernel from function, returning log-scaled values\n kernel = pyabc.distance.SimpleFunctionKernel(\n kernel_fun, ret_scale=pyabc.distance.SCALE_LOG)\n\n return kernel","def l2_reg_create_layer(prev, n, activation, lambtha):\n reg = tf.contrib.layers.l2_regularizer(lambtha)\n init = tf.contrib.layers.variance_scaling_initializer(mode=\"FAN_AVG\")\n t = tf.layers.Dense(units=n, activation=activation,\n kernel_initializer=init,\n kernel_regularizer=reg,\n )\n return t(prev)","def __init__(self, dm, h):\n super().__init__()\n self.h = h\n self.dm = dm\n\n self.depth = dm // h\n\n self.Wq = tf.keras.layers.Dense(dm)\n self.Wk = tf.keras.layers.Dense(dm)\n self.Wv = tf.keras.layers.Dense(dm)\n\n self.linear = tf.keras.layers.Dense(dm)","def ln(inputs, epsilon=1e-5, scope=None):\n mean, var = tf.nn.moments(inputs, [-1], keep_dims=True)\n with tf.variable_scope(scope + 'LN'):\n scale = tf.get_variable('alpha',\n shape=[inputs.get_shape()[-1]],\n initializer=tf.constant_initializer(1))\n shift = tf.get_variable('beta',\n shape=[inputs.get_shape()[-1]],\n initializer=tf.constant_initializer(0))\n LN = scale * (inputs - mean) / tf.sqrt(var + epsilon) + shift\n\n return LN","def laplacian_tf(P, Q):\r\n\r\n # Create transfer function H.\r\n H = np.zeros((P, Q), dtype=np.complex128)\r\n # Create laplacian transfer function.\r\n laplacian = np.array([[0, -1, 0], \r\n [-1, 4, -1], \r\n [0, -1, 0]], dtype=np.int64)\r\n # Center laplacian kernel on P x Q rectangle.\r\n H[int(np.floor(P/2))-1: int(np.floor(P/2))+2, int(np.floor(Q/2))-1: int(np.floor(Q/2))+2] = laplacian\r\n # Calculate laplacian transfer function.\r\n H = np.fft.fftshift(np.fft.fft2(H))\r\n return H","def DKL(p, q,eps=1e-12):\n return -Hshannon(p,eps=eps) + NLL(p, q,eps=eps)","def create_ars_model(odim=10,adim=2,hdims=[128],\n actv=tf.nn.relu,out_actv=tf.nn.tanh):\n import tensorflow as tf\n \n def mlp(x,hdims=[256,256],actv=tf.nn.relu,out_actv=tf.nn.relu):\n ki = tf.truncated_normal_initializer(stddev=0.1)\n for hdim in hdims[:-1]:\n x = tf.layers.dense(x,units=hdim,activation=actv,kernel_initializer=ki)\n return tf.layers.dense(x,units=hdims[-1],\n activation=out_actv,kernel_initializer=ki)\n def placeholder(dim=None):\n return tf.placeholder(dtype=tf.float32,shape=(None,dim) if dim else (None,))\n def placeholders(*args):\n \"\"\"\n Usage: a_ph,b_ph,c_ph = placeholders(adim,bdim,None)\n \"\"\"\n return [placeholder(dim) for dim in args]\n def get_vars(scope):\n return [x for x in tf.compat.v1.global_variables() if scope in x.name]\n \n # Have own session\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n \n # Placeholders\n o_ph = placeholder(odim)\n \n # Policy \n with tf.variable_scope('main'):\n mu = mlp(o_ph,hdims=hdims+[adim],actv=actv,out_actv=out_actv)\n \n # Params\n main_vars = get_vars('main')\n \n model = {'o_ph':o_ph,'mu':mu,'main_vars':main_vars}\n return model, sess","def test_lj612(self):\n x0 = np.array([\n [ 0.0637, 0.0126, 0.2203],\n [ 1.0573, -0.2011, 1.2864],\n [ 2.3928, 1.2209, -0.2230],\n [-0.6891, 1.6983, 0.0780],\n [-0.6312, -1.6261, -0.2601]\n ], dtype=np.float64)\n\n x_ph = tf.placeholder(shape=(5, 3), dtype=np.float64)\n\n params_np = np.array([3.0, 2.0, 1.0, 1.4], dtype=np.float64)\n params = tf.convert_to_tensor(params_np)\n # Ai, Ci\n param_idxs = np.array([\n [0, 3],\n [1, 2],\n [1, 2],\n [1, 2],\n [1, 2]], dtype=np.int32)\n\n exclusions = np.array([\n [0,0,1,0,0],\n [0,0,0,1,1],\n [1,0,0,0,0],\n [0,1,0,0,1],\n [0,1,0,1,0],\n ], dtype=np.bool)\n\n ef = LJ612Force(params, param_idxs, exclusions)\n\n sess = tf.Session()\n sess.run(tf.initializers.global_variables())\n\n num_atoms = 5\n ref_nrg = 0\n\n for i in range(num_atoms):\n Ai = params_np[param_idxs[i, 0]]\n Ci = params_np[param_idxs[i, 1]]\n for j in range(i+1, num_atoms):\n if not exclusions[i, j]:\n Aj = params_np[param_idxs[j, 0]]\n Cj = params_np[param_idxs[j, 1]]\n dij = np.linalg.norm(x0[i] - x0[j])\n ref_nrg += np.sqrt(Ai*Aj)/np.power(dij, 12) - np.sqrt(Ci*Cj)/np.power(dij, 6)\n\n # (ytz) TODO: add a test for forces, for now we rely on\n # autograd to be analytically correct.\n test_nrg_op = ef.energy(x_ph)\n\n test_nrg = sess.run(test_nrg_op, feed_dict={x_ph: x0})\n\n np.testing.assert_almost_equal(ref_nrg, test_nrg, decimal=15)\n\n test_grads_op = ef.gradients(x_ph)\n test_grads = sess.run(test_grads_op, feed_dict={x_ph: x0})\n\n net_force = np.sum(test_grads, axis=0)\n # this also checks that there are no NaNs\n np.testing.assert_almost_equal(net_force, [0,0,0], decimal=14)\n\n assert not np.any(np.isnan(sess.run(ef.hessians(x_ph), feed_dict={x_ph: x0})))\n assert not np.any(np.isnan(sess.run(ef.mixed_partials(x_ph), feed_dict={x_ph: x0})))","def ln(tensor, scope=None, epsilon=1e-5):\n assert(len(tensor.get_shape()) == 2)\n m, v = tf.nn.moments(tensor, [1], keep_dims=True)\n if not isinstance(scope, str):\n scope = ''\n with tf.variable_scope(scope + 'layer_norm'):\n scale = tf.get_variable('scale',\n shape=[tensor.get_shape()[1]],\n initializer=tf.constant_initializer(1))\n shift = tf.get_variable('shift',\n shape=[tensor.get_shape()[1]],\n initializer=tf.constant_initializer(0))\n LN_initial = (tensor - m) / tf.sqrt(v + epsilon)\n\n return LN_initial * scale + shift","def create(self):\n # 1st Layer: Conv -> norm -> ReLu\n conv1 = self.conv(x=self.X, stride_y=1, stride_x=1, padding='SAME', name='conv1')\n norm1 = lrn(conv1, 2, 1e-04, 0.75, name='norm1')\n # Apply relu function\n relu1 = tf.nn.relu(norm1)\n\n # 2st Layer: Conv -> norm -> ReLu\n conv2 = self.conv(x=relu1, stride_y=1, stride_x=1, padding='SAME', name='conv2')\n norm2 = lrn(conv2, 2, 1e-04, 0.75, name='norm2')\n # Apply relu function\n relu2 = tf.nn.relu(norm2)\n\n pool2 = tf.nn.max_pool(relu2, ksize=[1, 3, 3, 1],\n strides=[1, 2, 2, 1],\n padding='SAME')\n\n # 3st Layer: Conv -> norm -> ReLu\n conv3 = self.conv(x=pool2, stride_y=1, stride_x=1, padding='SAME', name='conv3')\n norm3 = lrn(conv3, 2, 1e-04, 0.75, name='norm3')\n # Apply relu function\n relu3 = tf.nn.relu(norm3)\n\n # 4st Layer: Conv -> norm -> ReLu\n conv4 = self.conv(x=relu3, stride_y=1, stride_x=1, padding='SAME', name='conv4')\n norm4 = lrn(conv4, 2, 1e-04, 0.75, name='norm4')\n # Apply relu function\n relu4 = tf.nn.relu(norm4)\n\n pool4 = tf.nn.max_pool(relu4, ksize=[1, 3, 3, 1],\n strides=[1, 2, 2, 1],\n padding='SAME')\n\n # 5st Layer: Conv -> norm -> ReLu\n conv5 = self.conv(x=pool4, stride_y=1, stride_x=1, padding='SAME', name='conv5')\n norm5 = lrn(conv5, 2, 1e-04, 0.75, name='norm5')\n # Apply relu function\n relu5 = tf.nn.relu(norm5)\n\n # 6st Layer: Conv -> norm -> ReLu\n conv6 = self.conv(x=relu5, stride_y=1, stride_x=1, padding='SAME', name='conv6')\n norm6 = lrn(conv6, 2, 1e-04, 0.75, name='norm6')\n # Apply relu function\n relu6 = tf.nn.relu(norm6)\n\n pool6 = tf.nn.avg_pool(relu6, ksize=[1, 4, 4, 1],\n strides=[1, 4, 4, 1],\n padding='SAME')\n\n flattened = tf.reshape(pool6, [-1, 128 * 4])\n self.fc7 = self.fc(flattened, name='fc7')","def sample_gumbel(shape, eps=1e-20):\n U = tf.random_uniform(shape,minval=0,maxval=1)\n return -tf.log(-tf.log(U + eps) + eps)","def sample_gumbel(shape, eps=1e-20):\n U = tf.random_uniform(shape,minval=0,maxval=1)\n return -tf.log(-tf.log(U + eps) + eps)","def ResNetLN34(num_classes=10):\n return ResNet(BasicLNBlock,[3,4,6,3], \n num_classes=num_classes,\n norm_type=\"LN\")","def Lotka_Volterra(a, b, c, d, init, t0, tf, eps):\n\n \n Y=meth_epsilon(init, t0, tf, eps,\\\n eq_Lotka_Volterra(a, b, c, d) , step_RK4)\n return Y","def model_gutenberg(r):\n\n\timport numpy as np\n\n\t#- initialisations ----------------------------------------------------------------------------\n\n\t#- depth intervals\n\td = np.array([0.0, 19.0, 38.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0])\n\td = 6371000.0 - 1000.0 * d\n\n\t#- density\n\trho_array = np.array([2.74, 3.00, 3.32, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.41, 3.53, 3.46, 3.48, 3.50, 3.53, 3.58, 3.62, 3.69, 3.82, 4.01, 4.21, 4.40, 4.56, 4.63])\n\n\t#- vp\n\tvp_array = np.array([6.14, 6.58, 8.20, 8.17, 8.14, 8.10, 8.07, 8.02, 7.93, 7.85, 7.89, 7.98, 8.10, 8.21, 8.38, 8.62, 8.87, 9.15, 9.45, 9.88, 10.30, 10.71, 11.10, 11.35])\n\n\t#- vs\n\tvs_array = np.array([3.55, 3.80, 4.65, 4.62, 4.57, 4.51, 4.46, 4.41, 4.37, 4.35, 4.36, 4.38, 4.42, 4.46, 4.54, 4.68, 4.85, 5.04, 5.21, 5.45, 5.76, 6.03, 6.23, 6.32])\n\n\t#- determine vp, vs and rho for the relevant layer --------------------------------------------\n\n\tfor k in np.arange(len(rho_array)):\n\t\tif (r <= d[k]) & (r > d[k+1]):\n\t\t\trho = rho_array[k]\n\t\t\tvpv = vp_array[k]\n\t\t\tvph = vpv\n\t\t\tvsv = vs_array[k]\n\t\t\tvsh = vsv\n\t\t\teta = 1.0\n\t\t\tcontinue\n\t\telif r <= 5371000.0:\n\t\t\trho = 4.63\n\t\t\tvpv = 11.35\n\t\t\tvph = vpv\n\t\t\tvsv = 6.32\n\t\t\tvsh = vsv\n\t\t\teta = 1.0\n\n\t#- convert to elastic parameters --------------------------------------------------------------\n\n\trho = 1000.0 * rho\n\tvpv = 1000.0 * vpv\n\tvph = 1000.0 * vph\n\tvsv = 1000.0 * vsv\n\tvsh = 1000.0 * vsh\n\n\tA = rho * vph**2\n\tC = rho * vpv**2\n\tN = rho * vsh**2\n\tL = rho * vsv**2\n\tF = eta * (A - 2 * L)\n\n\treturn rho, A, C, F, L, N","def _create_batchnorm(cls, op, op_t):\n # first, we init batchnorm node\n epsilon = 1e-5 # the epsilon value used in singa\n bn_node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n bn_node.attribute.extend([\n helper.make_attribute('momentum', op.handle.factor),\n helper.make_attribute('epsilon', epsilon),\n ])\n # then we add nodes of scal, bias, mean, var\n nodes = []\n running_values = {\"mean\": op.running_mean, \"var\": op.running_var}\n for tmp_name, running_value in running_values.items():\n node_name = op.name + \":\" + tmp_name\n bn_node.input.append(node_name)\n\n nodes.append(bn_node)\n return nodes","def eg3(N_train=1000, N_test=500, depend_ratio_train=0.8, depend_ratio_test=0.2, feature_num=10, stable_ratio=0.4):\n\n def eg3_kernel(n, p, stable_ratio=0.4, depend_ratio=0.8):\n p_stable = int(p * stable_ratio)\n p_noise = p - p_stable\n stable_feature = np.random.randn(n, p_stable)\n noise_feature_dependent = np.zeros([n, p_noise])\n noise_feature_independent = np.random.randn(n, p_noise)\n for i in range(p_noise):\n noise_feature_dependent[:, i] = stable_feature[:, i % p_stable] + stable_feature[:,\n (i + 1) % p_stable] + 2 * np.random.randn(\n n) # still need noise\n noise_depend_label = np.random.uniform(0, 1, n).reshape(-1, 1)\n noise_depend_label = np.concatenate([noise_depend_label] * p_noise, axis=1)\n noise_feature = np.where(noise_depend_label < depend_ratio, noise_feature_dependent, noise_feature_independent)\n\n b = np.zeros([p_stable, 1])\n linear_len = int(p_stable / 2)\n\n for i in range(linear_len): # linear part\n b[i, 0] = (-1) ** i * (i % 3 + 1) * p / 3\n for i in range(linear_len, b.shape[0]): # nonlinear part\n b[i, 0] = p / 2\n\n linear_part = np.matmul(stable_feature[:, :linear_len], b[:linear_len, 0])\n nolinear_part = np.zeros([n, 1])\n for i in range(linear_len, b.shape[0]):\n temp = stable_feature[:, i % p_stable] * stable_feature[:, (i + 1) % p_stable] * b[i, 0]\n temp = temp.reshape(-1, 1)\n nolinear_part += temp\n\n Y = linear_part.reshape(-1, 1) + nolinear_part + np.random.randn(n, 1)\n\n data = {}\n data['stable'] = stable_feature\n data['noise'] = noise_feature\n data['Y'] = Y\n data['params'] = b\n data['kernel'] = 'eg3'\n return data\n\n data_train = eg3_kernel(n=N_train, p=feature_num, stable_ratio=stable_ratio, depend_ratio=depend_ratio_train)\n data_test = eg3_kernel(n=N_test, p=feature_num, stable_ratio=stable_ratio, depend_ratio=depend_ratio_test)\n return data_train, data_test","def __init__(self, model, eps_list, k, a, repeat, random_start=True, loss_func='cw'):\n self.model = model\n self.eps_list = eps_list\n self.k = k\n self.a = a\n self.repeat = repeat\n self.rand = random_start\n\n if loss_func == 'xent':\n loss = model.xent\n elif loss_func == 'cw':\n label_mask = tf.one_hot(model.y,\n 10,\n on_value=1.0,\n off_value=0.0,\n dtype=tf.float32)\n correct_logit = tf.reduce_sum(label_mask * model.y_, axis=1)\n wrong_logit = tf.reduce_max((1 - label_mask) * model.y_, axis=1)\n loss = -tf.nn.relu(correct_logit - wrong_logit + 50)\n else:\n print('Unknown loss function. Defaulting to cross-entropy')\n loss = model.xent\n\n self.grad = tf.gradients(loss, model.x)[0]","def calc_K_tilda(kernel: Type[Kern], X_train: np.array, X_m: np.array):\n Knn = kernel.K(X_train, X_train)\n Knm = kernel.K(X_train, X_m)\n Kmn = kernel.K(X_m, X_train)\n Kmm = kernel.K(X_m, X_m)\n temp = np.dot(np.dot(Knm, np.linalg.inv(Kmm)), Kmn)\n K_tilda = np.subtract(Knn, temp)\n return K_tilda","def create_model(input_shape):\n h, w, c = input_shape\n\n\n # %%%\n inpt = tf.placeholder(tf.float32, (None,h,w,c), 'attributes')\n inpt_flattenned = tf.contrib.layers.flatten(inpt) #tf.reshape(inpt, [-1, h*w*c])\n H1 = tf.layers.dense(inpt_flattenned, units=200, activation=tf.sigmoid)\n H2 = tf.layers.dense(H1, units=20, activation=tf.sigmoid)\n encoding = tf.layers.dense(H2, units=latent_space_size, activation=tf.nn.sigmoid)\n H22 = tf.layers.dense(encoding, units=20, activation=tf.sigmoid)\n H11 = tf.layers.dense(H22, units=200, activation=tf.nn.sigmoid)\n dec_flt = tf.layers.dense(H11, units=h*w*c)\n decode = tf.reshape(dec_flt, [-1, h,w,c])\n# The results which were the most pleasing to the eye were achieved with the following loss function, 2 hidden layers and the latent spaces of tens of dimentions. \n# cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dec_flt, labels=tf.sigmoid(inpt_flattenned)))\n cost = tf.reduce_mean(tf.square(dec_flt-inpt_flattenned))\n # %%%\n\n model = {'cost': cost,\n 'input': input,\n 'enc': encoding,\n 'dec': decode\n }\n return model","def build_mlp(x, output_size, scope, n_layers, size, activation=tf.tanh, output_activation=None, regularizer=None):\n i = 0\n for i in range(n_layers):\n x = tf.layers.dense(inputs=x,units=size, activation=activation, name='fc{}'.format(i), kernel_regularizer=regularizer, bias_regularizer=regularizer)\n\n x = tf.layers.dense(inputs=x, units=output_size, activation=output_activation, name='fc{}'.format(i + 1), kernel_regularizer=regularizer, bias_regularizer=regularizer)\n return x","def make_mlp_model():\n return snt.Sequential([\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\n snt.LayerNorm()\n ])","def sample_gumbel(shape, eps=1e-20):\n U = tf.random_uniform(shape, minval=0, maxval=1)\n return -tf.log(-tf.log(U + eps) + eps)","def _create_elu(cls, op, op_t):\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n\n node.attribute.extend([\n helper.make_attribute('alpha', op.alpha),\n ])\n return node","def sample_gumbel(shape, eps=1e-20):\n U = tf.random_uniform(shape, minval=0, maxval=1)\n return -tf.log(-tf.log(U + eps) + eps)","def get_TF():\n \n # returns a vector of n points from the interval [-1,1]\n rnd = lambda n: np.random.uniform(-1,1,n)\n \n # choose two random points A, B in [-1,1] x [-1,1] \n A = rnd(2)\n B = rnd(2)\n \n # the line can be describe by y = kx + m,\n # k = slope, m = intercept with y axis\n k = (B[1] - A[1]) / (B[0] - A[0])\n m = B[1] - k * B[0] \n w_f = np.array([m, k, -1]) \n \n return w_f","def __init__(self, space, exponent):\n super().__init__(space=space, linear=False)\n self.__norm = LpNorm(space, exponent)\n self.__exponent = float(exponent)","def noam_norm(x, epsilon=1.0, scope=None, reuse=None):\n with tf.name_scope(scope, default_name=\"noam_norm\", values=[x]):\n shape = x.get_shape()\n ndims = len(shape)\n return tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(tf.to_float(shape[-1]))","def noam_norm(x, epsilon=1.0, scope=None, reuse=None):\n with tf.name_scope(scope, default_name=\"noam_norm\", values=[x]):\n shape = x.get_shape()\n ndims = len(shape)\n return tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(tf.to_float(shape[-1]))","def _tf1_ ( self , *args ) :\n #\n if not hasattr ( self , '_wo1' ) : self._wo1 = _WO1_ ( self )\n if not self._wo1 : self._wo1 = _WO1_ ( self )\n ## \n _wo = self._wo1 \n fun = ROOT.TF1 ( funID() , _wo , *args )\n fun.SetNpx ( 500 ) \n #\n return fun","def create_layer(prev, n, activation):\n\n kernel_ini = tf.contrib.layers.variance_scaling_initializer(mode=\"FAN_AVG\")\n layer = tf.layers.Dense(units=n, activation=activation,\n kernel_initializer=kernel_ini, name='layer')\n\n return layer(prev)","def _model(self, scope):\n\n with tf.variable_scope(scope):\n\n act = tf.nn.elu\n initializer = tfcl.variance_scaling_initializer()\n xs = tf.layers.dense(self.state, 400, act, kernel_initializer=initializer)\n xs = tf.layers.dense(xs, 300, None, kernel_initializer=initializer)\n\n xa = tf.layers.dense(self._action, 400, act, kernel_initializer=initializer)\n xa = tf.layers.dense(xa, 300, None, kernel_initializer=initializer)\n\n x = tf.nn.elu(xa + xs)\n\n q_value = tf.layers.dense(x, 1, None, kernel_initializer=initializer)\n\n return q_value","def test_xavier_normal():\n tf.reset_default_graph()\n tf.random.set_random_seed(0)\n xnormal_class = INITIALIZER_REGISTRY['xavier']\n xnormal_obj = xnormal_class({\"uniform\":False})\n tf_init = xnormal_obj.get_entity_initializer(init_type='tf')\n var1 = tf.get_variable(shape=(2000, 100), initializer=tf_init, name=\"var1\")\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n tf_var = sess.run(var1)\n np_var = xnormal_obj.get_entity_initializer(2000, 100, init_type='np')\n # print(np.mean(np_var), np.std(np_var))\n # print(np.mean(tf_var), np.std(tf_var))\n assert(np.round(np.mean(np_var),2)==np.round(np.mean(tf_var),2))\n assert(np.round(np.std(np_var),2)==np.round(np.std(tf_var),2))","def __init__(self, input_dim, hidden_dim, output_dim, epsilon):\n \n self.W1 = np.random.randn(input_dim, hidden_dim) / np.sqrt(input_dim)\n self.W2 = np.random.randn(hidden_dim,output_dim) / np.sqrt(hidden_dim)\n self.b1 = np.zeros((1, hidden_dim))\n self.b2 = np.zeros((1, output_dim))\n self.epsilon = epsilon","def build_critic_z(latent_space_dim: Tuple[int , int] ,num_dense_units: int = 100)->tf.keras.Model:\n\n input = tf.keras.layers.Input(shape=latent_space_dim, name=\"critic_z_input\")\n\n dense = tf.keras.layers.Flatten()(input)\n dense = tf.keras.layers.Dense(units=num_dense_units)(input)\n dense = tf.keras.layers.LeakyReLU(alpha=0.2)(dense)\n dense = tf.keras.layers.Dropout(0.25)(dense)\n\n dense = tf.keras.layers.Dense(units=num_dense_units)(dense)\n dense = tf.keras.layers.LeakyReLU(alpha=0.2)(dense)\n dense = tf.keras.layers.Dropout(0.25)(dense)\n \n dense = tf.keras.layers.Dense(units=1)(dense)\n\n model = tf.keras.Model(inputs=input, outputs=dense, name=\"critic_z\")\n return model","def _create_gemm(cls, op, op_t):\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n\n node.attribute.extend([\n helper.make_attribute('alpha', float(op.alpha)),\n helper.make_attribute('beta', float(op.beta)),\n helper.make_attribute('transA', op.transA),\n helper.make_attribute('transB', op.transB),\n ])\n\n return node","def KD(T=8):\n\n def KD_loss(input, teacher_logits):\n return nn.KLDivLoss()(F.log_softmax(input / T, dim=1), F.softmax(teacher_logits / T, dim=1))\n\n return KD_loss","def decoder(latent):\n out = tf.layers.dense(latent, 400, activation=tf.nn.relu)\n if USE_BINARY:\n out = tf.layers.dense(out, 28 * 28)\n out = tf.reshape(out, [-1, 28, 28, 1])\n return tf.distributions.Bernoulli(logits=out)\n else:\n mean = tf.reshape(tf.layers.dense(out, 28 * 28), [-1, 28, 28, 1])\n logstd = tf.reshape(tf.layers.dense(out, 28 * 28), [-1, 28, 28, 1])\n return tf.distributions.Normal(loc=mean, scale=tf.exp(logstd))","def noam_norm(x, epsilon=1.0, scope=None, reuse=None):\r\n with tf.name_scope(scope, default_name=\"noam_norm\", values=[x]):\r\n shape = x.get_shape()\r\n ndims = len(shape)\r\n return tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(tf.to_float(shape[-1]))","def gelu(input_tensor):\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\n return input_tensor * cdf","def gelu(input_tensor):\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\n return input_tensor * cdf","def __init__(self, num_units, input_size=None,\n nonlinearity=tf.nn.tanh,\n hh_init=tf.random_normal_initializer(stddev=0.15),\n xh_init=tf.random_normal_initializer(stddev=0.15),\n b_init=tf.constant_initializer(0.0, dtype=tf.float32),\n weight_noise=0.0,\n keep_prob=1.0,\n weightnorm=False,\n orthreg=False):\n self._num_units = num_units\n self._input_size = input_size or num_units\n self._nonlinearity = nonlinearity\n self._hh_init = hh_init\n self._xh_init = xh_init\n self._b_init = b_init\n self._weightnorm = weightnorm\n self._keep_prob = keep_prob\n self._weight_noise = weight_noise\n self._orthreg = orthreg","def KL(self):\n\n self.build_cholesky_if_needed()\n\n KL = -0.5 * self.num_inducing * self.num_nodes * self.dim_per_out\n\n for nd in range(self.num_nodes):\n q_sqrt_nd = self.q_sqrt_lst[nd]\n with params_as_tensors_for(q_sqrt_nd, convert=True):\n KL -= 0.5 * tf.reduce_sum(tf.log(tf.matrix_diag_part(q_sqrt_nd) ** 2))\n\n KL += tf.reduce_sum(tf.log(tf.matrix_diag_part(self.Lu[nd]))) * self.dim_per_out\n KL += 0.5 * tf.reduce_sum(\n tf.square(tf.matrix_triangular_solve(self.Lu_tiled_lst[nd], q_sqrt_nd, lower=True)))\n q_mu_nd = self.q_mu[:, nd * self.dim_per_out:(nd + 1) * self.dim_per_out]\n Kinv_m_nd = tf.cholesky_solve(self.Lu[nd], q_mu_nd)\n KL += 0.5 * tf.reduce_sum(q_mu_nd * Kinv_m_nd)\n\n return KL","def _create_variables(self):\n\n \n with tf.name_scope(\"variable\"):\n if self.reg_type == 'L2':\n regularizer = tf.contrib.layers.l2_regularizer(scale=self.reg_scale)\n else:\n regularizer = tf.contrib.layers.l1_regularizer(scale=self.reg_scale)\n \n self.dim_lst = [self.dim_inputs] + self.dim_hidden_lst + [self.number_structures]\n print(self.dim_lst)\n \n self.W_lst = []\n self.b_lst = []\n for i in range(len(self.dim_lst)-1):\n self.W_lst.append(tf.get_variable(\n \"W{}\".format(i+1),\n shape=[self.dim_lst[i], self.dim_lst[i+1]],\n initializer=tf.contrib.layers.xavier_initializer(),\n regularizer=regularizer)\n )\n # not output layer, has bias term\n if i < len(self.dim_lst) - 2:\n self.b_lst.append(tf.get_variable(\"b{}\".format(i+1), shape=[self.dim_lst[i+1]]))","def noam_norm(x, epsilon=1.0, name=None):\n with tf.name_scope(name, default_name=\"noam_norm\", values=[x]):\n shape = x.get_shape()\n ndims = len(shape)\n result = tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon)*tf.sqrt(tf.to_float(shape[-1]))\n return result","def _tf2_ ( self , *args ) :\n ##\n if not hasattr ( self , '_wo2' ) : self._wo2 = _WO2_ ( self )\n if not self._wo2 : self._wo2 = _WO2_ ( self )\n ## \n _wo = self._wo2\n fun = ROOT.TF2 ( funID () , _wo , *args )\n fun.SetNpx ( 100 ) \n fun.SetNpy ( 100 ) \n #\n return fun","def setUp(self):\n np.random.seed(1337)\n self.w0_val = np.random.rand(2, 3)\n self.w1_val = np.random.rand(3, 5)\n self.w2_val = np.random.rand(10, 3)\n self.w3_val = np.random.rand(10, 5)\n self.w4_val = np.random.rand(10, 5)\n self.w5_val = np.linspace(-7, 7, 200)\n # self.w5_val = np.random.rand(15)\n self.w6_val = np.random.rand(2, 3, 5, 7)\n\n self.w3_val /= np.expand_dims(np.sum(self.w3_val, axis=1), axis=1) # w3 is a probability distribution\n\n self.tf_w0 = tf.constant(self.w0_val, dtype=tf.float64)\n self.tf_w1 = tf.constant(self.w1_val, dtype=tf.float64)\n self.tf_w2 = tf.constant(self.w2_val, dtype=tf.float64)\n self.tf_w3 = tf.constant(self.w3_val, dtype=tf.float64)\n self.tf_w4 = tf.constant(self.w4_val, dtype=tf.float64)\n self.tf_w5 = tf.constant(self.w5_val, dtype=tf.float64)\n self.tf_w6 = tf.constant(self.w6_val, dtype=tf.float64)\n\n self.my_w0 = ad.Variable(self.w0_val, name=\"w0_val\")\n self.my_w1 = ad.Variable(self.w1_val, name=\"w1_val\")\n self.my_w2 = ad.Variable(self.w2_val, name=\"w2_val\")\n self.my_w3 = ad.Variable(self.w3_val, name=\"w3_val\")\n self.my_w4 = ad.Variable(self.w4_val, name=\"w4_val\")\n self.my_w5 = ad.Variable(self.w5_val, name=\"w5_val\")\n self.my_w6 = ad.Variable(self.w6_val, name=\"w6_val\")","def calc_alpha(epsilon): \n return float(0.5 * np.log((1-epsilon)/epsilon))","def makeenv(self):\n eps=np.ones((self.nx,self.ny))*const.epsilon_0\n mu=np.ones((self.nx,self.ny))*const.mu_0\n\n eps[:20,:] *= self.q #adself.ds a space of higher permittivity \n eps[-20:,:] *= self.q #adself.ds a space of higher permittivity \n eps[:,:20] *= self.q #adself.ds a space of higher permittivity \n eps[:,-20:] *= self.q #adself.ds a space of higher permittivity \n #mu[:20,:] /= self.q #adself.ds a space of higher permittivity \n #mu[-20:,:] /= self.q #adself.ds a space of higher permittivity \n #mu[:,:20] /= self.q #adself.ds a space of higher permittivity \n #mu[:,-20:] /= self.q #adself.ds a space of higher permittivity \n\n return eps, mu","def gelu_new(x):\n cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n return x * cdf","def __create_tf_matrix(alpha, a, d, q):\n\n tf_matrix = Matrix([[cos(q), -sin(q), 0., a],\n [sin(q) * cos(alpha), cos(q) * cos(alpha), -sin(alpha), -sin(alpha) * d],\n [sin(q) * sin(alpha), cos(q) * sin(alpha), cos(alpha), cos(alpha) * d],\n [0., 0., 0., 1.]])\n return tf_matrix","def __init__(self,ls,activations = [tf.nn.tanh, tf.nn.tanh, None], sess = None, RL = False, lr = 1e-2):\n self.ls = ls\n if sess == None:\n self.sess = self.tf_reset()\n else:\n self.sess = sess\n self.activations = activations\n self.input_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[0]]) # batch-size by state size\n self.output_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[-1]]) # action space size\n self.W_dict = {}\n self.b_dict = {}\n for i in range(len(ls)-1):\n self.W_dict[i] = tf.get_variable(name='W'+str(i), shape=[ls[i], ls[i+1]], initializer=tf.contrib.layers.xavier_initializer())\n self.b_dict[i] = tf.get_variable(name='b'+str(i), shape=[ls[i+1]], initializer=tf.constant_initializer(0.))\n\n\n self.layer = self.input_ph\n print(tf.shape(self.layer))\n\n\n for i in range(len(self.activations)):\n self.layer = tf.matmul(self.layer, self.W_dict[i]) + self.b_dict[i]\n print(tf.shape(self.layer))\n if self.activations[i] is not None:\n self.layer = self.activations[i](self.layer)\n self.output_pred = self.layer\n\n if RL == True: \n with tf.name_scope('reward_holder'):\n self.reward_holder = tf.placeholder(shape=[None],dtype=tf.float32)\n \n with tf.name_scope('get_resp_outs'):\n self.action_holder = tf.placeholder(shape=[None],dtype=tf.int32, name = 'action_holder')\n \n self.indexes = tf.range(0, tf.shape(self.output_pred)[0]) * tf.shape(self.output_pred)[1] + self.action_holder\n\n self.responsible_outputs = tf.gather(tf.reshape(self.output_pred, [-1]), self.indexes, name = 'responsible_outputs')\n # out of the output vector, this will pull out the indexes\n # But i still don't understand indexes.\n\n # i feel like instead of going thru all of this, you could have just saved the actual outputs. I think I'll try that.\n # then for responsible outputs, you'd do tf.gather(outputs, action_holder) oh maybe it's not different than this. \n # Maybe that's exactly what they're doing, bc action_holder is a scaler number. IDK.\n with tf.name_scope('loss'):\n self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder) #becuase reward_holder value \n # doesn't directly change as you change the Weights, this is equivalent to multiplying the gradient by the reward.\n # when you take the gradient, you're solving for d(log*A)/dW = d(log_p)/dW * d(log_p*A)/d(log_p) = A*d(log_p)/dW. so it's equivalent to mult gradient\n # by the reward function\n tvars = tf.trainable_variables()\n\n with tf.name_scope('update'):\n # self.train_step = tf.train.RMSPropOptimizer(learning_rate = lr, decay = 0.99).minimize(self.loss)\n self.train_step = tf.train.AdamOptimizer().minimize(self.loss)\n self.init = tf.global_variables_initializer()","def rt_layers(lam, T, T_0, tau):\n B_lam = planck_w(lam, T)\n B_surf = planck_w(lam, T_0)\n return B_surf * np.exp(-tau) + B_lam * (1 - np.exp(-tau))","def _redef_via_predef_eqn(self):\r\n time = self.current_T # + self.d_T\r\n\r\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\r\n (self.convect_coeff) \r\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\r\n (self.density * self.heat_capacity)\r\n\r\n # Source term.\r\n def F_func(elem, eta):\r\n x = elem.local_to_global(eta)\r\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\r\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\r\n return elem.funcs(eta) * F\r\n\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func,\r\n self.node_map,\r\n gauss_mult=2) # Use double gp_1D\r\n\r\n # Boundary term.\r\n def f_func(elem, eta):\r\n n = elem.guess_normal_vector_global(eta)\r\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n x = elem.local_to_global(eta)\r\n # Evaluate our boundary term.\r\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\r\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\r\n return elem.funcs(eta) * f\r\n\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func,\r\n self.node_map,\r\n gauss_mult=2)","def __init__(self, epsilon=1e-14):\n self.epsilon = epsilon","def __init__(self, alpha=80, beta=13, gamma=3, spatial_ker_weight=3, bilateral_ker_weight=10):\n self.alpha = alpha\n self.beta = beta\n self.gamma = gamma\n self.spatial_ker_weight = spatial_ker_weight\n self.bilateral_ker_weight = bilateral_ker_weight","def expDecay(L, N0, tf):\n\n\treturn N0*math.exp(-L*tf)","def __init__(self, alpha=1.0, epsilon=0.05, gamma=0.8, numTraining = 10):\n self.alpha = float(alpha)\n self.epsilon = float(epsilon)\n self.discount = float(gamma)\n self.numTraining = int(numTraining)","def model_initializer():\n model = tf.keras.models.Sequential()\n model.add(tf.keras.layers.Flatten())\n # model.add(tf.keras.layers.Dense(128, activation=tf.nn.elu))\n model.add(tf.keras.layers.Dense(64, activation=tf.nn.elu))\n model.add(tf.keras.layers.Dense(32, activation=tf.nn.elu))\n model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n\n\n model.compile(optimizer='rmsprop',\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n return model","def lrn(x, name, phase=True):\n # return tf.nn.local_response_normalization(x, depth_radius=radius,\n # alpha=alpha, beta=beta,\n # bias=bias, name=name)\n return tf.contrib.layers.batch_norm(x,\n center=True, scale=True,\n is_training=phase,\n name=name)","def Lattice_Theory_Algebraic(N,Kappa,d) :\n\t\n\tMu2 = (1.0/6.0)*( (math.pi/(N**(1.0/d)) )**2 )*( ( Kappa + 1.0)**( (d + 2.0)/float(d) ) )\n\treturn Mu2","def planck_w(lam, T):\n return ((2*h*c**2)/(lam**5))*(1./(np.exp((h*c)/(lam*k*T))-1))","def kl_latent_space(network, *args):\n\n z, log_det_J = network(*args)\n loss = tf.reduce_mean(0.5 * tf.square(tf.norm(z, axis=-1)) - log_det_J)\n return loss","def l2_norm(input_x, epsilon=1e-12):\n input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)\n return input_x_norm","def build_model():\n with tf.name_scope('placeholders'):\n real_data_int = tf.placeholder(tf.int32, shape=[None, picture_size])\n x_true = 2 * ((tf.cast(real_data_int, tf.float32) / 255.) - .5)\n z = tf.placeholder(tf.float32, [None, input_dim])\n if use_JL:\n JL = tf.placeholder(tf.float32, [d_last_layer_size, JL_dim])\n P_non_normalized = tf.placeholder(tf.float32, [JL_dim, n_projections])\n P_non_normalized_SWD = tf.placeholder(tf.float32, [picture_size, n_projections])\n else:\n JL = None\n P_non_normalized = tf.placeholder(tf.float32, [d_last_layer_size, n_projections])\n P_non_normalized_SWD = tf.placeholder(tf.float32, [picture_size, n_projections])\n\n x_generated = generator(z, n_features_first=n_features_first_g,\n n_features_reduction_factor=n_features_reduction_factor, min_features=64,\n BN=BN, power=power, extra_layer=extra_layer_g,\n init_method=init_method, n_features_image=n_features_image)\n\n d_pred_true, d_last_true = discriminator(x_true, reuse=False, n_features_last=n_features_last_d,\n n_features_increase_factor=n_features_reduction_factor,\n min_features=min_features, d_BN=d_BN, power=power,\n n_features_image=n_features_image, init_method=init_method)\n d_pred_gen, d_last_gen = discriminator(x_generated, reuse=True, n_features_last=n_features_last_d,\n n_features_increase_factor=n_features_reduction_factor,\n min_features=min_features, d_BN=d_BN, power=power,\n n_features_image=n_features_image, init_method=init_method)\n\n # define generator loss (big part taken from SWG)\n with tf.name_scope('g_loss'):\n # apply the Johnson-Lindenstrauss map, if wanted, to the flattened array\n if use_JL:\n JL_true = tf.matmul(d_last_true, JL)\n JL_gen = tf.matmul(d_last_gen, JL)\n else:\n JL_true = d_last_true\n JL_gen = d_last_gen\n\n # next project the samples (images). After being transposed, we have tensors\n # of the format: [[projected_image1_proj1, projected_image2_proj1, ...],\n # [projected_image1_proj2, projected_image2_proj2, ...],...]\n # Each row has the projections along one direction. This makes it easier for the sorting that follows.\n # first normalize the random normal vectors to lie in the sphere\n P = tf.nn.l2_normalize(P_non_normalized, axis=0)\n\n projected_true = tf.transpose(tf.matmul(JL_true, P))\n projected_fake = tf.transpose(tf.matmul(JL_gen, P))\n\n sorted_true, true_indices = tf.nn.top_k(input=projected_true, k=batch_size)\n sorted_fake, fake_indices = tf.nn.top_k(input=projected_fake, k=batch_size)\n\n # For faster gradient computation, we do not use sorted_fake to compute\n # loss. Instead we re-order the sorted_true so that the samples from the\n # true distribution go to the correct sample from the fake distribution.\n\n # It is less expensive (memory-wise) to rearrange arrays in TF.\n # Flatten the sorted_true from dim [n_projections, batch_size].\n flat_true = tf.reshape(sorted_true, [-1])\n\n # Modify the indices to reflect this transition to an array.\n # new index = row + index\n rows = np.asarray([batch_size * np.floor(i * 1.0 / batch_size) for i in range(n_projections * batch_size)])\n rows = rows.astype(np.int32)\n flat_idx = tf.reshape(fake_indices, [-1, 1]) + np.reshape(rows, [-1, 1])\n\n # The scatter operation takes care of reshaping to the rearranged matrix\n shape = tf.constant([batch_size * n_projections])\n rearranged_true = tf.reshape(tf.scatter_nd(flat_idx, flat_true, shape), [n_projections, batch_size])\n\n generator_loss = tf.reduce_mean(tf.square(projected_fake - rearranged_true))\n\n # get the sliced Wasserstein distance (SWD) (since SWD and JLSWD are not comparable)\n with tf.name_scope('SWD'):\n P_SWD = tf.nn.l2_normalize(P_non_normalized_SWD, axis=0)\n\n projected_true_SWD = tf.transpose(tf.matmul(x_true, P_SWD))\n projected_fake_SWD = tf.transpose(tf.matmul(x_generated, P_SWD))\n\n sorted_true_SWD, true_indices_SWD = tf.nn.top_k(input=projected_true_SWD, k=batch_size)\n sorted_fake_SWD, fake_indices_SWD = tf.nn.top_k(input=projected_fake_SWD, k=batch_size)\n\n flat_true_SWD = tf.reshape(sorted_true_SWD, [-1])\n flat_idx_SWD = tf.reshape(fake_indices_SWD, [-1, 1]) + np.reshape(rows, [-1, 1])\n\n rearranged_true_SWD = tf.reshape(tf.scatter_nd(flat_idx_SWD, flat_true_SWD, shape),\n [n_projections, batch_size])\n\n SWD = tf.reduce_mean(tf.square(projected_fake_SWD - rearranged_true_SWD))\n\n # define the discriminator loss\n with tf.name_scope('d_loss'):\n d_true_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_pred_true), logits=d_pred_true)\n d_fake_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_pred_gen), logits=d_pred_gen)\n discriminator_loss = tf.reduce_mean(d_true_loss + d_fake_loss)\n\n with tf.name_scope('g_optimizer'):\n generator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')\n g_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\n g_train = g_optimizer.minimize(generator_loss, var_list=generator_vars)\n\n with tf.name_scope('d_optimizer'):\n discriminator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')\n d_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\n d_train = d_optimizer.minimize(discriminator_loss, var_list=discriminator_vars)\n\n return real_data_int, z, x_generated, JL, P_non_normalized, P_non_normalized_SWD, SWD, g_train, d_train","def __init__(self, eps: float = 1e-5):\n if eps <= 0.0:\n raise ValueError(\"The epsilon value must be positive\")\n super().__init__()\n self.register_buffer('eps', torch.tensor(eps))","def noam_norm(x, epsilon=1.0, name=None):\n with tf.name_scope(name, default_name=\"noam_norm\", values=[x]):\n shape = x.get_shape()\n ndims = len(shape)\n return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(\n tf.to_float(shape[-1])))","def SCEC_LOH_1():\n\n #Initialize CrustModel\n model = CrustModel(2)\n\n #Slow layer\n vp=4.000\n vs=2.000\n rho=2.600\n Qa=10000.\n Qb=10000.\n thickness = 1.0\n\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\n\n #Halfspace\n vp=6.000\n vs=3.464\n rho=2.700\n Qa=10000.\n Qb=10000.\n thickness = 0 #Infinite thickness!\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\n\n return model","def __init__(self, img_input, p=6.):\n super(LPNorm, self).__init__()\n if p < 1:\n raise ValueError('p value should range between [1, inf)')\n self.name = \"L-{} Norm Loss\".format(p)\n self.p = p\n self.img = img_input","def lnlike(self, block):\n ##### Extract from datablock\n self.cosmology = {'Omega_m': block.get_double('cosmological_parameters', 'Omega_m'),\n 'Omega_l': block.get_double('cosmological_parameters', 'Omega_lambda'),\n 'Omega_b': block.get_double('cosmological_parameters', 'Omega_b'),\n 'h': block.get_double('cosmological_parameters', 'hubble')/100,\n 'ns': block.get_double('cosmological_parameters', 'n_s'),\n 'w0': block.get_double('cosmological_parameters', 'w'),\n 'wa': block.get_double('cosmological_parameters', 'wa'),\n 'sigma8': block.get_double('cosmological_parameters', 'sigma_8')}\n self.scaling = {\n # SZ\n 'Asz': block.get_double('mor_parameters', 'Asz'),\n 'Bsz': block.get_double('mor_parameters', 'Bsz'),\n 'Csz': block.get_double('mor_parameters', 'Csz'),\n 'Dsz': block.get_double('mor_parameters', 'Dsz'),\n # X-ray\n 'Ax': block.get_double('mor_parameters', 'Ax'),\n 'Bx': block.get_double('mor_parameters', 'Bx'),\n 'Cx': block.get_double('mor_parameters', 'Cx'),\n 'Dx': block.get_double('mor_parameters', 'Dx'),\n 'dlnMg_dlnr': block.get_double('mor_parameters', 'dlnMg_dlnr'),\n # WL\n 'WLbias': block.get_double('mor_parameters', 'WLbias'),\n 'WLscatter': block.get_double('mor_parameters', 'WLscatter'),\n 'HSTbias': block.get_double('mor_parameters', 'HSTbias'),\n 'HSTscatterLSS': block.get_double('mor_parameters', 'HSTscatterLSS'),\n 'MegacamBias': block.get_double('mor_parameters', 'MegacamBias'),\n 'MegacamScatterLSS': block.get_double('mor_parameters', 'MegacamScatterLSS'),\n # Correlation coefficients\n 'rhoSZWL': block.get_double('mor_parameters', 'rhoSZWL'),\n 'rhoWLX': block.get_double('mor_parameters', 'rhoWLX'),\n 'rhoSZX': block.get_double('mor_parameters', 'rhoSZX'),\n }\n # Halo mass function\n self.HMF = {'M_arr': block.get_double_array_1d('HMF', 'M_arr'),\n 'z_arr': block.get_double_array_1d('HMF', 'z_arr'),\n 'dNdlnM': block.get_double_array_nd('HMF', 'dNdlnM')}\n self.HMF['len_z'] = len(self.HMF['z_arr'])\n\n ##### Setup stuff for WL\n if self.todo['WL']:\n # Set bias and scatter for Megacam from sim calibration\n # and nuissance parameters\n self.WL.set_scaling(self.scaling)\n # Precompute array of angular diameter distances\n self.WL.get_dAs(self.cosmology)\n\n ##### Populate and check observable covariance matrices\n self.covmat = {'invertible': True}\n if not observablecovmat.set_covmats(self.todo, self.scaling, self.covmat):\n self.covmat['invertible'] = False\n return -np.inf\n\n ##### Set up interpolation for HMF\n HMF_in = self.HMF['dNdlnM'][1:,:]\n if np.any(HMF_in==0):\n HMF_in[np.where(HMF_in==0)] = np.nextafter(0, 1)\n self.HMF_interp = RectBivariateSpline(np.log(self.HMF['z_arr'][1:]), np.log(self.HMF['M_arr']), np.log(HMF_in))\n\n ##### Initialize mass-concentration relation class (for WL)\n if self.todo['WL']:\n self.MCrel = Mconversion_concentration.ConcentrationConversion(self.mcType, self.cosmology)\n\n ##### Compute interpolation table for M500-M200\n if self.todo['WL']:\n z_arr = np.linspace(.1, 2, 20)\n M500 = np.logspace(np.log10(self.HMF['M_arr'][0]), np.log10(self.HMF['M_arr'][-1]), 20)\n M200 = np.array([np.array([self.MCrel.MDelta_to_M200(m, 500., z) for m in M500]) for z in z_arr])\n self.lnM500_to_lnM200 = RectBivariateSpline(z_arr, np.log(M500), np.log(M200))\n\n ##### Evaluate the individual likelihoods\n len_data = len(self.catalog['SPT_ID'])\n\n if self.NPROC==0:\n # Iterate through cluster list\n likelihoods = np.array([self.clusterlike(i) for i in range(len_data)])\n else:\n # Launch a multiprocessing pool and get the likelihoods\n pool = Pool(processes=self.NPROC)\n argin = zip([self]*len_data, range(len_data))\n likelihoods = pool.map(unwrap_self_f, argin)\n pool.close()\n\n # If likelihood computation failed it returned 0\n if np.count_nonzero(likelihoods)<len_data:\n return -np.inf\n\n lnlike = np.sum(np.log(likelihoods))\n\n return lnlike","def build_reparam_kl_loss_and_gradients(inference, var_list):\n \n p_log_lik = [0.0] * inference.n_samples\n base_scope = tf.get_default_graph().unique_name(\"inference\") + '/'\n for s in range(inference.n_samples):\n # Form dictionary in order to replace conditioning on prior or\n # observed variable with conditioning on a specific value.\n scope = base_scope + tf.get_default_graph().unique_name(\"sample\")\n dict_swap = {}\n for x, qx in six.iteritems(inference.data):\n if isinstance(x, RandomVariable):\n if isinstance(qx, RandomVariable):\n qx_copy = copy(qx, scope=scope)\n dict_swap[x] = qx_copy.value()\n else:\n dict_swap[x] = qx\n\n for z, qz in six.iteritems(inference.latent_vars):\n # Copy q(z) to obtain new set of posterior samples.\n qz_copy = copy(qz, scope=scope)\n dict_swap[z] = qz_copy.value()\n\n for x in six.iterkeys(inference.data):\n if isinstance(x, RandomVariable):\n x_copy = copy(x, dict_swap, scope=scope)\n p_log_lik[s] += tf.reduce_sum(\n inference.scale.get(x, 1.0) * x_copy.log_prob(dict_swap[x]))\n\n p_log_lik = tf.reduce_mean(p_log_lik)\n\n kl_penalty = tf.reduce_sum([\n tf.reduce_sum(inference.kl_scaling.get(z, 1.0) * kl_divergence(qz, z))\n for z, qz in six.iteritems(inference.latent_vars)])\n\n if inference.logging:\n tf.summary.scalar(\"loss/p_log_lik\", p_log_lik,\n collections=[inference._summary_key])\n tf.summary.scalar(\"loss/kl_penalty\", kl_penalty,\n collections=[inference._summary_key])\n\n loss = -(p_log_lik - kl_penalty)\n grads = tf.gradients(loss, var_list)\n grads_and_vars = list(zip(grads, var_list))\n return loss, grads_and_vars","def __init__(self, alpha=1.0, epsilon=0.05, gamma=0.8, numTraining=10):\n self.alpha = float(alpha)\n self.epsilon = float(epsilon)\n self.discount = float(gamma)\n self.numTraining = int(numTraining)","def __init__(self, k=3, min_iter=50, max_iter=1000, learn_rate=1e-7,\n regularization=0.5, convergence_tol=0.001, use_pca=True,\n verbose=False, preprocessor=None):\n self.k = k\n self.min_iter = min_iter\n self.max_iter = max_iter\n self.learn_rate = learn_rate\n self.regularization = regularization\n self.convergence_tol = convergence_tol\n self.use_pca = use_pca\n self.verbose = verbose\n super(_base_LMNN, self).__init__(preprocessor)","def build_model(nx, layers, activations, lambtha, keep_prob):\n λ = lambtha\n\n # create model\n a_model = K.Sequential()\n n_layers = len(layers)\n regularizer = K.regularizers.l2(λ)\n\n for i in range(n_layers):\n # Adds a densely-connected layer with layer[i] units to the model:\n a_model.add(K.layers.Dense(\n units=layers[i],\n input_dim=nx,\n kernel_regularizer=regularizer,\n activation=activations[i],\n )\n )\n # To avoid creation of:\n # Layer (type) Output Shape Param #\n # dropout_2 (Dropout) (None, 10) 0\n if i < n_layers - 1:\n a_model.add(K.layers.Dropout(1 - keep_prob))\n return a_model","def gelu(input_tensor):\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\n return input_tensor * cdf","def gelu(input_tensor):\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\n return input_tensor * cdf","def log(x, eps=1e-7, name=None):\n return tf.log(x + eps, name=name)","def __init__(self, hidden_size, eps=1e-12):\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.variance_epsilon = eps","def build(self): \n self.computation_graph = tf.Graph()\n with self.computation_graph.as_default():\n\n self.factorization_layer = Factorization(self.args, self.vocab_size)\n self.regularizer_layer = Regularization(self.args)\n\n self.gamma = tf.placeholder(\"float\")\n self.loss = self.factorization_layer()\n\n self.batch = tf.Variable(0)\n self.step = tf.placeholder(\"float\")\n \n self.learning_rate_new = tf.train.polynomial_decay(self.args.initial_learning_rate,\n self.batch,\n self.true_step_size,\n self.args.minimal_learning_rate,\n self.args.annealing_factor)\n \n self.train_op = tf.train.AdamOptimizer(self.learning_rate_new).minimize(self.loss, global_step = self.batch)\n \n self.init = tf.global_variables_initializer()\n\n self.weights = overlap_generator(self.args, self.graph)","def decision_function(alphas, target, kernel, X_train, x_test, b):\n\n result = (alphas * target) @ kernel(X_train, x_test) - b\n return result","def build(self): \n self.computation_graph = tf.Graph()\n with self.computation_graph.as_default():\n\n self.factorization_layer = Factorization(self.args, self.vocab_size)\n self.regularizer_layer = Regularization(self.args)\n\n self.gamma = tf.placeholder(\"float\")\n self.loss = self.factorization_layer()+self.regularizer_layer(self.factorization_layer)\n\n self.batch = tf.Variable(0)\n self.step = tf.placeholder(\"float\")\n \n self.learning_rate_new = tf.train.polynomial_decay(self.args.initial_learning_rate,\n self.batch,\n self.true_step_size,\n self.args.minimal_learning_rate,\n self.args.annealing_factor)\n \n self.train_op = tf.train.AdamOptimizer(self.learning_rate_new).minimize(self.loss, global_step = self.batch)\n \n self.init = tf.global_variables_initializer()\n\n self.weights = overlap_generator(self.args, self.graph)","def beta_gen_lasso(p):\n cardi = 0.005\n return np.array([0]*int(p-int(cardi*p)) + [1]*int(cardi*p))","def az_kinetic_model(nt, tf, ka_rates, kb_rates, normed=True, X0=None):\n def dx(x, t, A, B):\n return np.dot(A, x) + B\n \n if type(nt) == list:\n nt = transform(nt)\n \n A = network_matrix(nt, ka_rates)\n B = np.dot(network_matrix(nt, kb_rates), np.ones(A.shape[0]))\n if X0 == None:\n X0 = np.zeros(A.shape[0])\n \n X = odeint(lambda x, t: dx(x, t, A, B), X0, np.linspace(0, tf, 1000))\n if normed:\n return az.norm_array(X[-1])\n else:\n return X[-1]","def ft_ll(m, t, y, x, x_kernel, x_kernel_params, t_kernel, t_kernel_params):\n # Take copies of everything - this is a function\n m = copy.deepcopy(m)\n t = copy.deepcopy(t)\n y = copy.deepcopy(y)\n x = copy.deepcopy(x)\n\n K_x = x_kernel(x, x, **x_kernel_params)\n N = len(y)\n\n lambd = np.zeros((N, 1))\n gamma = np.zeros((N, 1))\n\n K_t = [None] * N\n\n for n in range(N):\n K_t[n] = t_kernel(t[n], t[n], **t_kernel_params)\n lambd[n] = np.dot(np.ones((1, len(t[n]))), np.linalg.solve(K_t[n], np.ones((len(t[n]), 1))))\n # Making sure y[n] is a column vector\n y[n] = np.array(y[n], ndmin=2)\n if y[n].shape[0] == 1:\n y[n] = y[n].T\n gamma[n] = np.dot(np.ones((1, len(t[n]))), np.linalg.solve(K_t[n], y[n] - m[n] * np.ones(y[n].shape)))\n\n Lambd = np.diag(lambd.ravel())\n\n ll = 0\n\n # Terms relating to individual curves\n for n in range(N):\n ll += - 0.5 * np.dot((y[n] - m[n] * np.ones(y[n].shape)).T,\n np.linalg.solve(K_t[n], y[n] - m[n] * np.ones(y[n].shape)))\n ll += - 0.5 * np.log(np.linalg.det(K_t[n]))\n\n # Terms relating to K_x\n ll += + 0.5 * np.dot(gamma.T, np.linalg.solve(np.linalg.inv(K_x) + Lambd, gamma))\n ll += - 0.5 * np.log(np.linalg.det(np.linalg.inv(K_x) + Lambd))\n ll += - 0.5 * np.log(np.linalg.det(K_x))\n\n return ll","def __init__(self,ls,activations = [tf.nn.tanh, tf.nn.tanh, None], sess = None, RL = False, lr = 1e-2, reg_scale = 0.1):\n self.ls = ls\n if sess == None:\n self.sess = self.tf_reset()\n else:\n self.sess = sess\n self.activations = activations\n self.input_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[0]], name = 'msh_input_placeholder') # batch-size by state size\n self.output_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[-1]]) # action space size\n\n self.fc1 = tf.contrib.layers.fully_connected(self.input_ph, ls[1],\n weights_regularizer=tf.contrib.layers.l2_regularizer(reg_scale),\n activation_fn=activations[0])\n\n self.fc2 = tf.contrib.layers.fully_connected(self.fc1, ls[2],\n weights_regularizer=tf.contrib.layers.l2_regularizer(reg_scale),\n activation_fn=activations[1])\n\n self.output_pred = tf.contrib.layers.fully_connected(self.fc2, ls[-1],\n weights_regularizer=tf.contrib.layers.l2_regularizer(reg_scale),\n activation_fn=activations[-1])\n\n\n # self.W_dict = {}\n # self.b_dict = {}\n # for i in range(len(ls)-1):\n # self.W_dict[i] = tf.get_variable(name='W'+str(i), shape=[ls[i], ls[i+1]], initializer=tf.contrib.layers.xavier_initializer())\n # self.b_dict[i] = tf.get_variable(name='b'+str(i), shape=[ls[i+1]], initializer=tf.constant_initializer(0.))\n\n\n # self.layer = self.input_ph\n # print(tf.shape(self.layer))\n\n\n # for i in range(len(self.activations)):\n # self.layer = tf.matmul(self.layer, self.W_dict[i]) + self.b_dict[i]\n # print(tf.shape(self.layer))\n # if self.activations[i] is not None:\n # self.layer = self.activations[i](self.layer)\n # self.output_pred = self.layer\n\n if RL == True: \n with tf.name_scope('reward_holder'):\n self.reward_holder = tf.placeholder(shape=[None],dtype=tf.float32)\n \n with tf.name_scope('get_resp_outs'):\n self.action_holder = tf.placeholder(shape=[None],dtype=tf.int32, name = 'action_holder')\n \n self.indexes = tf.range(0, tf.shape(self.output_pred)[0]) * tf.shape(self.output_pred)[1] + self.action_holder\n\n self.responsible_outputs = tf.gather(tf.reshape(self.output_pred, [-1]), self.indexes, name = 'responsible_outputs')\n # out of the output vector, this will pull out the indexes\n # But i still don't understand indexes.\n\n # i feel like instead of going thru all of this, you could have just saved the actual outputs. I think I'll try that.\n # then for responsible outputs, you'd do tf.gather(outputs, action_holder) oh maybe it's not different than this. \n # Maybe that's exactly what they're doing, bc action_holder is a scaler number. IDK.\n with tf.name_scope('loss'):\n self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder) #becuase reward_holder value \n # doesn't directly change as you change the Weights, this is equivalent to multiplying the gradient by the reward.\n # when you take the gradient, you're solving for d(log*A)/dW = d(log_p)/dW * d(log_p*A)/d(log_p) = A*d(log_p)/dW. so it's equivalent to mult gradient\n # by the reward function\n tvars = tf.trainable_variables()\n\n with tf.name_scope('update'):\n # self.train_step = tf.train.RMSPropOptimizer(learning_rate = lr, decay = 0.99).minimize(self.loss)\n self.train_step = tf.train.AdamOptimizer().minimize(self.loss)\n self.init = tf.global_variables_initializer()","def weiner_tf(H, K):\r\n\r\n W = (1 / H) * ((np.conjugate(H) * H) / ((np.conjugate(H) * H) + K))\r\n return W","def test_2_layer():\r\n # angular frequency in radians * THz\r\n w = 100 * nu.THz\r\n # Relative permittivity of metal and dielectric\r\n em = -4.56 + 0.12j\r\n ed = 1.23 + 0.01j\r\n ex_list = ez_list = [ed, em]\r\n # Relative permeabilities\r\n mu_list = [1,1]\r\n # Dictionary of input parameters\r\n input_params = {'w': w, 'd_list': [inf,inf], 'ex_list': ex_list,\r\n 'ez_list': ez_list, 'mu_list': mu_list}\r\n \r\n # Calculate the theoretical kx\r\n theo_kx = (w / nu.c0) * cmath.sqrt((em * ed) / (em + ed))\r\n if theo_kx.imag < 0:\r\n theo_kx *= -1\r\n print('Theoretical kx:',\r\n '(%.7g+%.7gj) rad/um' % (theo_kx.real / nu.um**-1, theo_kx.imag / nu.um**-1))\r\n \r\n # If I use the theoretical kx value, the mode should be correct and\r\n # all my tests should pass.\r\n params = deepcopy(input_params)\r\n params['kx'] = theo_kx\r\n params = find_all_params_from_kx(params)\r\n kzd, kzm = params['kz_list']\r\n # check that kz_list is correct\r\n assert_floats_are_equal(kzd**2, (w**2 / nu.c0**2) * ed**2 / (em + ed))\r\n assert_floats_are_equal(kzm**2, (w**2 / nu.c0**2) * em**2 / (em + ed))\r\n # check that layer_bottom_list is correct\r\n assert params['layer_bottom_list'][0] == -inf\r\n assert params['layer_bottom_list'][1] == 0\r\n # Check that the boundary condition matrix agrees with hand-calculation\r\n bc_mat = bc_matrix(params)\r\n # ...top-left is Ex0down / H0down\r\n assert_floats_are_equal(bc_mat[0,0], -kzd / (w * ed * nu.eps0))\r\n # ...top-right is -Ex1up / H1up\r\n assert_floats_are_equal(bc_mat[0,1], -kzm / (w * em * nu.eps0))\r\n # ...bottom-left is eps0 * Ez0down / H0down\r\n assert_floats_are_equal(bc_mat[1,0], ed * -theo_kx / (w * ed * nu.eps0))\r\n # ...bottom-right is -eps1 * Ez1up / H1up\r\n assert_floats_are_equal(bc_mat[1,1], -em * -theo_kx / (w * em * nu.eps0))\r\n # Check that one of the eigenvalues is almost zero (compared to the size\r\n # of the matrix elements).\r\n eigenvalues = np.linalg.eig(bc_mat)[0]\r\n assert abs(eigenvalues).min() / abs(bc_mat).max() < 1e-6\r\n # Check that the mode passes all tests.\r\n assert check_mode(params, thorough=True) is True\r\n # Check that I can scale the fields and it still passes all tests.\r\n params_scaled = rescale_fields(1.23+4.56j, params)\r\n assert check_mode(params_scaled, thorough=True) is True\r\n \r\n # Now try my kx-finding algorithm, to see if it finds the right value.\r\n kx_list = find_kx(input_params)\r\n print('kx_list:',\r\n ['(%.7g+%.7gj) rad/um' % (kx.real / nu.um**-1, kx.imag / nu.um**-1)\r\n for kx in kx_list])\r\n kx = kx_list[0]\r\n assert_floats_are_equal(theo_kx, kx)\r\n \r\n plot_mode(params)\r\n \r\n print('If you see this message, all the tests succeeded!!')","def genball(npt, ndim, rstate=None):\n # use Barthe2005\n x = rstate.standard_normal(size=(npt, ndim))\n y = rstate.exponential(0.5, size=npt)\n x1 = x / np.sqrt((y + (x**2).sum(axis=1)))[:, None]\n return x1","def _kl_brute_force(a, b, name=None):\n\n def squared_frobenius_norm(x):\n \"\"\"Helper to make KL calculation slightly more readable.\"\"\"\n # http://mathworld.wolfram.com/FrobeniusNorm.html\n # The gradient of KL[p,q] is not defined when p==q. The culprit is\n # tf.norm, i.e., we cannot use the commented out code.\n # return tf.square(tf.norm(x, ord=\"fro\", axis=[-2, -1]))\n return tf.reduce_sum(tf.square(x), axis=[-2, -1])\n\n # TODO(b/35041439): See also b/35040945. Remove this function once LinOp\n # supports something like:\n # A.inverse().solve(B).norm(order='fro', axis=[-1, -2])\n def is_diagonal(x):\n \"\"\"Helper to identify if `LinearOperator` has only a diagonal component.\"\"\"\n return (isinstance(x, tf.linalg.LinearOperatorIdentity) or\n isinstance(x, tf.linalg.LinearOperatorScaledIdentity) or\n isinstance(x, tf.linalg.LinearOperatorDiag))\n\n with tf.name_scope(name or 'kl_mvn'):\n # Calculation is based on:\n # http://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians\n # and,\n # https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm\n # i.e.,\n # If Ca = AA', Cb = BB', then\n # tr[inv(Cb) Ca] = tr[inv(B)' inv(B) A A']\n # = tr[inv(B) A A' inv(B)']\n # = tr[(inv(B) A) (inv(B) A)']\n # = sum_{ij} (inv(B) A)_{ij}**2\n # = ||inv(B) A||_F**2\n # where ||.||_F is the Frobenius norm and the second equality follows from\n # the cyclic permutation property.\n if is_diagonal(a.scale) and is_diagonal(b.scale):\n # Using `stddev` because it handles expansion of Identity cases.\n b_inv_a = (a.stddev() / b.stddev())[..., tf.newaxis]\n else:\n b_inv_a = b.scale.solve(a.scale.to_dense())\n kl_div = (\n b.scale.log_abs_determinant() - a.scale.log_abs_determinant() +\n 0.5 * (-tf.cast(a.scale.domain_dimension_tensor(), a.dtype) +\n squared_frobenius_norm(b_inv_a) + squared_frobenius_norm(\n b.scale.solve((b.mean() - a.mean())[..., tf.newaxis]))))\n tensorshape_util.set_shape(\n kl_div, tf.broadcast_static_shape(a.batch_shape, b.batch_shape))\n return kl_div","def build(self): \n self.computation_graph = tf.Graph()\n with self.computation_graph.as_default():\n\n self.factorization_layer = Factorization(self.args, self.vocab_size)\n self.cluster_layer = Clustering(self.args)\n self.regularizer_layer = Regularization(self.args)\n\n self.gamma = tf.placeholder(\"float\")\n self.loss = self.factorization_layer()+self.gamma*self.cluster_layer(self.factorization_layer)+self.regularizer_layer(self.factorization_layer)\n\n self.batch = tf.Variable(0)\n self.step = tf.placeholder(\"float\")\n \n self.learning_rate_new = tf.train.polynomial_decay(self.args.initial_learning_rate,\n self.batch,\n self.true_step_size,\n self.args.minimal_learning_rate,\n self.args.annealing_factor)\n \n self.train_op = tf.train.AdamOptimizer(self.learning_rate_new).minimize(self.loss, global_step = self.batch)\n \n self.init = tf.global_variables_initializer()\n\n self.weights = overlap_generator(self.args, self.graph)","def build_mlp(input_placeholder, output_size, scope, n_layers, size, activation=tf.tanh, output_activation=None):\n output_placeholder = input_placeholder\n with tf.variable_scope(scope):\n for _ in range(n_layers):\n output_placeholder = tf.layers.dense(output_placeholder, size, activation=activation) # HINT: use tf.layers.dense (specify <input>, <size>, activation=<?>)\n output_placeholder = tf.layers.dense(output_placeholder, output_size, activation=output_activation) # HINT: use tf.layers.dense (specify <input>, <size>, activation=<?>)\n return output_placeholder","def lenet300_classic():\n return LeNet300(dropout=False, nonlinearity=nn.Tanh)"],"string":"[\n \"def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\\n f=lambda y,t : gamma*y*(1-y/K)\\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\\n return Y\",\n \"def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\\n\\n f=lambda y, t : gamma*y\\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\\n return Y\",\n \"def layernorm(w_in):\\n return nn.LayerNorm(w_in, eps=cfg.LN.EPS)\",\n \"def _DefaultLN(self, name):\\n p = self.params\\n return p.layernorm_tpl.Copy().Set(\\n name=name,\\n input_dim=p.model_dim,\\n use_fused_layernorm=p.use_fused_layernorm,\\n fprop_dtype=p.fprop_dtype)\",\n \"def _define_epsilon(n,T,a=1):\\n\\n return np.sqrt(np.log(n)/T)*a\",\n \"def __init__(self, epsilon=1e-5, momentum = 0.9, name=\\\"batch_norm\\\"):\\n \\n with tf.variable_scope(name):\\n self.epsilon = epsilon\\n self.momentum = momentum\\n\\n self.name = name\",\n \"def getWavelet(nf):\\n harr_wav_L = 1 / np.sqrt(2) * np.ones((1, 2))\\n harr_wav_H = 1 / np.sqrt(2) * np.ones((1, 2))\\n harr_wav_H[0, 0] = -1 * harr_wav_H[0, 0]\\n harr_wav_LL = np.transpose(harr_wav_L) * harr_wav_L\\n harr_wav_LH = np.transpose(harr_wav_L) * harr_wav_H\\n harr_wav_HL = np.transpose(harr_wav_H) * harr_wav_L\\n harr_wav_HH = np.transpose(harr_wav_H) * harr_wav_H\\n kLL = makeKernel(harr_wav_LL,nf)\\n kLH = makeKernel(harr_wav_LH,nf)\\n kHL = makeKernel(harr_wav_HL,nf)\\n kHH = makeKernel(harr_wav_HH,nf)\\n kLL = tf.convert_to_tensor(kLL,dtype=tf.float32)\\n kLH = tf.convert_to_tensor(kLH,dtype=tf.float32)\\n kHL = tf.convert_to_tensor(kHL,dtype=tf.float32)\\n kHH = tf.convert_to_tensor(kHH,dtype=tf.float32)\\n return kLL,kLH,kHL,kHH\",\n \"def l2_reg_create_layer(prev, n, activation, lambtha):\\n regulizer = tf.contrib.layers.l2_regularizer(lambtha)\\n init = tf.contrib.layers.variance_scaling_initializer(mode=\\\"FAN_AVG\\\")\\n tensor = tf.layers.Dense(units=n, activation=activation,\\n kernel_initializer=init,\\n kernel_regularizer=regulizer)\\n return tensor(prev)\",\n \"def __init__(self, sess, max_iter=50001, optim='adagrad', learning_rate=1e-2,\\n d_per_iter=1, g_per_iter=2, d_update=True, g_update=True,\\n real_n=1000, real_dim=2, fake_n=1000, z_dim=3, g_out_dim=2,\\n g_layers_depth=5, g_layers_width=None, g_activations=None,\\n d_out_dim=1, d_layers_depth=5, d_layers_width=5,\\n d_activations=None, d_batch_size=25, x_lims=None, y_lims=None,\\n grid_gran=21, grid_n=None, dataset='gaussian', expt='test_low_alpha'):\\n self.sess = sess\\n self.max_iter = max_iter\\n self.optim = optim\\n self.learning_rate = learning_rate\\n\\n self.d_per_iter = d_per_iter \\n self.g_per_iter = g_per_iter\\n self.d_update = d_update\\n self.g_update = not d_update \\n\\n self.real_n = real_n \\n self.real_dim = real_dim \\n self.fake_n = fake_n\\n\\n self.z_dim = z_dim\\n self.g_out_dim = g_out_dim\\n self.g_layers_depth = g_layers_depth\\n self.g_layers_width = [[5]] * (g_layers_depth - 1) + [[g_out_dim]]\\n self.g_activations = [tf.nn.tanh, tf.nn.elu]\\n\\n self.d_out_dim = d_out_dim\\n self.d_layers_depth = d_layers_depth\\n self.d_layers_width = d_layers_width\\n self.d_activations = [tf.nn.tanh, tf.nn.relu]\\n self.d_batch_size = d_batch_size\\n\\n self.x_lims = [-6., 2.]\\n self.y_lims = [-2., 6.]\\n self.grid_gran = grid_gran\\n self.grid_n = grid_gran ** 2\\n self.grid, self.x_grid, self.y_grid = self.make_grid()\\n\\n self.dataset = dataset \\n self.real_points = load_2d_data(dataset, real_n, real_dim)\\n\\n self.expt = expt\\n\\n self.build_model()\",\n \"def create_kernel(\\n self,\\n ) -> pyabc.StochasticKernel:\\n def kernel_fun(x, x_0, t, par) -> float:\\n \\\"\\\"\\\"The kernel function.\\\"\\\"\\\"\\n # the kernel value is computed by amici already\\n return x['llh']\\n\\n # create a kernel from function, returning log-scaled values\\n kernel = pyabc.distance.SimpleFunctionKernel(\\n kernel_fun, ret_scale=pyabc.distance.SCALE_LOG)\\n\\n return kernel\",\n \"def l2_reg_create_layer(prev, n, activation, lambtha):\\n reg = tf.contrib.layers.l2_regularizer(lambtha)\\n init = tf.contrib.layers.variance_scaling_initializer(mode=\\\"FAN_AVG\\\")\\n t = tf.layers.Dense(units=n, activation=activation,\\n kernel_initializer=init,\\n kernel_regularizer=reg,\\n )\\n return t(prev)\",\n \"def __init__(self, dm, h):\\n super().__init__()\\n self.h = h\\n self.dm = dm\\n\\n self.depth = dm // h\\n\\n self.Wq = tf.keras.layers.Dense(dm)\\n self.Wk = tf.keras.layers.Dense(dm)\\n self.Wv = tf.keras.layers.Dense(dm)\\n\\n self.linear = tf.keras.layers.Dense(dm)\",\n \"def ln(inputs, epsilon=1e-5, scope=None):\\n mean, var = tf.nn.moments(inputs, [-1], keep_dims=True)\\n with tf.variable_scope(scope + 'LN'):\\n scale = tf.get_variable('alpha',\\n shape=[inputs.get_shape()[-1]],\\n initializer=tf.constant_initializer(1))\\n shift = tf.get_variable('beta',\\n shape=[inputs.get_shape()[-1]],\\n initializer=tf.constant_initializer(0))\\n LN = scale * (inputs - mean) / tf.sqrt(var + epsilon) + shift\\n\\n return LN\",\n \"def laplacian_tf(P, Q):\\r\\n\\r\\n # Create transfer function H.\\r\\n H = np.zeros((P, Q), dtype=np.complex128)\\r\\n # Create laplacian transfer function.\\r\\n laplacian = np.array([[0, -1, 0], \\r\\n [-1, 4, -1], \\r\\n [0, -1, 0]], dtype=np.int64)\\r\\n # Center laplacian kernel on P x Q rectangle.\\r\\n H[int(np.floor(P/2))-1: int(np.floor(P/2))+2, int(np.floor(Q/2))-1: int(np.floor(Q/2))+2] = laplacian\\r\\n # Calculate laplacian transfer function.\\r\\n H = np.fft.fftshift(np.fft.fft2(H))\\r\\n return H\",\n \"def DKL(p, q,eps=1e-12):\\n return -Hshannon(p,eps=eps) + NLL(p, q,eps=eps)\",\n \"def create_ars_model(odim=10,adim=2,hdims=[128],\\n actv=tf.nn.relu,out_actv=tf.nn.tanh):\\n import tensorflow as tf\\n \\n def mlp(x,hdims=[256,256],actv=tf.nn.relu,out_actv=tf.nn.relu):\\n ki = tf.truncated_normal_initializer(stddev=0.1)\\n for hdim in hdims[:-1]:\\n x = tf.layers.dense(x,units=hdim,activation=actv,kernel_initializer=ki)\\n return tf.layers.dense(x,units=hdims[-1],\\n activation=out_actv,kernel_initializer=ki)\\n def placeholder(dim=None):\\n return tf.placeholder(dtype=tf.float32,shape=(None,dim) if dim else (None,))\\n def placeholders(*args):\\n \\\"\\\"\\\"\\n Usage: a_ph,b_ph,c_ph = placeholders(adim,bdim,None)\\n \\\"\\\"\\\"\\n return [placeholder(dim) for dim in args]\\n def get_vars(scope):\\n return [x for x in tf.compat.v1.global_variables() if scope in x.name]\\n \\n # Have own session\\n config = tf.ConfigProto()\\n config.gpu_options.allow_growth = True\\n sess = tf.Session(config=config)\\n \\n # Placeholders\\n o_ph = placeholder(odim)\\n \\n # Policy \\n with tf.variable_scope('main'):\\n mu = mlp(o_ph,hdims=hdims+[adim],actv=actv,out_actv=out_actv)\\n \\n # Params\\n main_vars = get_vars('main')\\n \\n model = {'o_ph':o_ph,'mu':mu,'main_vars':main_vars}\\n return model, sess\",\n \"def test_lj612(self):\\n x0 = np.array([\\n [ 0.0637, 0.0126, 0.2203],\\n [ 1.0573, -0.2011, 1.2864],\\n [ 2.3928, 1.2209, -0.2230],\\n [-0.6891, 1.6983, 0.0780],\\n [-0.6312, -1.6261, -0.2601]\\n ], dtype=np.float64)\\n\\n x_ph = tf.placeholder(shape=(5, 3), dtype=np.float64)\\n\\n params_np = np.array([3.0, 2.0, 1.0, 1.4], dtype=np.float64)\\n params = tf.convert_to_tensor(params_np)\\n # Ai, Ci\\n param_idxs = np.array([\\n [0, 3],\\n [1, 2],\\n [1, 2],\\n [1, 2],\\n [1, 2]], dtype=np.int32)\\n\\n exclusions = np.array([\\n [0,0,1,0,0],\\n [0,0,0,1,1],\\n [1,0,0,0,0],\\n [0,1,0,0,1],\\n [0,1,0,1,0],\\n ], dtype=np.bool)\\n\\n ef = LJ612Force(params, param_idxs, exclusions)\\n\\n sess = tf.Session()\\n sess.run(tf.initializers.global_variables())\\n\\n num_atoms = 5\\n ref_nrg = 0\\n\\n for i in range(num_atoms):\\n Ai = params_np[param_idxs[i, 0]]\\n Ci = params_np[param_idxs[i, 1]]\\n for j in range(i+1, num_atoms):\\n if not exclusions[i, j]:\\n Aj = params_np[param_idxs[j, 0]]\\n Cj = params_np[param_idxs[j, 1]]\\n dij = np.linalg.norm(x0[i] - x0[j])\\n ref_nrg += np.sqrt(Ai*Aj)/np.power(dij, 12) - np.sqrt(Ci*Cj)/np.power(dij, 6)\\n\\n # (ytz) TODO: add a test for forces, for now we rely on\\n # autograd to be analytically correct.\\n test_nrg_op = ef.energy(x_ph)\\n\\n test_nrg = sess.run(test_nrg_op, feed_dict={x_ph: x0})\\n\\n np.testing.assert_almost_equal(ref_nrg, test_nrg, decimal=15)\\n\\n test_grads_op = ef.gradients(x_ph)\\n test_grads = sess.run(test_grads_op, feed_dict={x_ph: x0})\\n\\n net_force = np.sum(test_grads, axis=0)\\n # this also checks that there are no NaNs\\n np.testing.assert_almost_equal(net_force, [0,0,0], decimal=14)\\n\\n assert not np.any(np.isnan(sess.run(ef.hessians(x_ph), feed_dict={x_ph: x0})))\\n assert not np.any(np.isnan(sess.run(ef.mixed_partials(x_ph), feed_dict={x_ph: x0})))\",\n \"def ln(tensor, scope=None, epsilon=1e-5):\\n assert(len(tensor.get_shape()) == 2)\\n m, v = tf.nn.moments(tensor, [1], keep_dims=True)\\n if not isinstance(scope, str):\\n scope = ''\\n with tf.variable_scope(scope + 'layer_norm'):\\n scale = tf.get_variable('scale',\\n shape=[tensor.get_shape()[1]],\\n initializer=tf.constant_initializer(1))\\n shift = tf.get_variable('shift',\\n shape=[tensor.get_shape()[1]],\\n initializer=tf.constant_initializer(0))\\n LN_initial = (tensor - m) / tf.sqrt(v + epsilon)\\n\\n return LN_initial * scale + shift\",\n \"def create(self):\\n # 1st Layer: Conv -> norm -> ReLu\\n conv1 = self.conv(x=self.X, stride_y=1, stride_x=1, padding='SAME', name='conv1')\\n norm1 = lrn(conv1, 2, 1e-04, 0.75, name='norm1')\\n # Apply relu function\\n relu1 = tf.nn.relu(norm1)\\n\\n # 2st Layer: Conv -> norm -> ReLu\\n conv2 = self.conv(x=relu1, stride_y=1, stride_x=1, padding='SAME', name='conv2')\\n norm2 = lrn(conv2, 2, 1e-04, 0.75, name='norm2')\\n # Apply relu function\\n relu2 = tf.nn.relu(norm2)\\n\\n pool2 = tf.nn.max_pool(relu2, ksize=[1, 3, 3, 1],\\n strides=[1, 2, 2, 1],\\n padding='SAME')\\n\\n # 3st Layer: Conv -> norm -> ReLu\\n conv3 = self.conv(x=pool2, stride_y=1, stride_x=1, padding='SAME', name='conv3')\\n norm3 = lrn(conv3, 2, 1e-04, 0.75, name='norm3')\\n # Apply relu function\\n relu3 = tf.nn.relu(norm3)\\n\\n # 4st Layer: Conv -> norm -> ReLu\\n conv4 = self.conv(x=relu3, stride_y=1, stride_x=1, padding='SAME', name='conv4')\\n norm4 = lrn(conv4, 2, 1e-04, 0.75, name='norm4')\\n # Apply relu function\\n relu4 = tf.nn.relu(norm4)\\n\\n pool4 = tf.nn.max_pool(relu4, ksize=[1, 3, 3, 1],\\n strides=[1, 2, 2, 1],\\n padding='SAME')\\n\\n # 5st Layer: Conv -> norm -> ReLu\\n conv5 = self.conv(x=pool4, stride_y=1, stride_x=1, padding='SAME', name='conv5')\\n norm5 = lrn(conv5, 2, 1e-04, 0.75, name='norm5')\\n # Apply relu function\\n relu5 = tf.nn.relu(norm5)\\n\\n # 6st Layer: Conv -> norm -> ReLu\\n conv6 = self.conv(x=relu5, stride_y=1, stride_x=1, padding='SAME', name='conv6')\\n norm6 = lrn(conv6, 2, 1e-04, 0.75, name='norm6')\\n # Apply relu function\\n relu6 = tf.nn.relu(norm6)\\n\\n pool6 = tf.nn.avg_pool(relu6, ksize=[1, 4, 4, 1],\\n strides=[1, 4, 4, 1],\\n padding='SAME')\\n\\n flattened = tf.reshape(pool6, [-1, 128 * 4])\\n self.fc7 = self.fc(flattened, name='fc7')\",\n \"def sample_gumbel(shape, eps=1e-20):\\n U = tf.random_uniform(shape,minval=0,maxval=1)\\n return -tf.log(-tf.log(U + eps) + eps)\",\n \"def sample_gumbel(shape, eps=1e-20):\\n U = tf.random_uniform(shape,minval=0,maxval=1)\\n return -tf.log(-tf.log(U + eps) + eps)\",\n \"def ResNetLN34(num_classes=10):\\n return ResNet(BasicLNBlock,[3,4,6,3], \\n num_classes=num_classes,\\n norm_type=\\\"LN\\\")\",\n \"def Lotka_Volterra(a, b, c, d, init, t0, tf, eps):\\n\\n \\n Y=meth_epsilon(init, t0, tf, eps,\\\\\\n eq_Lotka_Volterra(a, b, c, d) , step_RK4)\\n return Y\",\n \"def model_gutenberg(r):\\n\\n\\timport numpy as np\\n\\n\\t#- initialisations ----------------------------------------------------------------------------\\n\\n\\t#- depth intervals\\n\\td = np.array([0.0, 19.0, 38.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0])\\n\\td = 6371000.0 - 1000.0 * d\\n\\n\\t#- density\\n\\trho_array = np.array([2.74, 3.00, 3.32, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.41, 3.53, 3.46, 3.48, 3.50, 3.53, 3.58, 3.62, 3.69, 3.82, 4.01, 4.21, 4.40, 4.56, 4.63])\\n\\n\\t#- vp\\n\\tvp_array = np.array([6.14, 6.58, 8.20, 8.17, 8.14, 8.10, 8.07, 8.02, 7.93, 7.85, 7.89, 7.98, 8.10, 8.21, 8.38, 8.62, 8.87, 9.15, 9.45, 9.88, 10.30, 10.71, 11.10, 11.35])\\n\\n\\t#- vs\\n\\tvs_array = np.array([3.55, 3.80, 4.65, 4.62, 4.57, 4.51, 4.46, 4.41, 4.37, 4.35, 4.36, 4.38, 4.42, 4.46, 4.54, 4.68, 4.85, 5.04, 5.21, 5.45, 5.76, 6.03, 6.23, 6.32])\\n\\n\\t#- determine vp, vs and rho for the relevant layer --------------------------------------------\\n\\n\\tfor k in np.arange(len(rho_array)):\\n\\t\\tif (r <= d[k]) & (r > d[k+1]):\\n\\t\\t\\trho = rho_array[k]\\n\\t\\t\\tvpv = vp_array[k]\\n\\t\\t\\tvph = vpv\\n\\t\\t\\tvsv = vs_array[k]\\n\\t\\t\\tvsh = vsv\\n\\t\\t\\teta = 1.0\\n\\t\\t\\tcontinue\\n\\t\\telif r <= 5371000.0:\\n\\t\\t\\trho = 4.63\\n\\t\\t\\tvpv = 11.35\\n\\t\\t\\tvph = vpv\\n\\t\\t\\tvsv = 6.32\\n\\t\\t\\tvsh = vsv\\n\\t\\t\\teta = 1.0\\n\\n\\t#- convert to elastic parameters --------------------------------------------------------------\\n\\n\\trho = 1000.0 * rho\\n\\tvpv = 1000.0 * vpv\\n\\tvph = 1000.0 * vph\\n\\tvsv = 1000.0 * vsv\\n\\tvsh = 1000.0 * vsh\\n\\n\\tA = rho * vph**2\\n\\tC = rho * vpv**2\\n\\tN = rho * vsh**2\\n\\tL = rho * vsv**2\\n\\tF = eta * (A - 2 * L)\\n\\n\\treturn rho, A, C, F, L, N\",\n \"def _create_batchnorm(cls, op, op_t):\\n # first, we init batchnorm node\\n epsilon = 1e-5 # the epsilon value used in singa\\n bn_node = cls._common_singa_tensor_to_onnx_node(op, op_t)\\n bn_node.attribute.extend([\\n helper.make_attribute('momentum', op.handle.factor),\\n helper.make_attribute('epsilon', epsilon),\\n ])\\n # then we add nodes of scal, bias, mean, var\\n nodes = []\\n running_values = {\\\"mean\\\": op.running_mean, \\\"var\\\": op.running_var}\\n for tmp_name, running_value in running_values.items():\\n node_name = op.name + \\\":\\\" + tmp_name\\n bn_node.input.append(node_name)\\n\\n nodes.append(bn_node)\\n return nodes\",\n \"def eg3(N_train=1000, N_test=500, depend_ratio_train=0.8, depend_ratio_test=0.2, feature_num=10, stable_ratio=0.4):\\n\\n def eg3_kernel(n, p, stable_ratio=0.4, depend_ratio=0.8):\\n p_stable = int(p * stable_ratio)\\n p_noise = p - p_stable\\n stable_feature = np.random.randn(n, p_stable)\\n noise_feature_dependent = np.zeros([n, p_noise])\\n noise_feature_independent = np.random.randn(n, p_noise)\\n for i in range(p_noise):\\n noise_feature_dependent[:, i] = stable_feature[:, i % p_stable] + stable_feature[:,\\n (i + 1) % p_stable] + 2 * np.random.randn(\\n n) # still need noise\\n noise_depend_label = np.random.uniform(0, 1, n).reshape(-1, 1)\\n noise_depend_label = np.concatenate([noise_depend_label] * p_noise, axis=1)\\n noise_feature = np.where(noise_depend_label < depend_ratio, noise_feature_dependent, noise_feature_independent)\\n\\n b = np.zeros([p_stable, 1])\\n linear_len = int(p_stable / 2)\\n\\n for i in range(linear_len): # linear part\\n b[i, 0] = (-1) ** i * (i % 3 + 1) * p / 3\\n for i in range(linear_len, b.shape[0]): # nonlinear part\\n b[i, 0] = p / 2\\n\\n linear_part = np.matmul(stable_feature[:, :linear_len], b[:linear_len, 0])\\n nolinear_part = np.zeros([n, 1])\\n for i in range(linear_len, b.shape[0]):\\n temp = stable_feature[:, i % p_stable] * stable_feature[:, (i + 1) % p_stable] * b[i, 0]\\n temp = temp.reshape(-1, 1)\\n nolinear_part += temp\\n\\n Y = linear_part.reshape(-1, 1) + nolinear_part + np.random.randn(n, 1)\\n\\n data = {}\\n data['stable'] = stable_feature\\n data['noise'] = noise_feature\\n data['Y'] = Y\\n data['params'] = b\\n data['kernel'] = 'eg3'\\n return data\\n\\n data_train = eg3_kernel(n=N_train, p=feature_num, stable_ratio=stable_ratio, depend_ratio=depend_ratio_train)\\n data_test = eg3_kernel(n=N_test, p=feature_num, stable_ratio=stable_ratio, depend_ratio=depend_ratio_test)\\n return data_train, data_test\",\n \"def __init__(self, model, eps_list, k, a, repeat, random_start=True, loss_func='cw'):\\n self.model = model\\n self.eps_list = eps_list\\n self.k = k\\n self.a = a\\n self.repeat = repeat\\n self.rand = random_start\\n\\n if loss_func == 'xent':\\n loss = model.xent\\n elif loss_func == 'cw':\\n label_mask = tf.one_hot(model.y,\\n 10,\\n on_value=1.0,\\n off_value=0.0,\\n dtype=tf.float32)\\n correct_logit = tf.reduce_sum(label_mask * model.y_, axis=1)\\n wrong_logit = tf.reduce_max((1 - label_mask) * model.y_, axis=1)\\n loss = -tf.nn.relu(correct_logit - wrong_logit + 50)\\n else:\\n print('Unknown loss function. Defaulting to cross-entropy')\\n loss = model.xent\\n\\n self.grad = tf.gradients(loss, model.x)[0]\",\n \"def calc_K_tilda(kernel: Type[Kern], X_train: np.array, X_m: np.array):\\n Knn = kernel.K(X_train, X_train)\\n Knm = kernel.K(X_train, X_m)\\n Kmn = kernel.K(X_m, X_train)\\n Kmm = kernel.K(X_m, X_m)\\n temp = np.dot(np.dot(Knm, np.linalg.inv(Kmm)), Kmn)\\n K_tilda = np.subtract(Knn, temp)\\n return K_tilda\",\n \"def create_model(input_shape):\\n h, w, c = input_shape\\n\\n\\n # %%%\\n inpt = tf.placeholder(tf.float32, (None,h,w,c), 'attributes')\\n inpt_flattenned = tf.contrib.layers.flatten(inpt) #tf.reshape(inpt, [-1, h*w*c])\\n H1 = tf.layers.dense(inpt_flattenned, units=200, activation=tf.sigmoid)\\n H2 = tf.layers.dense(H1, units=20, activation=tf.sigmoid)\\n encoding = tf.layers.dense(H2, units=latent_space_size, activation=tf.nn.sigmoid)\\n H22 = tf.layers.dense(encoding, units=20, activation=tf.sigmoid)\\n H11 = tf.layers.dense(H22, units=200, activation=tf.nn.sigmoid)\\n dec_flt = tf.layers.dense(H11, units=h*w*c)\\n decode = tf.reshape(dec_flt, [-1, h,w,c])\\n# The results which were the most pleasing to the eye were achieved with the following loss function, 2 hidden layers and the latent spaces of tens of dimentions. \\n# cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dec_flt, labels=tf.sigmoid(inpt_flattenned)))\\n cost = tf.reduce_mean(tf.square(dec_flt-inpt_flattenned))\\n # %%%\\n\\n model = {'cost': cost,\\n 'input': input,\\n 'enc': encoding,\\n 'dec': decode\\n }\\n return model\",\n \"def build_mlp(x, output_size, scope, n_layers, size, activation=tf.tanh, output_activation=None, regularizer=None):\\n i = 0\\n for i in range(n_layers):\\n x = tf.layers.dense(inputs=x,units=size, activation=activation, name='fc{}'.format(i), kernel_regularizer=regularizer, bias_regularizer=regularizer)\\n\\n x = tf.layers.dense(inputs=x, units=output_size, activation=output_activation, name='fc{}'.format(i + 1), kernel_regularizer=regularizer, bias_regularizer=regularizer)\\n return x\",\n \"def make_mlp_model():\\n return snt.Sequential([\\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\\n snt.LayerNorm()\\n ])\",\n \"def sample_gumbel(shape, eps=1e-20):\\n U = tf.random_uniform(shape, minval=0, maxval=1)\\n return -tf.log(-tf.log(U + eps) + eps)\",\n \"def _create_elu(cls, op, op_t):\\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\\n\\n node.attribute.extend([\\n helper.make_attribute('alpha', op.alpha),\\n ])\\n return node\",\n \"def sample_gumbel(shape, eps=1e-20):\\n U = tf.random_uniform(shape, minval=0, maxval=1)\\n return -tf.log(-tf.log(U + eps) + eps)\",\n \"def get_TF():\\n \\n # returns a vector of n points from the interval [-1,1]\\n rnd = lambda n: np.random.uniform(-1,1,n)\\n \\n # choose two random points A, B in [-1,1] x [-1,1] \\n A = rnd(2)\\n B = rnd(2)\\n \\n # the line can be describe by y = kx + m,\\n # k = slope, m = intercept with y axis\\n k = (B[1] - A[1]) / (B[0] - A[0])\\n m = B[1] - k * B[0] \\n w_f = np.array([m, k, -1]) \\n \\n return w_f\",\n \"def __init__(self, space, exponent):\\n super().__init__(space=space, linear=False)\\n self.__norm = LpNorm(space, exponent)\\n self.__exponent = float(exponent)\",\n \"def noam_norm(x, epsilon=1.0, scope=None, reuse=None):\\n with tf.name_scope(scope, default_name=\\\"noam_norm\\\", values=[x]):\\n shape = x.get_shape()\\n ndims = len(shape)\\n return tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(tf.to_float(shape[-1]))\",\n \"def noam_norm(x, epsilon=1.0, scope=None, reuse=None):\\n with tf.name_scope(scope, default_name=\\\"noam_norm\\\", values=[x]):\\n shape = x.get_shape()\\n ndims = len(shape)\\n return tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(tf.to_float(shape[-1]))\",\n \"def _tf1_ ( self , *args ) :\\n #\\n if not hasattr ( self , '_wo1' ) : self._wo1 = _WO1_ ( self )\\n if not self._wo1 : self._wo1 = _WO1_ ( self )\\n ## \\n _wo = self._wo1 \\n fun = ROOT.TF1 ( funID() , _wo , *args )\\n fun.SetNpx ( 500 ) \\n #\\n return fun\",\n \"def create_layer(prev, n, activation):\\n\\n kernel_ini = tf.contrib.layers.variance_scaling_initializer(mode=\\\"FAN_AVG\\\")\\n layer = tf.layers.Dense(units=n, activation=activation,\\n kernel_initializer=kernel_ini, name='layer')\\n\\n return layer(prev)\",\n \"def _model(self, scope):\\n\\n with tf.variable_scope(scope):\\n\\n act = tf.nn.elu\\n initializer = tfcl.variance_scaling_initializer()\\n xs = tf.layers.dense(self.state, 400, act, kernel_initializer=initializer)\\n xs = tf.layers.dense(xs, 300, None, kernel_initializer=initializer)\\n\\n xa = tf.layers.dense(self._action, 400, act, kernel_initializer=initializer)\\n xa = tf.layers.dense(xa, 300, None, kernel_initializer=initializer)\\n\\n x = tf.nn.elu(xa + xs)\\n\\n q_value = tf.layers.dense(x, 1, None, kernel_initializer=initializer)\\n\\n return q_value\",\n \"def test_xavier_normal():\\n tf.reset_default_graph()\\n tf.random.set_random_seed(0)\\n xnormal_class = INITIALIZER_REGISTRY['xavier']\\n xnormal_obj = xnormal_class({\\\"uniform\\\":False})\\n tf_init = xnormal_obj.get_entity_initializer(init_type='tf')\\n var1 = tf.get_variable(shape=(2000, 100), initializer=tf_init, name=\\\"var1\\\")\\n with tf.Session() as sess:\\n sess.run(tf.global_variables_initializer())\\n tf_var = sess.run(var1)\\n np_var = xnormal_obj.get_entity_initializer(2000, 100, init_type='np')\\n # print(np.mean(np_var), np.std(np_var))\\n # print(np.mean(tf_var), np.std(tf_var))\\n assert(np.round(np.mean(np_var),2)==np.round(np.mean(tf_var),2))\\n assert(np.round(np.std(np_var),2)==np.round(np.std(tf_var),2))\",\n \"def __init__(self, input_dim, hidden_dim, output_dim, epsilon):\\n \\n self.W1 = np.random.randn(input_dim, hidden_dim) / np.sqrt(input_dim)\\n self.W2 = np.random.randn(hidden_dim,output_dim) / np.sqrt(hidden_dim)\\n self.b1 = np.zeros((1, hidden_dim))\\n self.b2 = np.zeros((1, output_dim))\\n self.epsilon = epsilon\",\n \"def build_critic_z(latent_space_dim: Tuple[int , int] ,num_dense_units: int = 100)->tf.keras.Model:\\n\\n input = tf.keras.layers.Input(shape=latent_space_dim, name=\\\"critic_z_input\\\")\\n\\n dense = tf.keras.layers.Flatten()(input)\\n dense = tf.keras.layers.Dense(units=num_dense_units)(input)\\n dense = tf.keras.layers.LeakyReLU(alpha=0.2)(dense)\\n dense = tf.keras.layers.Dropout(0.25)(dense)\\n\\n dense = tf.keras.layers.Dense(units=num_dense_units)(dense)\\n dense = tf.keras.layers.LeakyReLU(alpha=0.2)(dense)\\n dense = tf.keras.layers.Dropout(0.25)(dense)\\n \\n dense = tf.keras.layers.Dense(units=1)(dense)\\n\\n model = tf.keras.Model(inputs=input, outputs=dense, name=\\\"critic_z\\\")\\n return model\",\n \"def _create_gemm(cls, op, op_t):\\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\\n\\n node.attribute.extend([\\n helper.make_attribute('alpha', float(op.alpha)),\\n helper.make_attribute('beta', float(op.beta)),\\n helper.make_attribute('transA', op.transA),\\n helper.make_attribute('transB', op.transB),\\n ])\\n\\n return node\",\n \"def KD(T=8):\\n\\n def KD_loss(input, teacher_logits):\\n return nn.KLDivLoss()(F.log_softmax(input / T, dim=1), F.softmax(teacher_logits / T, dim=1))\\n\\n return KD_loss\",\n \"def decoder(latent):\\n out = tf.layers.dense(latent, 400, activation=tf.nn.relu)\\n if USE_BINARY:\\n out = tf.layers.dense(out, 28 * 28)\\n out = tf.reshape(out, [-1, 28, 28, 1])\\n return tf.distributions.Bernoulli(logits=out)\\n else:\\n mean = tf.reshape(tf.layers.dense(out, 28 * 28), [-1, 28, 28, 1])\\n logstd = tf.reshape(tf.layers.dense(out, 28 * 28), [-1, 28, 28, 1])\\n return tf.distributions.Normal(loc=mean, scale=tf.exp(logstd))\",\n \"def noam_norm(x, epsilon=1.0, scope=None, reuse=None):\\r\\n with tf.name_scope(scope, default_name=\\\"noam_norm\\\", values=[x]):\\r\\n shape = x.get_shape()\\r\\n ndims = len(shape)\\r\\n return tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(tf.to_float(shape[-1]))\",\n \"def gelu(input_tensor):\\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\\n return input_tensor * cdf\",\n \"def gelu(input_tensor):\\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\\n return input_tensor * cdf\",\n \"def __init__(self, num_units, input_size=None,\\n nonlinearity=tf.nn.tanh,\\n hh_init=tf.random_normal_initializer(stddev=0.15),\\n xh_init=tf.random_normal_initializer(stddev=0.15),\\n b_init=tf.constant_initializer(0.0, dtype=tf.float32),\\n weight_noise=0.0,\\n keep_prob=1.0,\\n weightnorm=False,\\n orthreg=False):\\n self._num_units = num_units\\n self._input_size = input_size or num_units\\n self._nonlinearity = nonlinearity\\n self._hh_init = hh_init\\n self._xh_init = xh_init\\n self._b_init = b_init\\n self._weightnorm = weightnorm\\n self._keep_prob = keep_prob\\n self._weight_noise = weight_noise\\n self._orthreg = orthreg\",\n \"def KL(self):\\n\\n self.build_cholesky_if_needed()\\n\\n KL = -0.5 * self.num_inducing * self.num_nodes * self.dim_per_out\\n\\n for nd in range(self.num_nodes):\\n q_sqrt_nd = self.q_sqrt_lst[nd]\\n with params_as_tensors_for(q_sqrt_nd, convert=True):\\n KL -= 0.5 * tf.reduce_sum(tf.log(tf.matrix_diag_part(q_sqrt_nd) ** 2))\\n\\n KL += tf.reduce_sum(tf.log(tf.matrix_diag_part(self.Lu[nd]))) * self.dim_per_out\\n KL += 0.5 * tf.reduce_sum(\\n tf.square(tf.matrix_triangular_solve(self.Lu_tiled_lst[nd], q_sqrt_nd, lower=True)))\\n q_mu_nd = self.q_mu[:, nd * self.dim_per_out:(nd + 1) * self.dim_per_out]\\n Kinv_m_nd = tf.cholesky_solve(self.Lu[nd], q_mu_nd)\\n KL += 0.5 * tf.reduce_sum(q_mu_nd * Kinv_m_nd)\\n\\n return KL\",\n \"def _create_variables(self):\\n\\n \\n with tf.name_scope(\\\"variable\\\"):\\n if self.reg_type == 'L2':\\n regularizer = tf.contrib.layers.l2_regularizer(scale=self.reg_scale)\\n else:\\n regularizer = tf.contrib.layers.l1_regularizer(scale=self.reg_scale)\\n \\n self.dim_lst = [self.dim_inputs] + self.dim_hidden_lst + [self.number_structures]\\n print(self.dim_lst)\\n \\n self.W_lst = []\\n self.b_lst = []\\n for i in range(len(self.dim_lst)-1):\\n self.W_lst.append(tf.get_variable(\\n \\\"W{}\\\".format(i+1),\\n shape=[self.dim_lst[i], self.dim_lst[i+1]],\\n initializer=tf.contrib.layers.xavier_initializer(),\\n regularizer=regularizer)\\n )\\n # not output layer, has bias term\\n if i < len(self.dim_lst) - 2:\\n self.b_lst.append(tf.get_variable(\\\"b{}\\\".format(i+1), shape=[self.dim_lst[i+1]]))\",\n \"def noam_norm(x, epsilon=1.0, name=None):\\n with tf.name_scope(name, default_name=\\\"noam_norm\\\", values=[x]):\\n shape = x.get_shape()\\n ndims = len(shape)\\n result = tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon)*tf.sqrt(tf.to_float(shape[-1]))\\n return result\",\n \"def _tf2_ ( self , *args ) :\\n ##\\n if not hasattr ( self , '_wo2' ) : self._wo2 = _WO2_ ( self )\\n if not self._wo2 : self._wo2 = _WO2_ ( self )\\n ## \\n _wo = self._wo2\\n fun = ROOT.TF2 ( funID () , _wo , *args )\\n fun.SetNpx ( 100 ) \\n fun.SetNpy ( 100 ) \\n #\\n return fun\",\n \"def setUp(self):\\n np.random.seed(1337)\\n self.w0_val = np.random.rand(2, 3)\\n self.w1_val = np.random.rand(3, 5)\\n self.w2_val = np.random.rand(10, 3)\\n self.w3_val = np.random.rand(10, 5)\\n self.w4_val = np.random.rand(10, 5)\\n self.w5_val = np.linspace(-7, 7, 200)\\n # self.w5_val = np.random.rand(15)\\n self.w6_val = np.random.rand(2, 3, 5, 7)\\n\\n self.w3_val /= np.expand_dims(np.sum(self.w3_val, axis=1), axis=1) # w3 is a probability distribution\\n\\n self.tf_w0 = tf.constant(self.w0_val, dtype=tf.float64)\\n self.tf_w1 = tf.constant(self.w1_val, dtype=tf.float64)\\n self.tf_w2 = tf.constant(self.w2_val, dtype=tf.float64)\\n self.tf_w3 = tf.constant(self.w3_val, dtype=tf.float64)\\n self.tf_w4 = tf.constant(self.w4_val, dtype=tf.float64)\\n self.tf_w5 = tf.constant(self.w5_val, dtype=tf.float64)\\n self.tf_w6 = tf.constant(self.w6_val, dtype=tf.float64)\\n\\n self.my_w0 = ad.Variable(self.w0_val, name=\\\"w0_val\\\")\\n self.my_w1 = ad.Variable(self.w1_val, name=\\\"w1_val\\\")\\n self.my_w2 = ad.Variable(self.w2_val, name=\\\"w2_val\\\")\\n self.my_w3 = ad.Variable(self.w3_val, name=\\\"w3_val\\\")\\n self.my_w4 = ad.Variable(self.w4_val, name=\\\"w4_val\\\")\\n self.my_w5 = ad.Variable(self.w5_val, name=\\\"w5_val\\\")\\n self.my_w6 = ad.Variable(self.w6_val, name=\\\"w6_val\\\")\",\n \"def calc_alpha(epsilon): \\n return float(0.5 * np.log((1-epsilon)/epsilon))\",\n \"def makeenv(self):\\n eps=np.ones((self.nx,self.ny))*const.epsilon_0\\n mu=np.ones((self.nx,self.ny))*const.mu_0\\n\\n eps[:20,:] *= self.q #adself.ds a space of higher permittivity \\n eps[-20:,:] *= self.q #adself.ds a space of higher permittivity \\n eps[:,:20] *= self.q #adself.ds a space of higher permittivity \\n eps[:,-20:] *= self.q #adself.ds a space of higher permittivity \\n #mu[:20,:] /= self.q #adself.ds a space of higher permittivity \\n #mu[-20:,:] /= self.q #adself.ds a space of higher permittivity \\n #mu[:,:20] /= self.q #adself.ds a space of higher permittivity \\n #mu[:,-20:] /= self.q #adself.ds a space of higher permittivity \\n\\n return eps, mu\",\n \"def gelu_new(x):\\n cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\\n return x * cdf\",\n \"def __create_tf_matrix(alpha, a, d, q):\\n\\n tf_matrix = Matrix([[cos(q), -sin(q), 0., a],\\n [sin(q) * cos(alpha), cos(q) * cos(alpha), -sin(alpha), -sin(alpha) * d],\\n [sin(q) * sin(alpha), cos(q) * sin(alpha), cos(alpha), cos(alpha) * d],\\n [0., 0., 0., 1.]])\\n return tf_matrix\",\n \"def __init__(self,ls,activations = [tf.nn.tanh, tf.nn.tanh, None], sess = None, RL = False, lr = 1e-2):\\n self.ls = ls\\n if sess == None:\\n self.sess = self.tf_reset()\\n else:\\n self.sess = sess\\n self.activations = activations\\n self.input_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[0]]) # batch-size by state size\\n self.output_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[-1]]) # action space size\\n self.W_dict = {}\\n self.b_dict = {}\\n for i in range(len(ls)-1):\\n self.W_dict[i] = tf.get_variable(name='W'+str(i), shape=[ls[i], ls[i+1]], initializer=tf.contrib.layers.xavier_initializer())\\n self.b_dict[i] = tf.get_variable(name='b'+str(i), shape=[ls[i+1]], initializer=tf.constant_initializer(0.))\\n\\n\\n self.layer = self.input_ph\\n print(tf.shape(self.layer))\\n\\n\\n for i in range(len(self.activations)):\\n self.layer = tf.matmul(self.layer, self.W_dict[i]) + self.b_dict[i]\\n print(tf.shape(self.layer))\\n if self.activations[i] is not None:\\n self.layer = self.activations[i](self.layer)\\n self.output_pred = self.layer\\n\\n if RL == True: \\n with tf.name_scope('reward_holder'):\\n self.reward_holder = tf.placeholder(shape=[None],dtype=tf.float32)\\n \\n with tf.name_scope('get_resp_outs'):\\n self.action_holder = tf.placeholder(shape=[None],dtype=tf.int32, name = 'action_holder')\\n \\n self.indexes = tf.range(0, tf.shape(self.output_pred)[0]) * tf.shape(self.output_pred)[1] + self.action_holder\\n\\n self.responsible_outputs = tf.gather(tf.reshape(self.output_pred, [-1]), self.indexes, name = 'responsible_outputs')\\n # out of the output vector, this will pull out the indexes\\n # But i still don't understand indexes.\\n\\n # i feel like instead of going thru all of this, you could have just saved the actual outputs. I think I'll try that.\\n # then for responsible outputs, you'd do tf.gather(outputs, action_holder) oh maybe it's not different than this. \\n # Maybe that's exactly what they're doing, bc action_holder is a scaler number. IDK.\\n with tf.name_scope('loss'):\\n self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder) #becuase reward_holder value \\n # doesn't directly change as you change the Weights, this is equivalent to multiplying the gradient by the reward.\\n # when you take the gradient, you're solving for d(log*A)/dW = d(log_p)/dW * d(log_p*A)/d(log_p) = A*d(log_p)/dW. so it's equivalent to mult gradient\\n # by the reward function\\n tvars = tf.trainable_variables()\\n\\n with tf.name_scope('update'):\\n # self.train_step = tf.train.RMSPropOptimizer(learning_rate = lr, decay = 0.99).minimize(self.loss)\\n self.train_step = tf.train.AdamOptimizer().minimize(self.loss)\\n self.init = tf.global_variables_initializer()\",\n \"def rt_layers(lam, T, T_0, tau):\\n B_lam = planck_w(lam, T)\\n B_surf = planck_w(lam, T_0)\\n return B_surf * np.exp(-tau) + B_lam * (1 - np.exp(-tau))\",\n \"def _redef_via_predef_eqn(self):\\r\\n time = self.current_T # + self.d_T\\r\\n\\r\\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\\\\r\\n (self.convect_coeff) \\r\\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\\\\r\\n (self.density * self.heat_capacity)\\r\\n\\r\\n # Source term.\\r\\n def F_func(elem, eta):\\r\\n x = elem.local_to_global(eta)\\r\\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\\r\\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * F\\r\\n\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func,\\r\\n self.node_map,\\r\\n gauss_mult=2) # Use double gp_1D\\r\\n\\r\\n # Boundary term.\\r\\n def f_func(elem, eta):\\r\\n n = elem.guess_normal_vector_global(eta)\\r\\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n x = elem.local_to_global(eta)\\r\\n # Evaluate our boundary term.\\r\\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\\r\\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * f\\r\\n\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func,\\r\\n self.node_map,\\r\\n gauss_mult=2)\",\n \"def __init__(self, epsilon=1e-14):\\n self.epsilon = epsilon\",\n \"def __init__(self, alpha=80, beta=13, gamma=3, spatial_ker_weight=3, bilateral_ker_weight=10):\\n self.alpha = alpha\\n self.beta = beta\\n self.gamma = gamma\\n self.spatial_ker_weight = spatial_ker_weight\\n self.bilateral_ker_weight = bilateral_ker_weight\",\n \"def expDecay(L, N0, tf):\\n\\n\\treturn N0*math.exp(-L*tf)\",\n \"def __init__(self, alpha=1.0, epsilon=0.05, gamma=0.8, numTraining = 10):\\n self.alpha = float(alpha)\\n self.epsilon = float(epsilon)\\n self.discount = float(gamma)\\n self.numTraining = int(numTraining)\",\n \"def model_initializer():\\n model = tf.keras.models.Sequential()\\n model.add(tf.keras.layers.Flatten())\\n # model.add(tf.keras.layers.Dense(128, activation=tf.nn.elu))\\n model.add(tf.keras.layers.Dense(64, activation=tf.nn.elu))\\n model.add(tf.keras.layers.Dense(32, activation=tf.nn.elu))\\n model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\\n\\n\\n model.compile(optimizer='rmsprop',\\n loss='sparse_categorical_crossentropy',\\n metrics=['accuracy'])\\n\\n return model\",\n \"def lrn(x, name, phase=True):\\n # return tf.nn.local_response_normalization(x, depth_radius=radius,\\n # alpha=alpha, beta=beta,\\n # bias=bias, name=name)\\n return tf.contrib.layers.batch_norm(x,\\n center=True, scale=True,\\n is_training=phase,\\n name=name)\",\n \"def Lattice_Theory_Algebraic(N,Kappa,d) :\\n\\t\\n\\tMu2 = (1.0/6.0)*( (math.pi/(N**(1.0/d)) )**2 )*( ( Kappa + 1.0)**( (d + 2.0)/float(d) ) )\\n\\treturn Mu2\",\n \"def planck_w(lam, T):\\n return ((2*h*c**2)/(lam**5))*(1./(np.exp((h*c)/(lam*k*T))-1))\",\n \"def kl_latent_space(network, *args):\\n\\n z, log_det_J = network(*args)\\n loss = tf.reduce_mean(0.5 * tf.square(tf.norm(z, axis=-1)) - log_det_J)\\n return loss\",\n \"def l2_norm(input_x, epsilon=1e-12):\\n input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)\\n return input_x_norm\",\n \"def build_model():\\n with tf.name_scope('placeholders'):\\n real_data_int = tf.placeholder(tf.int32, shape=[None, picture_size])\\n x_true = 2 * ((tf.cast(real_data_int, tf.float32) / 255.) - .5)\\n z = tf.placeholder(tf.float32, [None, input_dim])\\n if use_JL:\\n JL = tf.placeholder(tf.float32, [d_last_layer_size, JL_dim])\\n P_non_normalized = tf.placeholder(tf.float32, [JL_dim, n_projections])\\n P_non_normalized_SWD = tf.placeholder(tf.float32, [picture_size, n_projections])\\n else:\\n JL = None\\n P_non_normalized = tf.placeholder(tf.float32, [d_last_layer_size, n_projections])\\n P_non_normalized_SWD = tf.placeholder(tf.float32, [picture_size, n_projections])\\n\\n x_generated = generator(z, n_features_first=n_features_first_g,\\n n_features_reduction_factor=n_features_reduction_factor, min_features=64,\\n BN=BN, power=power, extra_layer=extra_layer_g,\\n init_method=init_method, n_features_image=n_features_image)\\n\\n d_pred_true, d_last_true = discriminator(x_true, reuse=False, n_features_last=n_features_last_d,\\n n_features_increase_factor=n_features_reduction_factor,\\n min_features=min_features, d_BN=d_BN, power=power,\\n n_features_image=n_features_image, init_method=init_method)\\n d_pred_gen, d_last_gen = discriminator(x_generated, reuse=True, n_features_last=n_features_last_d,\\n n_features_increase_factor=n_features_reduction_factor,\\n min_features=min_features, d_BN=d_BN, power=power,\\n n_features_image=n_features_image, init_method=init_method)\\n\\n # define generator loss (big part taken from SWG)\\n with tf.name_scope('g_loss'):\\n # apply the Johnson-Lindenstrauss map, if wanted, to the flattened array\\n if use_JL:\\n JL_true = tf.matmul(d_last_true, JL)\\n JL_gen = tf.matmul(d_last_gen, JL)\\n else:\\n JL_true = d_last_true\\n JL_gen = d_last_gen\\n\\n # next project the samples (images). After being transposed, we have tensors\\n # of the format: [[projected_image1_proj1, projected_image2_proj1, ...],\\n # [projected_image1_proj2, projected_image2_proj2, ...],...]\\n # Each row has the projections along one direction. This makes it easier for the sorting that follows.\\n # first normalize the random normal vectors to lie in the sphere\\n P = tf.nn.l2_normalize(P_non_normalized, axis=0)\\n\\n projected_true = tf.transpose(tf.matmul(JL_true, P))\\n projected_fake = tf.transpose(tf.matmul(JL_gen, P))\\n\\n sorted_true, true_indices = tf.nn.top_k(input=projected_true, k=batch_size)\\n sorted_fake, fake_indices = tf.nn.top_k(input=projected_fake, k=batch_size)\\n\\n # For faster gradient computation, we do not use sorted_fake to compute\\n # loss. Instead we re-order the sorted_true so that the samples from the\\n # true distribution go to the correct sample from the fake distribution.\\n\\n # It is less expensive (memory-wise) to rearrange arrays in TF.\\n # Flatten the sorted_true from dim [n_projections, batch_size].\\n flat_true = tf.reshape(sorted_true, [-1])\\n\\n # Modify the indices to reflect this transition to an array.\\n # new index = row + index\\n rows = np.asarray([batch_size * np.floor(i * 1.0 / batch_size) for i in range(n_projections * batch_size)])\\n rows = rows.astype(np.int32)\\n flat_idx = tf.reshape(fake_indices, [-1, 1]) + np.reshape(rows, [-1, 1])\\n\\n # The scatter operation takes care of reshaping to the rearranged matrix\\n shape = tf.constant([batch_size * n_projections])\\n rearranged_true = tf.reshape(tf.scatter_nd(flat_idx, flat_true, shape), [n_projections, batch_size])\\n\\n generator_loss = tf.reduce_mean(tf.square(projected_fake - rearranged_true))\\n\\n # get the sliced Wasserstein distance (SWD) (since SWD and JLSWD are not comparable)\\n with tf.name_scope('SWD'):\\n P_SWD = tf.nn.l2_normalize(P_non_normalized_SWD, axis=0)\\n\\n projected_true_SWD = tf.transpose(tf.matmul(x_true, P_SWD))\\n projected_fake_SWD = tf.transpose(tf.matmul(x_generated, P_SWD))\\n\\n sorted_true_SWD, true_indices_SWD = tf.nn.top_k(input=projected_true_SWD, k=batch_size)\\n sorted_fake_SWD, fake_indices_SWD = tf.nn.top_k(input=projected_fake_SWD, k=batch_size)\\n\\n flat_true_SWD = tf.reshape(sorted_true_SWD, [-1])\\n flat_idx_SWD = tf.reshape(fake_indices_SWD, [-1, 1]) + np.reshape(rows, [-1, 1])\\n\\n rearranged_true_SWD = tf.reshape(tf.scatter_nd(flat_idx_SWD, flat_true_SWD, shape),\\n [n_projections, batch_size])\\n\\n SWD = tf.reduce_mean(tf.square(projected_fake_SWD - rearranged_true_SWD))\\n\\n # define the discriminator loss\\n with tf.name_scope('d_loss'):\\n d_true_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_pred_true), logits=d_pred_true)\\n d_fake_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_pred_gen), logits=d_pred_gen)\\n discriminator_loss = tf.reduce_mean(d_true_loss + d_fake_loss)\\n\\n with tf.name_scope('g_optimizer'):\\n generator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')\\n g_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\\n g_train = g_optimizer.minimize(generator_loss, var_list=generator_vars)\\n\\n with tf.name_scope('d_optimizer'):\\n discriminator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')\\n d_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\\n d_train = d_optimizer.minimize(discriminator_loss, var_list=discriminator_vars)\\n\\n return real_data_int, z, x_generated, JL, P_non_normalized, P_non_normalized_SWD, SWD, g_train, d_train\",\n \"def __init__(self, eps: float = 1e-5):\\n if eps <= 0.0:\\n raise ValueError(\\\"The epsilon value must be positive\\\")\\n super().__init__()\\n self.register_buffer('eps', torch.tensor(eps))\",\n \"def noam_norm(x, epsilon=1.0, name=None):\\n with tf.name_scope(name, default_name=\\\"noam_norm\\\", values=[x]):\\n shape = x.get_shape()\\n ndims = len(shape)\\n return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(\\n tf.to_float(shape[-1])))\",\n \"def SCEC_LOH_1():\\n\\n #Initialize CrustModel\\n model = CrustModel(2)\\n\\n #Slow layer\\n vp=4.000\\n vs=2.000\\n rho=2.600\\n Qa=10000.\\n Qb=10000.\\n thickness = 1.0\\n\\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\\n\\n #Halfspace\\n vp=6.000\\n vs=3.464\\n rho=2.700\\n Qa=10000.\\n Qb=10000.\\n thickness = 0 #Infinite thickness!\\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\\n\\n return model\",\n \"def __init__(self, img_input, p=6.):\\n super(LPNorm, self).__init__()\\n if p < 1:\\n raise ValueError('p value should range between [1, inf)')\\n self.name = \\\"L-{} Norm Loss\\\".format(p)\\n self.p = p\\n self.img = img_input\",\n \"def lnlike(self, block):\\n ##### Extract from datablock\\n self.cosmology = {'Omega_m': block.get_double('cosmological_parameters', 'Omega_m'),\\n 'Omega_l': block.get_double('cosmological_parameters', 'Omega_lambda'),\\n 'Omega_b': block.get_double('cosmological_parameters', 'Omega_b'),\\n 'h': block.get_double('cosmological_parameters', 'hubble')/100,\\n 'ns': block.get_double('cosmological_parameters', 'n_s'),\\n 'w0': block.get_double('cosmological_parameters', 'w'),\\n 'wa': block.get_double('cosmological_parameters', 'wa'),\\n 'sigma8': block.get_double('cosmological_parameters', 'sigma_8')}\\n self.scaling = {\\n # SZ\\n 'Asz': block.get_double('mor_parameters', 'Asz'),\\n 'Bsz': block.get_double('mor_parameters', 'Bsz'),\\n 'Csz': block.get_double('mor_parameters', 'Csz'),\\n 'Dsz': block.get_double('mor_parameters', 'Dsz'),\\n # X-ray\\n 'Ax': block.get_double('mor_parameters', 'Ax'),\\n 'Bx': block.get_double('mor_parameters', 'Bx'),\\n 'Cx': block.get_double('mor_parameters', 'Cx'),\\n 'Dx': block.get_double('mor_parameters', 'Dx'),\\n 'dlnMg_dlnr': block.get_double('mor_parameters', 'dlnMg_dlnr'),\\n # WL\\n 'WLbias': block.get_double('mor_parameters', 'WLbias'),\\n 'WLscatter': block.get_double('mor_parameters', 'WLscatter'),\\n 'HSTbias': block.get_double('mor_parameters', 'HSTbias'),\\n 'HSTscatterLSS': block.get_double('mor_parameters', 'HSTscatterLSS'),\\n 'MegacamBias': block.get_double('mor_parameters', 'MegacamBias'),\\n 'MegacamScatterLSS': block.get_double('mor_parameters', 'MegacamScatterLSS'),\\n # Correlation coefficients\\n 'rhoSZWL': block.get_double('mor_parameters', 'rhoSZWL'),\\n 'rhoWLX': block.get_double('mor_parameters', 'rhoWLX'),\\n 'rhoSZX': block.get_double('mor_parameters', 'rhoSZX'),\\n }\\n # Halo mass function\\n self.HMF = {'M_arr': block.get_double_array_1d('HMF', 'M_arr'),\\n 'z_arr': block.get_double_array_1d('HMF', 'z_arr'),\\n 'dNdlnM': block.get_double_array_nd('HMF', 'dNdlnM')}\\n self.HMF['len_z'] = len(self.HMF['z_arr'])\\n\\n ##### Setup stuff for WL\\n if self.todo['WL']:\\n # Set bias and scatter for Megacam from sim calibration\\n # and nuissance parameters\\n self.WL.set_scaling(self.scaling)\\n # Precompute array of angular diameter distances\\n self.WL.get_dAs(self.cosmology)\\n\\n ##### Populate and check observable covariance matrices\\n self.covmat = {'invertible': True}\\n if not observablecovmat.set_covmats(self.todo, self.scaling, self.covmat):\\n self.covmat['invertible'] = False\\n return -np.inf\\n\\n ##### Set up interpolation for HMF\\n HMF_in = self.HMF['dNdlnM'][1:,:]\\n if np.any(HMF_in==0):\\n HMF_in[np.where(HMF_in==0)] = np.nextafter(0, 1)\\n self.HMF_interp = RectBivariateSpline(np.log(self.HMF['z_arr'][1:]), np.log(self.HMF['M_arr']), np.log(HMF_in))\\n\\n ##### Initialize mass-concentration relation class (for WL)\\n if self.todo['WL']:\\n self.MCrel = Mconversion_concentration.ConcentrationConversion(self.mcType, self.cosmology)\\n\\n ##### Compute interpolation table for M500-M200\\n if self.todo['WL']:\\n z_arr = np.linspace(.1, 2, 20)\\n M500 = np.logspace(np.log10(self.HMF['M_arr'][0]), np.log10(self.HMF['M_arr'][-1]), 20)\\n M200 = np.array([np.array([self.MCrel.MDelta_to_M200(m, 500., z) for m in M500]) for z in z_arr])\\n self.lnM500_to_lnM200 = RectBivariateSpline(z_arr, np.log(M500), np.log(M200))\\n\\n ##### Evaluate the individual likelihoods\\n len_data = len(self.catalog['SPT_ID'])\\n\\n if self.NPROC==0:\\n # Iterate through cluster list\\n likelihoods = np.array([self.clusterlike(i) for i in range(len_data)])\\n else:\\n # Launch a multiprocessing pool and get the likelihoods\\n pool = Pool(processes=self.NPROC)\\n argin = zip([self]*len_data, range(len_data))\\n likelihoods = pool.map(unwrap_self_f, argin)\\n pool.close()\\n\\n # If likelihood computation failed it returned 0\\n if np.count_nonzero(likelihoods)<len_data:\\n return -np.inf\\n\\n lnlike = np.sum(np.log(likelihoods))\\n\\n return lnlike\",\n \"def build_reparam_kl_loss_and_gradients(inference, var_list):\\n \\n p_log_lik = [0.0] * inference.n_samples\\n base_scope = tf.get_default_graph().unique_name(\\\"inference\\\") + '/'\\n for s in range(inference.n_samples):\\n # Form dictionary in order to replace conditioning on prior or\\n # observed variable with conditioning on a specific value.\\n scope = base_scope + tf.get_default_graph().unique_name(\\\"sample\\\")\\n dict_swap = {}\\n for x, qx in six.iteritems(inference.data):\\n if isinstance(x, RandomVariable):\\n if isinstance(qx, RandomVariable):\\n qx_copy = copy(qx, scope=scope)\\n dict_swap[x] = qx_copy.value()\\n else:\\n dict_swap[x] = qx\\n\\n for z, qz in six.iteritems(inference.latent_vars):\\n # Copy q(z) to obtain new set of posterior samples.\\n qz_copy = copy(qz, scope=scope)\\n dict_swap[z] = qz_copy.value()\\n\\n for x in six.iterkeys(inference.data):\\n if isinstance(x, RandomVariable):\\n x_copy = copy(x, dict_swap, scope=scope)\\n p_log_lik[s] += tf.reduce_sum(\\n inference.scale.get(x, 1.0) * x_copy.log_prob(dict_swap[x]))\\n\\n p_log_lik = tf.reduce_mean(p_log_lik)\\n\\n kl_penalty = tf.reduce_sum([\\n tf.reduce_sum(inference.kl_scaling.get(z, 1.0) * kl_divergence(qz, z))\\n for z, qz in six.iteritems(inference.latent_vars)])\\n\\n if inference.logging:\\n tf.summary.scalar(\\\"loss/p_log_lik\\\", p_log_lik,\\n collections=[inference._summary_key])\\n tf.summary.scalar(\\\"loss/kl_penalty\\\", kl_penalty,\\n collections=[inference._summary_key])\\n\\n loss = -(p_log_lik - kl_penalty)\\n grads = tf.gradients(loss, var_list)\\n grads_and_vars = list(zip(grads, var_list))\\n return loss, grads_and_vars\",\n \"def __init__(self, alpha=1.0, epsilon=0.05, gamma=0.8, numTraining=10):\\n self.alpha = float(alpha)\\n self.epsilon = float(epsilon)\\n self.discount = float(gamma)\\n self.numTraining = int(numTraining)\",\n \"def __init__(self, k=3, min_iter=50, max_iter=1000, learn_rate=1e-7,\\n regularization=0.5, convergence_tol=0.001, use_pca=True,\\n verbose=False, preprocessor=None):\\n self.k = k\\n self.min_iter = min_iter\\n self.max_iter = max_iter\\n self.learn_rate = learn_rate\\n self.regularization = regularization\\n self.convergence_tol = convergence_tol\\n self.use_pca = use_pca\\n self.verbose = verbose\\n super(_base_LMNN, self).__init__(preprocessor)\",\n \"def build_model(nx, layers, activations, lambtha, keep_prob):\\n λ = lambtha\\n\\n # create model\\n a_model = K.Sequential()\\n n_layers = len(layers)\\n regularizer = K.regularizers.l2(λ)\\n\\n for i in range(n_layers):\\n # Adds a densely-connected layer with layer[i] units to the model:\\n a_model.add(K.layers.Dense(\\n units=layers[i],\\n input_dim=nx,\\n kernel_regularizer=regularizer,\\n activation=activations[i],\\n )\\n )\\n # To avoid creation of:\\n # Layer (type) Output Shape Param #\\n # dropout_2 (Dropout) (None, 10) 0\\n if i < n_layers - 1:\\n a_model.add(K.layers.Dropout(1 - keep_prob))\\n return a_model\",\n \"def gelu(input_tensor):\\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\\n return input_tensor * cdf\",\n \"def gelu(input_tensor):\\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\\n return input_tensor * cdf\",\n \"def log(x, eps=1e-7, name=None):\\n return tf.log(x + eps, name=name)\",\n \"def __init__(self, hidden_size, eps=1e-12):\\n super(LayerNorm, self).__init__()\\n self.weight = nn.Parameter(torch.ones(hidden_size))\\n self.bias = nn.Parameter(torch.zeros(hidden_size))\\n self.variance_epsilon = eps\",\n \"def build(self): \\n self.computation_graph = tf.Graph()\\n with self.computation_graph.as_default():\\n\\n self.factorization_layer = Factorization(self.args, self.vocab_size)\\n self.regularizer_layer = Regularization(self.args)\\n\\n self.gamma = tf.placeholder(\\\"float\\\")\\n self.loss = self.factorization_layer()\\n\\n self.batch = tf.Variable(0)\\n self.step = tf.placeholder(\\\"float\\\")\\n \\n self.learning_rate_new = tf.train.polynomial_decay(self.args.initial_learning_rate,\\n self.batch,\\n self.true_step_size,\\n self.args.minimal_learning_rate,\\n self.args.annealing_factor)\\n \\n self.train_op = tf.train.AdamOptimizer(self.learning_rate_new).minimize(self.loss, global_step = self.batch)\\n \\n self.init = tf.global_variables_initializer()\\n\\n self.weights = overlap_generator(self.args, self.graph)\",\n \"def decision_function(alphas, target, kernel, X_train, x_test, b):\\n\\n result = (alphas * target) @ kernel(X_train, x_test) - b\\n return result\",\n \"def build(self): \\n self.computation_graph = tf.Graph()\\n with self.computation_graph.as_default():\\n\\n self.factorization_layer = Factorization(self.args, self.vocab_size)\\n self.regularizer_layer = Regularization(self.args)\\n\\n self.gamma = tf.placeholder(\\\"float\\\")\\n self.loss = self.factorization_layer()+self.regularizer_layer(self.factorization_layer)\\n\\n self.batch = tf.Variable(0)\\n self.step = tf.placeholder(\\\"float\\\")\\n \\n self.learning_rate_new = tf.train.polynomial_decay(self.args.initial_learning_rate,\\n self.batch,\\n self.true_step_size,\\n self.args.minimal_learning_rate,\\n self.args.annealing_factor)\\n \\n self.train_op = tf.train.AdamOptimizer(self.learning_rate_new).minimize(self.loss, global_step = self.batch)\\n \\n self.init = tf.global_variables_initializer()\\n\\n self.weights = overlap_generator(self.args, self.graph)\",\n \"def beta_gen_lasso(p):\\n cardi = 0.005\\n return np.array([0]*int(p-int(cardi*p)) + [1]*int(cardi*p))\",\n \"def az_kinetic_model(nt, tf, ka_rates, kb_rates, normed=True, X0=None):\\n def dx(x, t, A, B):\\n return np.dot(A, x) + B\\n \\n if type(nt) == list:\\n nt = transform(nt)\\n \\n A = network_matrix(nt, ka_rates)\\n B = np.dot(network_matrix(nt, kb_rates), np.ones(A.shape[0]))\\n if X0 == None:\\n X0 = np.zeros(A.shape[0])\\n \\n X = odeint(lambda x, t: dx(x, t, A, B), X0, np.linspace(0, tf, 1000))\\n if normed:\\n return az.norm_array(X[-1])\\n else:\\n return X[-1]\",\n \"def ft_ll(m, t, y, x, x_kernel, x_kernel_params, t_kernel, t_kernel_params):\\n # Take copies of everything - this is a function\\n m = copy.deepcopy(m)\\n t = copy.deepcopy(t)\\n y = copy.deepcopy(y)\\n x = copy.deepcopy(x)\\n\\n K_x = x_kernel(x, x, **x_kernel_params)\\n N = len(y)\\n\\n lambd = np.zeros((N, 1))\\n gamma = np.zeros((N, 1))\\n\\n K_t = [None] * N\\n\\n for n in range(N):\\n K_t[n] = t_kernel(t[n], t[n], **t_kernel_params)\\n lambd[n] = np.dot(np.ones((1, len(t[n]))), np.linalg.solve(K_t[n], np.ones((len(t[n]), 1))))\\n # Making sure y[n] is a column vector\\n y[n] = np.array(y[n], ndmin=2)\\n if y[n].shape[0] == 1:\\n y[n] = y[n].T\\n gamma[n] = np.dot(np.ones((1, len(t[n]))), np.linalg.solve(K_t[n], y[n] - m[n] * np.ones(y[n].shape)))\\n\\n Lambd = np.diag(lambd.ravel())\\n\\n ll = 0\\n\\n # Terms relating to individual curves\\n for n in range(N):\\n ll += - 0.5 * np.dot((y[n] - m[n] * np.ones(y[n].shape)).T,\\n np.linalg.solve(K_t[n], y[n] - m[n] * np.ones(y[n].shape)))\\n ll += - 0.5 * np.log(np.linalg.det(K_t[n]))\\n\\n # Terms relating to K_x\\n ll += + 0.5 * np.dot(gamma.T, np.linalg.solve(np.linalg.inv(K_x) + Lambd, gamma))\\n ll += - 0.5 * np.log(np.linalg.det(np.linalg.inv(K_x) + Lambd))\\n ll += - 0.5 * np.log(np.linalg.det(K_x))\\n\\n return ll\",\n \"def __init__(self,ls,activations = [tf.nn.tanh, tf.nn.tanh, None], sess = None, RL = False, lr = 1e-2, reg_scale = 0.1):\\n self.ls = ls\\n if sess == None:\\n self.sess = self.tf_reset()\\n else:\\n self.sess = sess\\n self.activations = activations\\n self.input_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[0]], name = 'msh_input_placeholder') # batch-size by state size\\n self.output_ph = tf.placeholder(dtype=tf.float32, shape=[None, ls[-1]]) # action space size\\n\\n self.fc1 = tf.contrib.layers.fully_connected(self.input_ph, ls[1],\\n weights_regularizer=tf.contrib.layers.l2_regularizer(reg_scale),\\n activation_fn=activations[0])\\n\\n self.fc2 = tf.contrib.layers.fully_connected(self.fc1, ls[2],\\n weights_regularizer=tf.contrib.layers.l2_regularizer(reg_scale),\\n activation_fn=activations[1])\\n\\n self.output_pred = tf.contrib.layers.fully_connected(self.fc2, ls[-1],\\n weights_regularizer=tf.contrib.layers.l2_regularizer(reg_scale),\\n activation_fn=activations[-1])\\n\\n\\n # self.W_dict = {}\\n # self.b_dict = {}\\n # for i in range(len(ls)-1):\\n # self.W_dict[i] = tf.get_variable(name='W'+str(i), shape=[ls[i], ls[i+1]], initializer=tf.contrib.layers.xavier_initializer())\\n # self.b_dict[i] = tf.get_variable(name='b'+str(i), shape=[ls[i+1]], initializer=tf.constant_initializer(0.))\\n\\n\\n # self.layer = self.input_ph\\n # print(tf.shape(self.layer))\\n\\n\\n # for i in range(len(self.activations)):\\n # self.layer = tf.matmul(self.layer, self.W_dict[i]) + self.b_dict[i]\\n # print(tf.shape(self.layer))\\n # if self.activations[i] is not None:\\n # self.layer = self.activations[i](self.layer)\\n # self.output_pred = self.layer\\n\\n if RL == True: \\n with tf.name_scope('reward_holder'):\\n self.reward_holder = tf.placeholder(shape=[None],dtype=tf.float32)\\n \\n with tf.name_scope('get_resp_outs'):\\n self.action_holder = tf.placeholder(shape=[None],dtype=tf.int32, name = 'action_holder')\\n \\n self.indexes = tf.range(0, tf.shape(self.output_pred)[0]) * tf.shape(self.output_pred)[1] + self.action_holder\\n\\n self.responsible_outputs = tf.gather(tf.reshape(self.output_pred, [-1]), self.indexes, name = 'responsible_outputs')\\n # out of the output vector, this will pull out the indexes\\n # But i still don't understand indexes.\\n\\n # i feel like instead of going thru all of this, you could have just saved the actual outputs. I think I'll try that.\\n # then for responsible outputs, you'd do tf.gather(outputs, action_holder) oh maybe it's not different than this. \\n # Maybe that's exactly what they're doing, bc action_holder is a scaler number. IDK.\\n with tf.name_scope('loss'):\\n self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder) #becuase reward_holder value \\n # doesn't directly change as you change the Weights, this is equivalent to multiplying the gradient by the reward.\\n # when you take the gradient, you're solving for d(log*A)/dW = d(log_p)/dW * d(log_p*A)/d(log_p) = A*d(log_p)/dW. so it's equivalent to mult gradient\\n # by the reward function\\n tvars = tf.trainable_variables()\\n\\n with tf.name_scope('update'):\\n # self.train_step = tf.train.RMSPropOptimizer(learning_rate = lr, decay = 0.99).minimize(self.loss)\\n self.train_step = tf.train.AdamOptimizer().minimize(self.loss)\\n self.init = tf.global_variables_initializer()\",\n \"def weiner_tf(H, K):\\r\\n\\r\\n W = (1 / H) * ((np.conjugate(H) * H) / ((np.conjugate(H) * H) + K))\\r\\n return W\",\n \"def test_2_layer():\\r\\n # angular frequency in radians * THz\\r\\n w = 100 * nu.THz\\r\\n # Relative permittivity of metal and dielectric\\r\\n em = -4.56 + 0.12j\\r\\n ed = 1.23 + 0.01j\\r\\n ex_list = ez_list = [ed, em]\\r\\n # Relative permeabilities\\r\\n mu_list = [1,1]\\r\\n # Dictionary of input parameters\\r\\n input_params = {'w': w, 'd_list': [inf,inf], 'ex_list': ex_list,\\r\\n 'ez_list': ez_list, 'mu_list': mu_list}\\r\\n \\r\\n # Calculate the theoretical kx\\r\\n theo_kx = (w / nu.c0) * cmath.sqrt((em * ed) / (em + ed))\\r\\n if theo_kx.imag < 0:\\r\\n theo_kx *= -1\\r\\n print('Theoretical kx:',\\r\\n '(%.7g+%.7gj) rad/um' % (theo_kx.real / nu.um**-1, theo_kx.imag / nu.um**-1))\\r\\n \\r\\n # If I use the theoretical kx value, the mode should be correct and\\r\\n # all my tests should pass.\\r\\n params = deepcopy(input_params)\\r\\n params['kx'] = theo_kx\\r\\n params = find_all_params_from_kx(params)\\r\\n kzd, kzm = params['kz_list']\\r\\n # check that kz_list is correct\\r\\n assert_floats_are_equal(kzd**2, (w**2 / nu.c0**2) * ed**2 / (em + ed))\\r\\n assert_floats_are_equal(kzm**2, (w**2 / nu.c0**2) * em**2 / (em + ed))\\r\\n # check that layer_bottom_list is correct\\r\\n assert params['layer_bottom_list'][0] == -inf\\r\\n assert params['layer_bottom_list'][1] == 0\\r\\n # Check that the boundary condition matrix agrees with hand-calculation\\r\\n bc_mat = bc_matrix(params)\\r\\n # ...top-left is Ex0down / H0down\\r\\n assert_floats_are_equal(bc_mat[0,0], -kzd / (w * ed * nu.eps0))\\r\\n # ...top-right is -Ex1up / H1up\\r\\n assert_floats_are_equal(bc_mat[0,1], -kzm / (w * em * nu.eps0))\\r\\n # ...bottom-left is eps0 * Ez0down / H0down\\r\\n assert_floats_are_equal(bc_mat[1,0], ed * -theo_kx / (w * ed * nu.eps0))\\r\\n # ...bottom-right is -eps1 * Ez1up / H1up\\r\\n assert_floats_are_equal(bc_mat[1,1], -em * -theo_kx / (w * em * nu.eps0))\\r\\n # Check that one of the eigenvalues is almost zero (compared to the size\\r\\n # of the matrix elements).\\r\\n eigenvalues = np.linalg.eig(bc_mat)[0]\\r\\n assert abs(eigenvalues).min() / abs(bc_mat).max() < 1e-6\\r\\n # Check that the mode passes all tests.\\r\\n assert check_mode(params, thorough=True) is True\\r\\n # Check that I can scale the fields and it still passes all tests.\\r\\n params_scaled = rescale_fields(1.23+4.56j, params)\\r\\n assert check_mode(params_scaled, thorough=True) is True\\r\\n \\r\\n # Now try my kx-finding algorithm, to see if it finds the right value.\\r\\n kx_list = find_kx(input_params)\\r\\n print('kx_list:',\\r\\n ['(%.7g+%.7gj) rad/um' % (kx.real / nu.um**-1, kx.imag / nu.um**-1)\\r\\n for kx in kx_list])\\r\\n kx = kx_list[0]\\r\\n assert_floats_are_equal(theo_kx, kx)\\r\\n \\r\\n plot_mode(params)\\r\\n \\r\\n print('If you see this message, all the tests succeeded!!')\",\n \"def genball(npt, ndim, rstate=None):\\n # use Barthe2005\\n x = rstate.standard_normal(size=(npt, ndim))\\n y = rstate.exponential(0.5, size=npt)\\n x1 = x / np.sqrt((y + (x**2).sum(axis=1)))[:, None]\\n return x1\",\n \"def _kl_brute_force(a, b, name=None):\\n\\n def squared_frobenius_norm(x):\\n \\\"\\\"\\\"Helper to make KL calculation slightly more readable.\\\"\\\"\\\"\\n # http://mathworld.wolfram.com/FrobeniusNorm.html\\n # The gradient of KL[p,q] is not defined when p==q. The culprit is\\n # tf.norm, i.e., we cannot use the commented out code.\\n # return tf.square(tf.norm(x, ord=\\\"fro\\\", axis=[-2, -1]))\\n return tf.reduce_sum(tf.square(x), axis=[-2, -1])\\n\\n # TODO(b/35041439): See also b/35040945. Remove this function once LinOp\\n # supports something like:\\n # A.inverse().solve(B).norm(order='fro', axis=[-1, -2])\\n def is_diagonal(x):\\n \\\"\\\"\\\"Helper to identify if `LinearOperator` has only a diagonal component.\\\"\\\"\\\"\\n return (isinstance(x, tf.linalg.LinearOperatorIdentity) or\\n isinstance(x, tf.linalg.LinearOperatorScaledIdentity) or\\n isinstance(x, tf.linalg.LinearOperatorDiag))\\n\\n with tf.name_scope(name or 'kl_mvn'):\\n # Calculation is based on:\\n # http://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians\\n # and,\\n # https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm\\n # i.e.,\\n # If Ca = AA', Cb = BB', then\\n # tr[inv(Cb) Ca] = tr[inv(B)' inv(B) A A']\\n # = tr[inv(B) A A' inv(B)']\\n # = tr[(inv(B) A) (inv(B) A)']\\n # = sum_{ij} (inv(B) A)_{ij}**2\\n # = ||inv(B) A||_F**2\\n # where ||.||_F is the Frobenius norm and the second equality follows from\\n # the cyclic permutation property.\\n if is_diagonal(a.scale) and is_diagonal(b.scale):\\n # Using `stddev` because it handles expansion of Identity cases.\\n b_inv_a = (a.stddev() / b.stddev())[..., tf.newaxis]\\n else:\\n b_inv_a = b.scale.solve(a.scale.to_dense())\\n kl_div = (\\n b.scale.log_abs_determinant() - a.scale.log_abs_determinant() +\\n 0.5 * (-tf.cast(a.scale.domain_dimension_tensor(), a.dtype) +\\n squared_frobenius_norm(b_inv_a) + squared_frobenius_norm(\\n b.scale.solve((b.mean() - a.mean())[..., tf.newaxis]))))\\n tensorshape_util.set_shape(\\n kl_div, tf.broadcast_static_shape(a.batch_shape, b.batch_shape))\\n return kl_div\",\n \"def build(self): \\n self.computation_graph = tf.Graph()\\n with self.computation_graph.as_default():\\n\\n self.factorization_layer = Factorization(self.args, self.vocab_size)\\n self.cluster_layer = Clustering(self.args)\\n self.regularizer_layer = Regularization(self.args)\\n\\n self.gamma = tf.placeholder(\\\"float\\\")\\n self.loss = self.factorization_layer()+self.gamma*self.cluster_layer(self.factorization_layer)+self.regularizer_layer(self.factorization_layer)\\n\\n self.batch = tf.Variable(0)\\n self.step = tf.placeholder(\\\"float\\\")\\n \\n self.learning_rate_new = tf.train.polynomial_decay(self.args.initial_learning_rate,\\n self.batch,\\n self.true_step_size,\\n self.args.minimal_learning_rate,\\n self.args.annealing_factor)\\n \\n self.train_op = tf.train.AdamOptimizer(self.learning_rate_new).minimize(self.loss, global_step = self.batch)\\n \\n self.init = tf.global_variables_initializer()\\n\\n self.weights = overlap_generator(self.args, self.graph)\",\n \"def build_mlp(input_placeholder, output_size, scope, n_layers, size, activation=tf.tanh, output_activation=None):\\n output_placeholder = input_placeholder\\n with tf.variable_scope(scope):\\n for _ in range(n_layers):\\n output_placeholder = tf.layers.dense(output_placeholder, size, activation=activation) # HINT: use tf.layers.dense (specify <input>, <size>, activation=<?>)\\n output_placeholder = tf.layers.dense(output_placeholder, output_size, activation=output_activation) # HINT: use tf.layers.dense (specify <input>, <size>, activation=<?>)\\n return output_placeholder\",\n \"def lenet300_classic():\\n return LeNet300(dropout=False, nonlinearity=nn.Tanh)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62754345","0.60140204","0.5792275","0.57416606","0.55366963","0.55194634","0.54221153","0.5412391","0.5388891","0.53688395","0.5351537","0.5287469","0.5258017","0.5232318","0.5227428","0.52260095","0.5220578","0.5220438","0.5192999","0.5192457","0.5192457","0.51861155","0.5176782","0.51766866","0.5176248","0.5158793","0.51582414","0.5155939","0.5149196","0.5147057","0.51456314","0.51355493","0.51245093","0.5117279","0.51166415","0.5115797","0.51154387","0.51154387","0.5113543","0.5113506","0.51134807","0.5111143","0.5097206","0.50964427","0.50925756","0.5089105","0.50847226","0.507774","0.5075767","0.5075767","0.5068311","0.5049579","0.504794","0.5042837","0.5037023","0.5031544","0.5030641","0.5029748","0.5024592","0.5021359","0.5019489","0.5017116","0.5016589","0.5012673","0.500813","0.5007556","0.50069153","0.50058514","0.50008035","0.49997318","0.49973342","0.49937835","0.49910378","0.4989601","0.4989512","0.4987257","0.4986713","0.49846396","0.49792436","0.49737445","0.49736184","0.49679664","0.49671105","0.49664998","0.49664998","0.49647248","0.4961266","0.49602178","0.49584705","0.4952586","0.4952137","0.49499983","0.49496457","0.49475092","0.49431115","0.49426806","0.4939316","0.49345532","0.49333897","0.49327186","0.49270532"],"string":"[\n \"0.62754345\",\n \"0.60140204\",\n \"0.5792275\",\n \"0.57416606\",\n \"0.55366963\",\n \"0.55194634\",\n \"0.54221153\",\n \"0.5412391\",\n \"0.5388891\",\n \"0.53688395\",\n \"0.5351537\",\n \"0.5287469\",\n \"0.5258017\",\n \"0.5232318\",\n \"0.5227428\",\n \"0.52260095\",\n \"0.5220578\",\n \"0.5220438\",\n \"0.5192999\",\n \"0.5192457\",\n \"0.5192457\",\n \"0.51861155\",\n \"0.5176782\",\n \"0.51766866\",\n \"0.5176248\",\n \"0.5158793\",\n \"0.51582414\",\n \"0.5155939\",\n \"0.5149196\",\n \"0.5147057\",\n \"0.51456314\",\n \"0.51355493\",\n \"0.51245093\",\n \"0.5117279\",\n \"0.51166415\",\n \"0.5115797\",\n \"0.51154387\",\n \"0.51154387\",\n \"0.5113543\",\n \"0.5113506\",\n \"0.51134807\",\n \"0.5111143\",\n \"0.5097206\",\n \"0.50964427\",\n \"0.50925756\",\n \"0.5089105\",\n \"0.50847226\",\n \"0.507774\",\n \"0.5075767\",\n \"0.5075767\",\n \"0.5068311\",\n \"0.5049579\",\n \"0.504794\",\n \"0.5042837\",\n \"0.5037023\",\n \"0.5031544\",\n \"0.5030641\",\n \"0.5029748\",\n \"0.5024592\",\n \"0.5021359\",\n \"0.5019489\",\n \"0.5017116\",\n \"0.5016589\",\n \"0.5012673\",\n \"0.500813\",\n \"0.5007556\",\n \"0.50069153\",\n \"0.50058514\",\n \"0.50008035\",\n \"0.49997318\",\n \"0.49973342\",\n \"0.49937835\",\n \"0.49910378\",\n \"0.4989601\",\n \"0.4989512\",\n \"0.4987257\",\n \"0.4986713\",\n \"0.49846396\",\n \"0.49792436\",\n \"0.49737445\",\n \"0.49736184\",\n \"0.49679664\",\n \"0.49671105\",\n \"0.49664998\",\n \"0.49664998\",\n \"0.49647248\",\n \"0.4961266\",\n \"0.49602178\",\n \"0.49584705\",\n \"0.4952586\",\n \"0.4952137\",\n \"0.49499983\",\n \"0.49496457\",\n \"0.49475092\",\n \"0.49431115\",\n \"0.49426806\",\n \"0.4939316\",\n \"0.49345532\",\n \"0.49333897\",\n \"0.49327186\",\n \"0.49270532\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11599,"cells":{"query":{"kind":"string","value":"AppendCols(numCols=1) > bool Exactly the same as AppendRows() but for columns."},"document":{"kind":"string","value":"def AppendCols(self, numCols=1): # real signature unknown; restored from __doc__\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add_new_cols(cat, prefix=\"\", floatcols=None, boolcols=None):\n\t\n\tif floatcols != None:\n\t\tfor col in floatcols:\n\t\t\tcat.add_column(astropy.table.MaskedColumn(name=prefix+col, dtype=float, length=len(cat)))\n\t\t\tcat[prefix+col].mask = [True] * len(cat)\n\tif boolcols != None:\n\t\tfor col in boolcols:\n\t\t\tcat.add_column(astropy.table.MaskedColumn(name=prefix+col, dtype=bool, length=len(cat)))\n\t\t\tcat[prefix+col].mask = [True] * len(cat)","def add_column(self):\n if len(self._grid) == 1:\n self._grid[0].append(None)\n elif len(self._grid) > 1:\n for i in range(len(self._grid)):\n self._grid[i].append(None)\n return True","def getNumCols(self):\n return self.__cols","def add_column(matrix):\n import numpy as np\n shape = np.shape(matrix)\n if matrix is np.zeros(shape):\n pass","def appendColumn(self, contents = None):\n\n\t\t\t\t#Find the last column\n\t\t\t\tcolumn = len(tuple(self.thing.iter_cols())) + 1\n\n\t\t\t\t#Write to cells\n\t\t\t\tif ((contents != None) and (len(contents) != 0)):\n\t\t\t\t\tfor row, item in enumerate(contents):\n\t\t\t\t\t\tself.setCell(row + 1, column, item)\n\t\t\t\telse:\n\t\t\t\t\tself.setCell(1, column, \" \")","def add_columns(array, cols=1):\n # TODO: error handling\n rows = array.shape[0]\n new_cols = np.empty((rows, cols), dtype=np.object)\n new_array = np.concatenate((array, new_cols),\n axis=1)\n return new_array","def test_num_columns(self):\n pass","def AppendRows(self, numRows = 1):\n for i in range(numRows):\n self.data = numpy.vstack((self.data,\n numpy.array([''] * self.data.shape[1], dtype = numpy.object),\n ))\n self.rowmask = numpy.append(self.rowmask, numpy.zeros((numRows,), dtype = numpy.bool))\n\n msg = wx.grid.GridTableMessage(self,\n wx.grid.GRIDTABLE_NOTIFY_ROWS_APPENDED,\n numRows)\n #if not self._batchcount:\n # self.GetView().ProcessTableMessage(msg)\n self.GetView().ProcessTableMessage(msg)\n return True","def append_columns(classdict, shape=()):\n heavy = common.heavy\n for (itype, type_) in enumerate(sorted(type_info)):\n if not heavy and type_ in heavy_types:\n continue # skip heavy type in non-heavy mode\n colpos = itype + 1\n colname = 'c_%s' % type_\n if type_ == 'enum':\n base = tb.Atom.from_sctype(sctype_from_type[type_])\n col = tb.EnumCol(enum, enum(0), base, shape=shape, pos=colpos)\n else:\n sctype = sctype_from_type[type_]\n dtype = np.dtype((sctype, shape))\n col = tb.Col.from_dtype(dtype, pos=colpos)\n classdict[colname] = col\n ncols = colpos\n return ncols","def num_cols(self):\n return len(self.column_names())","def no_of_columns(self): \n return len(self.columns) + (1 if self.serialize else 0)","def newrow(self):\n maxlen = 0\n for colbuf in self.colbufs:\n maxlen = max(maxlen, len(colbuf))\n\n for i in range(maxlen):\n first = True\n for colbuf in self.colbufs:\n if first:\n first = False\n else:\n sys.stdout.write(self.sepstr)\n if i < len(colbuf):\n sys.stdout.write(colbuf[i])\n else:\n sys.stdout.write(\" \"*self.colwidth)\n sys.stdout.write(\"\\n\")\n\n self.colbufs = []\n for i in range(self.ncolumns):\n self.colbufs.append([])","def add_feature_columns(self, feature_columns: typing.List[str]):\n self.feature_columns += feature_columns","def _add_cols(df: pandas.DataFrame, scope = (globals(), locals())) -> None:\n command : str = input(\"\\nAdd a column:\\n\")\n if command.lower() in ['n', 'no', 'quit()', 'exit', 'return']:\n return\n\n col_name : str = command[ \\\n re.search(r'[\\w\\.\\(\\)]+', command).start(): \\\n re.search(r'[\\w\\.\\(\\)]+', command).end() \\\n ]\n # new column's name\n\n arg : str = command[re.search(r'[=,;]', command).end():]\n # the new column's \"function\"\n ref_cols = re.findall(r'(?<=\\{)\\w[\\w\\.\\(\\)]*(?=\\})', arg)\n # df column names that are referenced to create new columns\n\n for i in range(len(ref_cols)):\n arg = re.sub(\n f'{{{ref_cols[i]}}}',\n f'df[\\'{ref_cols[i]}\\']',\n arg\n )\n # substituting references\n\n scope[0].update(globals())\n scope[1].update(locals())\n\n col_arg = eval(arg, scope[0], scope[1])\n # pandas.Series for type checking\n df[col_name] = col_arg\n # creating column\n\n more : str = input(\"\\nWould you like to add more columns?\\n\")\n if more.lower() in ['y', 'yes', 'continue', 'true']:\n return _add_cols(df)\n return","def append_columns(cls, columns, grid=None, grid_url=None):\n grid_id = parse_grid_id_args(grid, grid_url)\n\n grid_ops.ensure_uploaded(grid_id)\n\n # Verify unique column names\n column_names = [c.name for c in columns]\n if grid:\n existing_column_names = [c.name for c in grid]\n column_names.extend(existing_column_names)\n duplicate_name = utils.get_first_duplicate(column_names)\n if duplicate_name:\n err = exceptions.NON_UNIQUE_COLUMN_MESSAGE.format(duplicate_name)\n raise exceptions.InputError(err)\n\n # This is sorta gross, we need to double-encode this.\n body = {\"cols\": _json.dumps(columns, cls=PlotlyJSONEncoder)}\n fid = grid_id\n response = v2.grids.col_create(fid, body)\n parsed_content = response.json()\n\n cls._fill_in_response_column_ids(columns, parsed_content[\"cols\"], fid)\n\n if grid:\n grid.extend(columns)","def addemptycolumn(self, colname, coltype):\n setattr(self,colname,N.zeros((len(self),),coltype))\n self._modflag=True\n self._type[colname]=coltype\n\n #Looks strange here because we count columns from 1 but\n #Python counts them from 0\n self._ncolumns+=1\n self._d[colname]=self._ncolumns\n self._colnames.append(colname)\n self._header+='# %d %s\\n'%(self._ncolumns,colname)","def configcols(self,cols,coltype=None,colformat=None,visible=None,latexphantomflag=False,defaultvalue=None):\n\n if type(cols) is str:\n cols=[cols,]\n for col in cols: # test if the column already exist\n if col == None: continue\n if col in self.colinfo:\n newcolflag=0\n oldcoltype=self.colinfo[col]['type'] # save the previous setting\n # if the coltype is changed, and no new format given: give it default format\n if (not (oldcoltype == coltype)) and colformat==None and (not (coltype==None)):\n colformat='default'\n else:\n newcolflag=1\n self.cols.append(col)\n self.colinfo[col]={} #initialize colinfo\n oldcoltype=''\n # as default: columns are type string\n if coltype==None:\n coltype='s'\n # new col: give it default format if none given\n if colformat==None:\n colformat='default'\n # set the type self.colinfo[col]['type'] and self.colinfo[col]['format']\n self.setcoltype(col,coltype,colformat=colformat)\n\n # set if the column is visible, i.e. if it is printed by default by printtexttable\n self.setcol2visible(col,visible)\n\n # latex table: set if instead of spaces you want to use phantom{0}\n self.setcol2latexphantom(col,latexphantomflag)\n\n # set column to the defaultvalue if necessary\n if newcolflag or (not defaultvalue==None):\n self.setcol2value(col,defaultvalue)\n self.colinfo[col]['autoformat']='%s'\n else:\n # redo typecasting if necessary\n if (not newcolflag) and (not coltype==oldcoltype) :\n self.redotypecasting(col)","def augment (self, *args):\n cols = list(self.columns())\n for aug in args:\n try:\n cols.extend(aug.columns())\n except AttributeError:\n cols.append(aug)\n return Matrix(*cols, columns=True)","def cols(self, col):\n self.col += col","def setOptionalColumns(self, colnames):\n # Make sure all column names are lower case so comparisons in _TableRow\n # are not case sensitive. From a modularity standpoint, this should be\n # done in _TableRow, but it is more efficient to do it here, since the\n # conversion need be done only once.\n if colnames == [0]:\n self.optional_cols = colnames\n else:\n self.optional_cols = [colname.lower() for colname in colnames]","def _modify_columns(self, cols, X, y=None):","def columns(self):\n \n pass","def has_group_cols(self):\n return len(self.group_cols) != 0","def add_columns(self, **columns):\n return self.as_dataframe(self.data.assign(**columns))","def add_col(self):\r\n reader = csv.reader(open(self.in_csvfile, newline=''))\r\n rows = list(reader)\r\n rows[0].append(self.col_name)\r\n for i in range(1, len(rows)):\r\n rows[i].append(self.cell_filler(rows[i]))\r\n writer = csv.writer(open(self.out_csvfile, 'w', newline=''))\r\n writer.writerows(rows)","def number_of_columns(self):\n return len(self._columns)","def GetNumColumns(self):\n return len(self.columns)","def AddColumnsInRow(self, r, ncol):\n return _table.Table_AddColumnsInRow(self, r, ncol)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def ncolumns(self):\n return len(self.__column_list)","def num_cols(self):\n return (len(self.rows[0]))","def columns(self):\n return NotImplemented","def add_columns(self, specs, before=False):\n import hxl.filters\n return hxl.filters.AddColumnsFilter(self, specs=specs, before=before)","def setNumCols(serDisplay, cols):\n cmd = array.array('B', (124,0))\n if (cols == 20):\n cmd[1] = 3\n else:\n if (cols != 16):\n print(\"WARNING: num columns of %d not valid - must be 16 or 20. Defaulting to 16\", cols)\n cmd[1] = 6 \n writeToDisplay(serDisplay, cmd.tostring())","def get_num_cols(self):\n return self._num_cols","def cols_valid(self,\n df: pd.DataFrame,\n req_cols: set) -> bool:\n missing_cols = req_cols.difference(df.columns)\n\n if len(missing_cols) > 0:\n logging.error(f\"{missing_cols} columns required but missing\")\n return False\n\n return True","def add_widget_multicolumn(self,name, widget_dict, sizes=None):\n if recoverer is not None:\n for widget_name in widget_dict:\n widget_dict[widget_name] = self.stato_iniziale(widget_name, widget_dict[widget_name])\n\n self.widget_dict[name] = partial(st_functional_columns, widget_dict, sizes)","def test_addheader(self):\n datasets = [pd.DataFrame(index=range(100),columns=range(54)) for b in range(10)]\n datasetsnew = tutorial_pamap2.addheader(datasets)\n test = datasetsnew[0].shape == datasets[0].shape\n assert test","def GetNumberCols(self):\n return len(self.__colsKey)","def getNoOfCols(self):\n return _patchExtractor.patchExtractor_getNoOfCols(self)","def num_cols(self):\n return len(self.rows[0])","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\n return (self.GetNumberRows() + numRows)","def _add_necessary_columns(args, custom_columns):\n # we need to add the variant's chrom, start and gene if \n # not already there.\n if custom_columns.find(\"gene\") < 0:\n custom_columns += \", gene\"\n if custom_columns.find(\"start\") < 0:\n custom_columns += \", start\"\n \n return custom_columns","def setOutputCols(self, *value):\n if len(value) == 1 and type(value[0]) == list:\n return self._set(outputCols=value[0])\n else:\n return self._set(outputCols=list(value))","def cols(self):\n\n return []","def n_cols(self):\n\n return len(self.plaincolumns)","def add_column(self, column_type, column_span=1):\n if column_type == columnTYPE.TIMELINE:\n self._columns.append(\n GraphVisualizerTimeline(\n self._layout, self._next_column, column_span)\n )\n elif column_type == columnTYPE.CONNECTED:\n self._columns.append(\n GraphVisualizerConnectedcolumn(\n self._layout, self._next_column, column_span)\n )\n\n self._next_column += column_span\n\n return len(self._columns) - 1","def concatMatrix(self, a, l, cols):\n l_i = l * np.identity(cols)\n concat = np.concatenate((a, l_i))\n\n return concat","def SetTableColumns(this, a_cols):\n this.cols = a_cols","def is_row_column_headers_visible(self):\n return self.container['is_row_column_headers_visible']","def AddColumnInfo(self, colInfo):\r\n \r\n self._columns.append(colInfo)\r\n self._total_col_width += colInfo.GetWidth()\r\n self._owner.AdjustMyScrollbars()\r\n self._owner._dirty = True","def __append_columns(self, new_dataframe):\n self.dataframe = pd.merge(self.dataframe, new_dataframe)","def columns(self, *args):\n column_set = set(self._columns)\n for c in args:\n if c in column_set:\n continue\n else:\n self._columns.append(c)\n # column_set.add(c) # FIXME failing tests\n return self","def append(self, row_or_table):\n row, table, inc = row_or_table, row_or_table, 1\n if not row:\n return\n if isinstance(table, Table):\n row, inc = table.get_columns(*self.column_labels), table.num_rows\n for i, column in enumerate(self._columns):\n self._columns[column] = np.append(self[column], row[i])\n self._num_rows = self.num_rows + inc\n return self","def _cols_if_none(X, self_cols):\n return X.columns.tolist() if not self_cols else self_cols","def add_column(\n self,\n header: \"RenderableType\" = \"\",\n footer: \"RenderableType\" = \"\",\n *,\n header_style: Optional[StyleType] = None,\n footer_style: Optional[StyleType] = None,\n style: Optional[StyleType] = None,\n justify: \"JustifyMethod\" = \"left\",\n vertical: \"VerticalAlignMethod\" = \"top\",\n overflow: \"OverflowMethod\" = \"ellipsis\",\n width: Optional[int] = None,\n min_width: Optional[int] = None,\n max_width: Optional[int] = None,\n ratio: Optional[int] = None,\n no_wrap: bool = False,\n ) -> None:\n\n column = Column(\n _index=len(self.columns),\n header=header,\n footer=footer,\n header_style=header_style or \"\",\n footer_style=footer_style or \"\",\n style=style or \"\",\n justify=justify,\n vertical=vertical,\n overflow=overflow,\n width=width,\n min_width=min_width,\n max_width=max_width,\n ratio=ratio,\n no_wrap=no_wrap,\n )\n self.columns.append(column)","def getColumnCount(self) -> int:\n ...","def is_row_column_headers_visible(self, is_row_column_headers_visible):\n\n self.container['is_row_column_headers_visible'] = is_row_column_headers_visible","def are_there_available_columns_to_play(self):\n available_columns = self.get_available_columns()\n return self._state.n_neutral_markers != 3 and len(available_columns) > 0","def _addcolumns(self, columnname, columndata=\"\"):\n self[columnname] = columndata","def test_add_column(self):\n self.spy_on(DataGrid.add_column)\n\n DataGridColumnsHook(extension=self.extension,\n datagrid_cls=DataGrid,\n columns=[Column(id='sandbox')])\n\n self.assertTrue(DataGrid.add_column.called)","def columns(self, new_columns: ColumnT) -> None:\n new_columns2: ndarray = init.check_column_validity(new_columns)\n len_new: int = len(new_columns2)\n len_old: int = len(self._columns)\n if len_new != len_old:\n raise ValueError(f'There are {len_old} columns in the DataFrame. '\n f'You provided {len_new}.')\n\n new_column_info: ColInfoT = {}\n for old_col, new_col in zip(self._columns, new_columns2):\n new_column_info[new_col] = utils.Column(*self._column_info[old_col].values)\n\n self._column_info = new_column_info\n self._columns = new_columns2","def has_headers(self):\n for column in self.columns:\n if column.header:\n return True\n return False","def test_provide_column_types():\n expected_column_types = (bool, float)\n data = ((1, 1.0), (0, 2))\n headers = (\"a\", \"b\")\n\n def preprocessor(data, headers, column_types=(), **_):\n assert expected_column_types == column_types\n return data, headers\n\n format_output(\n data,\n headers,\n \"csv\",\n column_types=expected_column_types,\n preprocessors=(preprocessor,),\n )","def has_columns(df, columns):\n result = True\n for column in columns:\n if column not in df.columns:\n print(\"Missing column: {} in DataFrame\".format(column))\n result = False\n\n return result","def becomes_column(self):\n row_type = self.get_type()\n # Note: \"begin repeat\" does become a column\n non_columns = ('begin group', 'end group', 'end repeat')\n return row_type not in non_columns","def test_add_column(self):\n name_column = Varchar()\n name_column._meta.name = \"name\"\n\n genre_column = Varchar()\n genre_column._meta.name = \"genre\"\n\n schema: t.List[DiffableTable] = [\n DiffableTable(\n class_name=\"Band\",\n tablename=\"band\",\n columns=[name_column, genre_column],\n )\n ]\n schema_snapshot: t.List[DiffableTable] = [\n DiffableTable(\n class_name=\"Band\",\n tablename=\"band\",\n columns=[name_column],\n )\n ]\n\n schema_differ = SchemaDiffer(\n schema=schema, schema_snapshot=schema_snapshot, auto_input=\"y\"\n )\n\n self.assertTrue(len(schema_differ.add_columns.statements) == 1)\n self.assertEqual(\n schema_differ.add_columns.statements[0],\n \"manager.add_column(table_class_name='Band', tablename='band', column_name='genre', db_column_name='genre', column_class_name='Varchar', column_class=Varchar, params={'length': 255, 'default': '', 'null': False, 'primary_key': False, 'unique': False, 'index': False, 'index_method': IndexMethod.btree, 'choices': None, 'db_column_name': None})\", # noqa\n )","def isEmptyColumn(self, j, rowns ):\n for i in range(0,rowns) :\n if not self.isEmpty(i,j):\n return False\n return True","def IsColumnMode(self):\n return self.VertEdit.Enabled","def add_blank_data_column(self):\n\n header_title, ok_pressed = QInputDialog.getText(self, \"Add Column\", \"Enter heading for the column:\",\n QLineEdit.Normal, \"\")\n if ok_pressed and header_title != '':\n # print(header_title)\n\n default_value, set_default_pressed = QInputDialog.getText(self, \"Set Default Value\",\n \"Enter default value to set for column if any:\",\n QLineEdit.Normal, \"\")\n\n row_count = self.csv_data_table.rowCount()\n last_column_count = self.csv_data_table.columnCount()\n self.csv_data_table.insertColumn(last_column_count)\n for empty_row in range(0, row_count):\n item = QTableWidgetItem(default_value)\n self.csv_data_table.setItem(empty_row, last_column_count, item)\n\n # TODO: fix untraced bug present in show/hide columns\n self.column_headers.append(header_title)\n self.column_headers_all.append(header_title)\n # print(self.column_headers)\n # print(self.column_headers_all)\n self.csv_data_table.setHorizontalHeaderLabels(self.column_headers)","def setNumColumns(self, num):\n ExportDialog.numColumns = num","def _add_column(self, column):\n if column is None:\n column = len(self._columns)\n\n if column in self._columns:\n raise ValueError(f\"Duplicate column name: {column}\")\n\n if isinstance(column, int):\n assert column >= len(self._columns)\n for empty in range(len(self._columns), column):\n self._add_column(empty)\n\n self._columns.append(column)\n for idx in self.index:\n row = self._data[idx]\n row.append(None)\n\n return len(self._columns) - 1","def getOutputCols(self):\n if len(self.getOrDefault(self.outputCols)) == 0:\n return [\"finished_\" + input_col for input_col in self.getInputCols()]\n else:\n return self.getOrDefault(self.outputCols)","def AddColumnInfo(self, colInfo):\r\n\r\n self._header_win.AddColumnInfo(colInfo)\r\n self.DoHeaderLayout()","def add_line(self, cols, values, units=None, desc=None, fmt=None,\n match=None):\n if self.lines is None:\n types = []\n for val in values:\n if is_int(val):\n types.append('<i4')\n elif is_float(val):\n types.append('<f8')\n else:\n types.append(STR_DTYPE)\n self.lines = Table(rows=[values], names=cols, dtype=types,\n masked=True)\n if units is not None:\n for colname, unit in zip(self.lines.colnames, units):\n self.lines[colname].unit = unit\n if desc is not None:\n for colname, d in zip(self.lines.colnames, desc):\n self.lines[colname].description = d\n if fmt is not None:\n for colname, f in zip(self.lines.colnames, fmt):\n self.lines[colname].format = f\n else:\n # add new columns\n if units is None:\n units = [None] * len(cols)\n if desc is None:\n desc = [None] * len(cols)\n if fmt is None:\n fmt = [None] * len(cols)\n for col, val, unit, d, f in zip(cols, values, units, desc, fmt):\n if col not in self.lines.colnames:\n nlines = len(self.lines)\n if is_int(val):\n typ = '<i4'\n elif is_float(val):\n typ = '<f8'\n else:\n typ = STR_DTYPE\n col = MaskedColumn(ma.masked_array(np.empty(nlines),\n mask=np.ones(nlines)),\n name=col, dtype=typ, unit=unit,\n description=d, format=f)\n self.lines.add_column(col)\n\n if match is not None:\n if len(match) == 2:\n matchkey, matchval = match\n add_if_not_matched = False\n else:\n matchkey, matchval, add_if_not_matched = match\n\n if match is not None and matchkey in self.lines.colnames:\n l = np.argwhere(self.lines[matchkey] == matchval)\n if len(l) > 0:\n for col, val, unit in zip(cols, values, units):\n if unit is None or unit == self.lines[col].unit:\n self.lines[col][l] = val\n else:\n self.lines[col][l] = (val * unit).to(\n self.lines[col].unit).value\n return\n else:\n if not add_if_not_matched:\n return\n\n # add new row\n ncol = len(self.lines.colnames)\n row = [None] * ncol\n mask = np.ones(ncol)\n for col, val, unit in zip(cols, values, units):\n i = self.lines.colnames.index(col)\n if unit is None or unit == self.lines[col].unit:\n row[i] = val\n else:\n row[i] = (val * unit).to(self.lines[col].unit).value\n mask[i] = 0\n self.lines.add_row(row, mask=mask)","def setAllColumns(self, newAllColumns):\n \n pass","def _these_columns_cannot_annotate_exp_cons(self):\n _cols = set([]) #\n for param_name, req_cols in self.required_columns.items():\n _cols |= req_cols\n\n return _cols | self.other_useful_columns","def set_columns(self, columns):\n self.columns = columns","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","def _validate_cols(cols):\n\n\tif cols is not None and len(cols) < 2:\n\t\traise ValueError('too few features')","def ComputeUnshownColumns(results, shown_columns, config, built_in_cols):\n unshown_set = set() # lowercases column names\n unshown_list = [] # original-case column names\n shown_set = {col.lower() for col in shown_columns}\n labels_already_seen = set() # whole labels, original case\n\n def _MaybeAddLabel(label_name):\n \"\"\"Add the key part of the given label if needed.\"\"\"\n if label_name.lower() in labels_already_seen:\n return\n labels_already_seen.add(label_name.lower())\n if '-' in label_name:\n col, _value = label_name.split('-', 1)\n _MaybeAddCol(col)\n\n def _MaybeAddCol(col):\n if col.lower() not in shown_set and col.lower() not in unshown_set:\n unshown_list.append(col)\n unshown_set.add(col.lower())\n\n # The user can always add any of the default columns.\n for col in config.default_col_spec.split():\n _MaybeAddCol(col)\n\n # The user can always add any of the built-in columns.\n for col in built_in_cols:\n _MaybeAddCol(col)\n\n # The user can add a column for any well-known labels\n for wkl in config.well_known_labels:\n _MaybeAddLabel(wkl.label)\n\n phase_names = set(itertools.chain.from_iterable(\n (phase.name.lower() for phase in result.phases) for result in results))\n # The user can add a column for any custom field\n field_ids_alread_seen = set()\n for fd in config.field_defs:\n field_lower = fd.field_name.lower()\n field_ids_alread_seen.add(fd.field_id)\n if fd.is_phase_field:\n for name in phase_names:\n phase_field_col = name + '.' + field_lower\n if (phase_field_col not in shown_set and\n phase_field_col not in unshown_set):\n unshown_list.append(phase_field_col)\n unshown_set.add(phase_field_col)\n elif field_lower not in shown_set and field_lower not in unshown_set:\n unshown_list.append(fd.field_name)\n unshown_set.add(field_lower)\n\n if fd.field_type == tracker_pb2.FieldTypes.APPROVAL_TYPE:\n approval_lower_approver = (\n field_lower + tracker_constants.APPROVER_COL_SUFFIX)\n if (approval_lower_approver not in shown_set and\n approval_lower_approver not in unshown_set):\n unshown_list.append(\n fd.field_name + tracker_constants.APPROVER_COL_SUFFIX)\n unshown_set.add(approval_lower_approver)\n\n # The user can add a column for any key-value label or field in the results.\n for r in results:\n for label_name in tracker_bizobj.GetLabels(r):\n _MaybeAddLabel(label_name)\n for field_value in r.field_values:\n if field_value.field_id not in field_ids_alread_seen:\n field_ids_alread_seen.add(field_value.field_id)\n fd = tracker_bizobj.FindFieldDefByID(field_value.field_id, config)\n if fd: # could be None for a foreign field, which we don't display.\n field_lower = fd.field_name.lower()\n if field_lower not in shown_set and field_lower not in unshown_set:\n unshown_list.append(fd.field_name)\n unshown_set.add(field_lower)\n\n return sorted(unshown_list)","def GetColumnCount(self):\r\n\r\n return len(self._columns)","def test_overall_report_columns():\n assert (len(overall_data['columns']) == 31)","def add_column(self, name, type):\n raise NotImplementedError(\n \"Please implement the 'add_column' method in a derived class.\")","def getInputCols(self):\n return self.getOrDefault(self.inputCols)"],"string":"[\n \"def add_new_cols(cat, prefix=\\\"\\\", floatcols=None, boolcols=None):\\n\\t\\n\\tif floatcols != None:\\n\\t\\tfor col in floatcols:\\n\\t\\t\\tcat.add_column(astropy.table.MaskedColumn(name=prefix+col, dtype=float, length=len(cat)))\\n\\t\\t\\tcat[prefix+col].mask = [True] * len(cat)\\n\\tif boolcols != None:\\n\\t\\tfor col in boolcols:\\n\\t\\t\\tcat.add_column(astropy.table.MaskedColumn(name=prefix+col, dtype=bool, length=len(cat)))\\n\\t\\t\\tcat[prefix+col].mask = [True] * len(cat)\",\n \"def add_column(self):\\n if len(self._grid) == 1:\\n self._grid[0].append(None)\\n elif len(self._grid) > 1:\\n for i in range(len(self._grid)):\\n self._grid[i].append(None)\\n return True\",\n \"def getNumCols(self):\\n return self.__cols\",\n \"def add_column(matrix):\\n import numpy as np\\n shape = np.shape(matrix)\\n if matrix is np.zeros(shape):\\n pass\",\n \"def appendColumn(self, contents = None):\\n\\n\\t\\t\\t\\t#Find the last column\\n\\t\\t\\t\\tcolumn = len(tuple(self.thing.iter_cols())) + 1\\n\\n\\t\\t\\t\\t#Write to cells\\n\\t\\t\\t\\tif ((contents != None) and (len(contents) != 0)):\\n\\t\\t\\t\\t\\tfor row, item in enumerate(contents):\\n\\t\\t\\t\\t\\t\\tself.setCell(row + 1, column, item)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.setCell(1, column, \\\" \\\")\",\n \"def add_columns(array, cols=1):\\n # TODO: error handling\\n rows = array.shape[0]\\n new_cols = np.empty((rows, cols), dtype=np.object)\\n new_array = np.concatenate((array, new_cols),\\n axis=1)\\n return new_array\",\n \"def test_num_columns(self):\\n pass\",\n \"def AppendRows(self, numRows = 1):\\n for i in range(numRows):\\n self.data = numpy.vstack((self.data,\\n numpy.array([''] * self.data.shape[1], dtype = numpy.object),\\n ))\\n self.rowmask = numpy.append(self.rowmask, numpy.zeros((numRows,), dtype = numpy.bool))\\n\\n msg = wx.grid.GridTableMessage(self,\\n wx.grid.GRIDTABLE_NOTIFY_ROWS_APPENDED,\\n numRows)\\n #if not self._batchcount:\\n # self.GetView().ProcessTableMessage(msg)\\n self.GetView().ProcessTableMessage(msg)\\n return True\",\n \"def append_columns(classdict, shape=()):\\n heavy = common.heavy\\n for (itype, type_) in enumerate(sorted(type_info)):\\n if not heavy and type_ in heavy_types:\\n continue # skip heavy type in non-heavy mode\\n colpos = itype + 1\\n colname = 'c_%s' % type_\\n if type_ == 'enum':\\n base = tb.Atom.from_sctype(sctype_from_type[type_])\\n col = tb.EnumCol(enum, enum(0), base, shape=shape, pos=colpos)\\n else:\\n sctype = sctype_from_type[type_]\\n dtype = np.dtype((sctype, shape))\\n col = tb.Col.from_dtype(dtype, pos=colpos)\\n classdict[colname] = col\\n ncols = colpos\\n return ncols\",\n \"def num_cols(self):\\n return len(self.column_names())\",\n \"def no_of_columns(self): \\n return len(self.columns) + (1 if self.serialize else 0)\",\n \"def newrow(self):\\n maxlen = 0\\n for colbuf in self.colbufs:\\n maxlen = max(maxlen, len(colbuf))\\n\\n for i in range(maxlen):\\n first = True\\n for colbuf in self.colbufs:\\n if first:\\n first = False\\n else:\\n sys.stdout.write(self.sepstr)\\n if i < len(colbuf):\\n sys.stdout.write(colbuf[i])\\n else:\\n sys.stdout.write(\\\" \\\"*self.colwidth)\\n sys.stdout.write(\\\"\\\\n\\\")\\n\\n self.colbufs = []\\n for i in range(self.ncolumns):\\n self.colbufs.append([])\",\n \"def add_feature_columns(self, feature_columns: typing.List[str]):\\n self.feature_columns += feature_columns\",\n \"def _add_cols(df: pandas.DataFrame, scope = (globals(), locals())) -> None:\\n command : str = input(\\\"\\\\nAdd a column:\\\\n\\\")\\n if command.lower() in ['n', 'no', 'quit()', 'exit', 'return']:\\n return\\n\\n col_name : str = command[ \\\\\\n re.search(r'[\\\\w\\\\.\\\\(\\\\)]+', command).start(): \\\\\\n re.search(r'[\\\\w\\\\.\\\\(\\\\)]+', command).end() \\\\\\n ]\\n # new column's name\\n\\n arg : str = command[re.search(r'[=,;]', command).end():]\\n # the new column's \\\"function\\\"\\n ref_cols = re.findall(r'(?<=\\\\{)\\\\w[\\\\w\\\\.\\\\(\\\\)]*(?=\\\\})', arg)\\n # df column names that are referenced to create new columns\\n\\n for i in range(len(ref_cols)):\\n arg = re.sub(\\n f'{{{ref_cols[i]}}}',\\n f'df[\\\\'{ref_cols[i]}\\\\']',\\n arg\\n )\\n # substituting references\\n\\n scope[0].update(globals())\\n scope[1].update(locals())\\n\\n col_arg = eval(arg, scope[0], scope[1])\\n # pandas.Series for type checking\\n df[col_name] = col_arg\\n # creating column\\n\\n more : str = input(\\\"\\\\nWould you like to add more columns?\\\\n\\\")\\n if more.lower() in ['y', 'yes', 'continue', 'true']:\\n return _add_cols(df)\\n return\",\n \"def append_columns(cls, columns, grid=None, grid_url=None):\\n grid_id = parse_grid_id_args(grid, grid_url)\\n\\n grid_ops.ensure_uploaded(grid_id)\\n\\n # Verify unique column names\\n column_names = [c.name for c in columns]\\n if grid:\\n existing_column_names = [c.name for c in grid]\\n column_names.extend(existing_column_names)\\n duplicate_name = utils.get_first_duplicate(column_names)\\n if duplicate_name:\\n err = exceptions.NON_UNIQUE_COLUMN_MESSAGE.format(duplicate_name)\\n raise exceptions.InputError(err)\\n\\n # This is sorta gross, we need to double-encode this.\\n body = {\\\"cols\\\": _json.dumps(columns, cls=PlotlyJSONEncoder)}\\n fid = grid_id\\n response = v2.grids.col_create(fid, body)\\n parsed_content = response.json()\\n\\n cls._fill_in_response_column_ids(columns, parsed_content[\\\"cols\\\"], fid)\\n\\n if grid:\\n grid.extend(columns)\",\n \"def addemptycolumn(self, colname, coltype):\\n setattr(self,colname,N.zeros((len(self),),coltype))\\n self._modflag=True\\n self._type[colname]=coltype\\n\\n #Looks strange here because we count columns from 1 but\\n #Python counts them from 0\\n self._ncolumns+=1\\n self._d[colname]=self._ncolumns\\n self._colnames.append(colname)\\n self._header+='# %d %s\\\\n'%(self._ncolumns,colname)\",\n \"def configcols(self,cols,coltype=None,colformat=None,visible=None,latexphantomflag=False,defaultvalue=None):\\n\\n if type(cols) is str:\\n cols=[cols,]\\n for col in cols: # test if the column already exist\\n if col == None: continue\\n if col in self.colinfo:\\n newcolflag=0\\n oldcoltype=self.colinfo[col]['type'] # save the previous setting\\n # if the coltype is changed, and no new format given: give it default format\\n if (not (oldcoltype == coltype)) and colformat==None and (not (coltype==None)):\\n colformat='default'\\n else:\\n newcolflag=1\\n self.cols.append(col)\\n self.colinfo[col]={} #initialize colinfo\\n oldcoltype=''\\n # as default: columns are type string\\n if coltype==None:\\n coltype='s'\\n # new col: give it default format if none given\\n if colformat==None:\\n colformat='default'\\n # set the type self.colinfo[col]['type'] and self.colinfo[col]['format']\\n self.setcoltype(col,coltype,colformat=colformat)\\n\\n # set if the column is visible, i.e. if it is printed by default by printtexttable\\n self.setcol2visible(col,visible)\\n\\n # latex table: set if instead of spaces you want to use phantom{0}\\n self.setcol2latexphantom(col,latexphantomflag)\\n\\n # set column to the defaultvalue if necessary\\n if newcolflag or (not defaultvalue==None):\\n self.setcol2value(col,defaultvalue)\\n self.colinfo[col]['autoformat']='%s'\\n else:\\n # redo typecasting if necessary\\n if (not newcolflag) and (not coltype==oldcoltype) :\\n self.redotypecasting(col)\",\n \"def augment (self, *args):\\n cols = list(self.columns())\\n for aug in args:\\n try:\\n cols.extend(aug.columns())\\n except AttributeError:\\n cols.append(aug)\\n return Matrix(*cols, columns=True)\",\n \"def cols(self, col):\\n self.col += col\",\n \"def setOptionalColumns(self, colnames):\\n # Make sure all column names are lower case so comparisons in _TableRow\\n # are not case sensitive. From a modularity standpoint, this should be\\n # done in _TableRow, but it is more efficient to do it here, since the\\n # conversion need be done only once.\\n if colnames == [0]:\\n self.optional_cols = colnames\\n else:\\n self.optional_cols = [colname.lower() for colname in colnames]\",\n \"def _modify_columns(self, cols, X, y=None):\",\n \"def columns(self):\\n \\n pass\",\n \"def has_group_cols(self):\\n return len(self.group_cols) != 0\",\n \"def add_columns(self, **columns):\\n return self.as_dataframe(self.data.assign(**columns))\",\n \"def add_col(self):\\r\\n reader = csv.reader(open(self.in_csvfile, newline=''))\\r\\n rows = list(reader)\\r\\n rows[0].append(self.col_name)\\r\\n for i in range(1, len(rows)):\\r\\n rows[i].append(self.cell_filler(rows[i]))\\r\\n writer = csv.writer(open(self.out_csvfile, 'w', newline=''))\\r\\n writer.writerows(rows)\",\n \"def number_of_columns(self):\\n return len(self._columns)\",\n \"def GetNumColumns(self):\\n return len(self.columns)\",\n \"def AddColumnsInRow(self, r, ncol):\\n return _table.Table_AddColumnsInRow(self, r, ncol)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def ncolumns(self):\\n return len(self.__column_list)\",\n \"def num_cols(self):\\n return (len(self.rows[0]))\",\n \"def columns(self):\\n return NotImplemented\",\n \"def add_columns(self, specs, before=False):\\n import hxl.filters\\n return hxl.filters.AddColumnsFilter(self, specs=specs, before=before)\",\n \"def setNumCols(serDisplay, cols):\\n cmd = array.array('B', (124,0))\\n if (cols == 20):\\n cmd[1] = 3\\n else:\\n if (cols != 16):\\n print(\\\"WARNING: num columns of %d not valid - must be 16 or 20. Defaulting to 16\\\", cols)\\n cmd[1] = 6 \\n writeToDisplay(serDisplay, cmd.tostring())\",\n \"def get_num_cols(self):\\n return self._num_cols\",\n \"def cols_valid(self,\\n df: pd.DataFrame,\\n req_cols: set) -> bool:\\n missing_cols = req_cols.difference(df.columns)\\n\\n if len(missing_cols) > 0:\\n logging.error(f\\\"{missing_cols} columns required but missing\\\")\\n return False\\n\\n return True\",\n \"def add_widget_multicolumn(self,name, widget_dict, sizes=None):\\n if recoverer is not None:\\n for widget_name in widget_dict:\\n widget_dict[widget_name] = self.stato_iniziale(widget_name, widget_dict[widget_name])\\n\\n self.widget_dict[name] = partial(st_functional_columns, widget_dict, sizes)\",\n \"def test_addheader(self):\\n datasets = [pd.DataFrame(index=range(100),columns=range(54)) for b in range(10)]\\n datasetsnew = tutorial_pamap2.addheader(datasets)\\n test = datasetsnew[0].shape == datasets[0].shape\\n assert test\",\n \"def GetNumberCols(self):\\n return len(self.__colsKey)\",\n \"def getNoOfCols(self):\\n return _patchExtractor.patchExtractor_getNoOfCols(self)\",\n \"def num_cols(self):\\n return len(self.rows[0])\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def AppendRows(self, numRows=1): # real signature unknown; restored from __doc__\\n return (self.GetNumberRows() + numRows)\",\n \"def _add_necessary_columns(args, custom_columns):\\n # we need to add the variant's chrom, start and gene if \\n # not already there.\\n if custom_columns.find(\\\"gene\\\") < 0:\\n custom_columns += \\\", gene\\\"\\n if custom_columns.find(\\\"start\\\") < 0:\\n custom_columns += \\\", start\\\"\\n \\n return custom_columns\",\n \"def setOutputCols(self, *value):\\n if len(value) == 1 and type(value[0]) == list:\\n return self._set(outputCols=value[0])\\n else:\\n return self._set(outputCols=list(value))\",\n \"def cols(self):\\n\\n return []\",\n \"def n_cols(self):\\n\\n return len(self.plaincolumns)\",\n \"def add_column(self, column_type, column_span=1):\\n if column_type == columnTYPE.TIMELINE:\\n self._columns.append(\\n GraphVisualizerTimeline(\\n self._layout, self._next_column, column_span)\\n )\\n elif column_type == columnTYPE.CONNECTED:\\n self._columns.append(\\n GraphVisualizerConnectedcolumn(\\n self._layout, self._next_column, column_span)\\n )\\n\\n self._next_column += column_span\\n\\n return len(self._columns) - 1\",\n \"def concatMatrix(self, a, l, cols):\\n l_i = l * np.identity(cols)\\n concat = np.concatenate((a, l_i))\\n\\n return concat\",\n \"def SetTableColumns(this, a_cols):\\n this.cols = a_cols\",\n \"def is_row_column_headers_visible(self):\\n return self.container['is_row_column_headers_visible']\",\n \"def AddColumnInfo(self, colInfo):\\r\\n \\r\\n self._columns.append(colInfo)\\r\\n self._total_col_width += colInfo.GetWidth()\\r\\n self._owner.AdjustMyScrollbars()\\r\\n self._owner._dirty = True\",\n \"def __append_columns(self, new_dataframe):\\n self.dataframe = pd.merge(self.dataframe, new_dataframe)\",\n \"def columns(self, *args):\\n column_set = set(self._columns)\\n for c in args:\\n if c in column_set:\\n continue\\n else:\\n self._columns.append(c)\\n # column_set.add(c) # FIXME failing tests\\n return self\",\n \"def append(self, row_or_table):\\n row, table, inc = row_or_table, row_or_table, 1\\n if not row:\\n return\\n if isinstance(table, Table):\\n row, inc = table.get_columns(*self.column_labels), table.num_rows\\n for i, column in enumerate(self._columns):\\n self._columns[column] = np.append(self[column], row[i])\\n self._num_rows = self.num_rows + inc\\n return self\",\n \"def _cols_if_none(X, self_cols):\\n return X.columns.tolist() if not self_cols else self_cols\",\n \"def add_column(\\n self,\\n header: \\\"RenderableType\\\" = \\\"\\\",\\n footer: \\\"RenderableType\\\" = \\\"\\\",\\n *,\\n header_style: Optional[StyleType] = None,\\n footer_style: Optional[StyleType] = None,\\n style: Optional[StyleType] = None,\\n justify: \\\"JustifyMethod\\\" = \\\"left\\\",\\n vertical: \\\"VerticalAlignMethod\\\" = \\\"top\\\",\\n overflow: \\\"OverflowMethod\\\" = \\\"ellipsis\\\",\\n width: Optional[int] = None,\\n min_width: Optional[int] = None,\\n max_width: Optional[int] = None,\\n ratio: Optional[int] = None,\\n no_wrap: bool = False,\\n ) -> None:\\n\\n column = Column(\\n _index=len(self.columns),\\n header=header,\\n footer=footer,\\n header_style=header_style or \\\"\\\",\\n footer_style=footer_style or \\\"\\\",\\n style=style or \\\"\\\",\\n justify=justify,\\n vertical=vertical,\\n overflow=overflow,\\n width=width,\\n min_width=min_width,\\n max_width=max_width,\\n ratio=ratio,\\n no_wrap=no_wrap,\\n )\\n self.columns.append(column)\",\n \"def getColumnCount(self) -> int:\\n ...\",\n \"def is_row_column_headers_visible(self, is_row_column_headers_visible):\\n\\n self.container['is_row_column_headers_visible'] = is_row_column_headers_visible\",\n \"def are_there_available_columns_to_play(self):\\n available_columns = self.get_available_columns()\\n return self._state.n_neutral_markers != 3 and len(available_columns) > 0\",\n \"def _addcolumns(self, columnname, columndata=\\\"\\\"):\\n self[columnname] = columndata\",\n \"def test_add_column(self):\\n self.spy_on(DataGrid.add_column)\\n\\n DataGridColumnsHook(extension=self.extension,\\n datagrid_cls=DataGrid,\\n columns=[Column(id='sandbox')])\\n\\n self.assertTrue(DataGrid.add_column.called)\",\n \"def columns(self, new_columns: ColumnT) -> None:\\n new_columns2: ndarray = init.check_column_validity(new_columns)\\n len_new: int = len(new_columns2)\\n len_old: int = len(self._columns)\\n if len_new != len_old:\\n raise ValueError(f'There are {len_old} columns in the DataFrame. '\\n f'You provided {len_new}.')\\n\\n new_column_info: ColInfoT = {}\\n for old_col, new_col in zip(self._columns, new_columns2):\\n new_column_info[new_col] = utils.Column(*self._column_info[old_col].values)\\n\\n self._column_info = new_column_info\\n self._columns = new_columns2\",\n \"def has_headers(self):\\n for column in self.columns:\\n if column.header:\\n return True\\n return False\",\n \"def test_provide_column_types():\\n expected_column_types = (bool, float)\\n data = ((1, 1.0), (0, 2))\\n headers = (\\\"a\\\", \\\"b\\\")\\n\\n def preprocessor(data, headers, column_types=(), **_):\\n assert expected_column_types == column_types\\n return data, headers\\n\\n format_output(\\n data,\\n headers,\\n \\\"csv\\\",\\n column_types=expected_column_types,\\n preprocessors=(preprocessor,),\\n )\",\n \"def has_columns(df, columns):\\n result = True\\n for column in columns:\\n if column not in df.columns:\\n print(\\\"Missing column: {} in DataFrame\\\".format(column))\\n result = False\\n\\n return result\",\n \"def becomes_column(self):\\n row_type = self.get_type()\\n # Note: \\\"begin repeat\\\" does become a column\\n non_columns = ('begin group', 'end group', 'end repeat')\\n return row_type not in non_columns\",\n \"def test_add_column(self):\\n name_column = Varchar()\\n name_column._meta.name = \\\"name\\\"\\n\\n genre_column = Varchar()\\n genre_column._meta.name = \\\"genre\\\"\\n\\n schema: t.List[DiffableTable] = [\\n DiffableTable(\\n class_name=\\\"Band\\\",\\n tablename=\\\"band\\\",\\n columns=[name_column, genre_column],\\n )\\n ]\\n schema_snapshot: t.List[DiffableTable] = [\\n DiffableTable(\\n class_name=\\\"Band\\\",\\n tablename=\\\"band\\\",\\n columns=[name_column],\\n )\\n ]\\n\\n schema_differ = SchemaDiffer(\\n schema=schema, schema_snapshot=schema_snapshot, auto_input=\\\"y\\\"\\n )\\n\\n self.assertTrue(len(schema_differ.add_columns.statements) == 1)\\n self.assertEqual(\\n schema_differ.add_columns.statements[0],\\n \\\"manager.add_column(table_class_name='Band', tablename='band', column_name='genre', db_column_name='genre', column_class_name='Varchar', column_class=Varchar, params={'length': 255, 'default': '', 'null': False, 'primary_key': False, 'unique': False, 'index': False, 'index_method': IndexMethod.btree, 'choices': None, 'db_column_name': None})\\\", # noqa\\n )\",\n \"def isEmptyColumn(self, j, rowns ):\\n for i in range(0,rowns) :\\n if not self.isEmpty(i,j):\\n return False\\n return True\",\n \"def IsColumnMode(self):\\n return self.VertEdit.Enabled\",\n \"def add_blank_data_column(self):\\n\\n header_title, ok_pressed = QInputDialog.getText(self, \\\"Add Column\\\", \\\"Enter heading for the column:\\\",\\n QLineEdit.Normal, \\\"\\\")\\n if ok_pressed and header_title != '':\\n # print(header_title)\\n\\n default_value, set_default_pressed = QInputDialog.getText(self, \\\"Set Default Value\\\",\\n \\\"Enter default value to set for column if any:\\\",\\n QLineEdit.Normal, \\\"\\\")\\n\\n row_count = self.csv_data_table.rowCount()\\n last_column_count = self.csv_data_table.columnCount()\\n self.csv_data_table.insertColumn(last_column_count)\\n for empty_row in range(0, row_count):\\n item = QTableWidgetItem(default_value)\\n self.csv_data_table.setItem(empty_row, last_column_count, item)\\n\\n # TODO: fix untraced bug present in show/hide columns\\n self.column_headers.append(header_title)\\n self.column_headers_all.append(header_title)\\n # print(self.column_headers)\\n # print(self.column_headers_all)\\n self.csv_data_table.setHorizontalHeaderLabels(self.column_headers)\",\n \"def setNumColumns(self, num):\\n ExportDialog.numColumns = num\",\n \"def _add_column(self, column):\\n if column is None:\\n column = len(self._columns)\\n\\n if column in self._columns:\\n raise ValueError(f\\\"Duplicate column name: {column}\\\")\\n\\n if isinstance(column, int):\\n assert column >= len(self._columns)\\n for empty in range(len(self._columns), column):\\n self._add_column(empty)\\n\\n self._columns.append(column)\\n for idx in self.index:\\n row = self._data[idx]\\n row.append(None)\\n\\n return len(self._columns) - 1\",\n \"def getOutputCols(self):\\n if len(self.getOrDefault(self.outputCols)) == 0:\\n return [\\\"finished_\\\" + input_col for input_col in self.getInputCols()]\\n else:\\n return self.getOrDefault(self.outputCols)\",\n \"def AddColumnInfo(self, colInfo):\\r\\n\\r\\n self._header_win.AddColumnInfo(colInfo)\\r\\n self.DoHeaderLayout()\",\n \"def add_line(self, cols, values, units=None, desc=None, fmt=None,\\n match=None):\\n if self.lines is None:\\n types = []\\n for val in values:\\n if is_int(val):\\n types.append('<i4')\\n elif is_float(val):\\n types.append('<f8')\\n else:\\n types.append(STR_DTYPE)\\n self.lines = Table(rows=[values], names=cols, dtype=types,\\n masked=True)\\n if units is not None:\\n for colname, unit in zip(self.lines.colnames, units):\\n self.lines[colname].unit = unit\\n if desc is not None:\\n for colname, d in zip(self.lines.colnames, desc):\\n self.lines[colname].description = d\\n if fmt is not None:\\n for colname, f in zip(self.lines.colnames, fmt):\\n self.lines[colname].format = f\\n else:\\n # add new columns\\n if units is None:\\n units = [None] * len(cols)\\n if desc is None:\\n desc = [None] * len(cols)\\n if fmt is None:\\n fmt = [None] * len(cols)\\n for col, val, unit, d, f in zip(cols, values, units, desc, fmt):\\n if col not in self.lines.colnames:\\n nlines = len(self.lines)\\n if is_int(val):\\n typ = '<i4'\\n elif is_float(val):\\n typ = '<f8'\\n else:\\n typ = STR_DTYPE\\n col = MaskedColumn(ma.masked_array(np.empty(nlines),\\n mask=np.ones(nlines)),\\n name=col, dtype=typ, unit=unit,\\n description=d, format=f)\\n self.lines.add_column(col)\\n\\n if match is not None:\\n if len(match) == 2:\\n matchkey, matchval = match\\n add_if_not_matched = False\\n else:\\n matchkey, matchval, add_if_not_matched = match\\n\\n if match is not None and matchkey in self.lines.colnames:\\n l = np.argwhere(self.lines[matchkey] == matchval)\\n if len(l) > 0:\\n for col, val, unit in zip(cols, values, units):\\n if unit is None or unit == self.lines[col].unit:\\n self.lines[col][l] = val\\n else:\\n self.lines[col][l] = (val * unit).to(\\n self.lines[col].unit).value\\n return\\n else:\\n if not add_if_not_matched:\\n return\\n\\n # add new row\\n ncol = len(self.lines.colnames)\\n row = [None] * ncol\\n mask = np.ones(ncol)\\n for col, val, unit in zip(cols, values, units):\\n i = self.lines.colnames.index(col)\\n if unit is None or unit == self.lines[col].unit:\\n row[i] = val\\n else:\\n row[i] = (val * unit).to(self.lines[col].unit).value\\n mask[i] = 0\\n self.lines.add_row(row, mask=mask)\",\n \"def setAllColumns(self, newAllColumns):\\n \\n pass\",\n \"def _these_columns_cannot_annotate_exp_cons(self):\\n _cols = set([]) #\\n for param_name, req_cols in self.required_columns.items():\\n _cols |= req_cols\\n\\n return _cols | self.other_useful_columns\",\n \"def set_columns(self, columns):\\n self.columns = columns\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\n \"def _validate_cols(cols):\\n\\n\\tif cols is not None and len(cols) < 2:\\n\\t\\traise ValueError('too few features')\",\n \"def ComputeUnshownColumns(results, shown_columns, config, built_in_cols):\\n unshown_set = set() # lowercases column names\\n unshown_list = [] # original-case column names\\n shown_set = {col.lower() for col in shown_columns}\\n labels_already_seen = set() # whole labels, original case\\n\\n def _MaybeAddLabel(label_name):\\n \\\"\\\"\\\"Add the key part of the given label if needed.\\\"\\\"\\\"\\n if label_name.lower() in labels_already_seen:\\n return\\n labels_already_seen.add(label_name.lower())\\n if '-' in label_name:\\n col, _value = label_name.split('-', 1)\\n _MaybeAddCol(col)\\n\\n def _MaybeAddCol(col):\\n if col.lower() not in shown_set and col.lower() not in unshown_set:\\n unshown_list.append(col)\\n unshown_set.add(col.lower())\\n\\n # The user can always add any of the default columns.\\n for col in config.default_col_spec.split():\\n _MaybeAddCol(col)\\n\\n # The user can always add any of the built-in columns.\\n for col in built_in_cols:\\n _MaybeAddCol(col)\\n\\n # The user can add a column for any well-known labels\\n for wkl in config.well_known_labels:\\n _MaybeAddLabel(wkl.label)\\n\\n phase_names = set(itertools.chain.from_iterable(\\n (phase.name.lower() for phase in result.phases) for result in results))\\n # The user can add a column for any custom field\\n field_ids_alread_seen = set()\\n for fd in config.field_defs:\\n field_lower = fd.field_name.lower()\\n field_ids_alread_seen.add(fd.field_id)\\n if fd.is_phase_field:\\n for name in phase_names:\\n phase_field_col = name + '.' + field_lower\\n if (phase_field_col not in shown_set and\\n phase_field_col not in unshown_set):\\n unshown_list.append(phase_field_col)\\n unshown_set.add(phase_field_col)\\n elif field_lower not in shown_set and field_lower not in unshown_set:\\n unshown_list.append(fd.field_name)\\n unshown_set.add(field_lower)\\n\\n if fd.field_type == tracker_pb2.FieldTypes.APPROVAL_TYPE:\\n approval_lower_approver = (\\n field_lower + tracker_constants.APPROVER_COL_SUFFIX)\\n if (approval_lower_approver not in shown_set and\\n approval_lower_approver not in unshown_set):\\n unshown_list.append(\\n fd.field_name + tracker_constants.APPROVER_COL_SUFFIX)\\n unshown_set.add(approval_lower_approver)\\n\\n # The user can add a column for any key-value label or field in the results.\\n for r in results:\\n for label_name in tracker_bizobj.GetLabels(r):\\n _MaybeAddLabel(label_name)\\n for field_value in r.field_values:\\n if field_value.field_id not in field_ids_alread_seen:\\n field_ids_alread_seen.add(field_value.field_id)\\n fd = tracker_bizobj.FindFieldDefByID(field_value.field_id, config)\\n if fd: # could be None for a foreign field, which we don't display.\\n field_lower = fd.field_name.lower()\\n if field_lower not in shown_set and field_lower not in unshown_set:\\n unshown_list.append(fd.field_name)\\n unshown_set.add(field_lower)\\n\\n return sorted(unshown_list)\",\n \"def GetColumnCount(self):\\r\\n\\r\\n return len(self._columns)\",\n \"def test_overall_report_columns():\\n assert (len(overall_data['columns']) == 31)\",\n \"def add_column(self, name, type):\\n raise NotImplementedError(\\n \\\"Please implement the 'add_column' method in a derived class.\\\")\",\n \"def getInputCols(self):\\n return self.getOrDefault(self.inputCols)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6017477","0.5631939","0.55924374","0.5548952","0.5529502","0.5455568","0.5444327","0.54101974","0.53786486","0.53754896","0.53557205","0.5345275","0.53391767","0.53190124","0.5316484","0.5307784","0.528852","0.5280459","0.5254802","0.525032","0.5224798","0.5210471","0.519142","0.5182929","0.5171422","0.51590073","0.5138467","0.511951","0.5107517","0.5107517","0.5107517","0.5107517","0.5107517","0.5107517","0.5107517","0.51029235","0.5073577","0.50599355","0.5046192","0.50409997","0.5039767","0.5030293","0.50225997","0.5018143","0.5011441","0.49959913","0.49565881","0.4956546","0.4956546","0.49565023","0.4931904","0.4927329","0.4922511","0.49132326","0.4909706","0.49093506","0.4898184","0.489302","0.48866713","0.48814216","0.48716393","0.4869125","0.48419115","0.48409948","0.4837871","0.48337877","0.48331335","0.48290867","0.481677","0.48164892","0.48080793","0.4801225","0.4796543","0.4791469","0.4782204","0.47712302","0.4769396","0.47693327","0.4764534","0.47609645","0.47507933","0.47505766","0.47498333","0.47419858","0.47407573","0.4733812","0.4728473","0.4728208","0.47249004","0.4723722","0.47205988","0.47183746"],"string":"[\n \"0.6017477\",\n \"0.5631939\",\n \"0.55924374\",\n \"0.5548952\",\n \"0.5529502\",\n \"0.5455568\",\n \"0.5444327\",\n \"0.54101974\",\n \"0.53786486\",\n \"0.53754896\",\n \"0.53557205\",\n \"0.5345275\",\n \"0.53391767\",\n \"0.53190124\",\n \"0.5316484\",\n \"0.5307784\",\n \"0.528852\",\n \"0.5280459\",\n \"0.5254802\",\n \"0.525032\",\n \"0.5224798\",\n \"0.5210471\",\n \"0.519142\",\n \"0.5182929\",\n \"0.5171422\",\n \"0.51590073\",\n \"0.5138467\",\n \"0.511951\",\n \"0.5107517\",\n \"0.5107517\",\n \"0.5107517\",\n \"0.5107517\",\n \"0.5107517\",\n \"0.5107517\",\n \"0.5107517\",\n \"0.51029235\",\n \"0.5073577\",\n \"0.50599355\",\n \"0.5046192\",\n \"0.50409997\",\n \"0.5039767\",\n \"0.5030293\",\n \"0.50225997\",\n \"0.5018143\",\n \"0.5011441\",\n \"0.49959913\",\n \"0.49565881\",\n \"0.4956546\",\n \"0.4956546\",\n \"0.49565023\",\n \"0.4931904\",\n \"0.4927329\",\n \"0.4922511\",\n \"0.49132326\",\n \"0.4909706\",\n \"0.49093506\",\n \"0.4898184\",\n \"0.489302\",\n \"0.48866713\",\n \"0.48814216\",\n \"0.48716393\",\n \"0.4869125\",\n \"0.48419115\",\n \"0.48409948\",\n \"0.4837871\",\n \"0.48337877\",\n \"0.48331335\",\n \"0.48290867\",\n \"0.481677\",\n \"0.48164892\",\n \"0.48080793\",\n \"0.4801225\",\n \"0.4796543\",\n \"0.4791469\",\n \"0.4782204\",\n \"0.47712302\",\n \"0.4769396\",\n \"0.47693327\",\n \"0.4764534\",\n \"0.47609645\",\n \"0.47507933\",\n \"0.47505766\",\n \"0.47498333\",\n \"0.47419858\",\n \"0.47407573\",\n \"0.4733812\",\n \"0.4728473\",\n \"0.4728208\",\n \"0.47249004\",\n \"0.4723722\",\n \"0.47205988\",\n \"0.47183746\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.89480335"},"document_rank":{"kind":"string","value":"3"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":115,"numItemsPerPage":100,"numTotalItems":95772,"offset":11500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTkyMTUyOCwianRpIjoiN2MzY2NlZTYtNTg1MS00MGExLThjM2YtMjdkNDkxN2Y0YWUyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5MjUxMjgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.gyCcPxLMQCRkAFaYTGph-xMTolsHbPuaPRIiga_zCMo8jJZho-Sl5P2XIESwy8Ia4JPIyBobx9cypZ1PtOAfDw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Test if this cart is empty. | def is_empty(self):
return self.id is None or self.nb_cart_items == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_shopping_cart_is_empty(self):\n response = self.client.get(self.SHOP_CART_URL)\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"Your shopping cart is empty.\")\n self.assertQuerysetEqual(response.context['contents'], [])",
"def test_shopping_car... | [
"0.8263173",
"0.7682502",
"0.76717985",
"0.7634377",
"0.7626435",
"0.7626435",
"0.76094395",
"0.7590669",
"0.7586988",
"0.75739545",
"0.75577635",
"0.75392646",
"0.7537761",
"0.7518471",
"0.74757564",
"0.74757564",
"0.74580145",
"0.7455165",
"0.7455165",
"0.7435698",
"0.74259... | 0.8744867 | 0 |
Yield successive chunks from iterable of length size. | def chunker(iterable, size):
for i in range(0, len(iterable), size):
yield iterable[i:i + size] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chunks(iterator, size):\n for index in range(0, len(iterator), size):\n yield iterator[index:index + size]",
"def iter_chunks(iterable, size):\n it = iter(iterable)\n while True:\n chunk = tuple(itertools.islice(it, size))\n if len(chunk) == 0:\n break\n yield ... | [
"0.8773219",
"0.8520928",
"0.8467599",
"0.841324",
"0.8410151",
"0.8247795",
"0.8213483",
"0.8210779",
"0.810383",
"0.8072789",
"0.8064006",
"0.80410075",
"0.7970535",
"0.79383165",
"0.79333675",
"0.7927991",
"0.7896404",
"0.7862617",
"0.77980405",
"0.7778066",
"0.7762152",
... | 0.8812423 | 2 |
Runs experiment using DP, QL or both. Creates new directory automatically Save result summary to summary file | def run_Experiment(DP = None, QL = None):
# Path information
output_path, exp_num = create_new_dir() #dirs Exp/1, Exp/2, ...
DP_path = join(output_path,'DP') #dirs Exp/1/DP
QL_path = join(output_path,'QL') #dirs Exp/1/QL
print("************ Exp ", exp_num, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_test(self):\n\n # populate *_ps sets\n self.enter_project_file()\n\n # populate *_dir sets\n self.enter_directories()\n\n # The files in the directories makes up the largest possible set of files\n self.result_files = self.result_files_dir\n self.design_file... | [
"0.6303137",
"0.5954984",
"0.5947202",
"0.5942798",
"0.5933814",
"0.5922406",
"0.58769554",
"0.58608824",
"0.58519894",
"0.5840858",
"0.5767799",
"0.5748718",
"0.5712309",
"0.5707479",
"0.5706759",
"0.56962764",
"0.56911665",
"0.56899905",
"0.56842196",
"0.567968",
"0.5671062... | 0.76796246 | 0 |
start point of scraping use urls, pass soup tag to Unvs return a list of 100 unvs(university) object | def scrape():
url_base='https://www.usnews.com/best-colleges/rankings/national-universities'
unvss=[]
for page in range(N_PAGE):
url=url_base+'?_page={}'.format(page+1)
soup=get_soup(url)
unvs_tags=soup.find_all('li',id=re.compile(r'^view-.*'),class_='block-normal block-loose-for-lar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_national_university_data(univ_url):\n f_name = 'national_university_html.json'\n base_url = 'https://www.usnews.com'\n html_cache = load_cache(f_name)\n\n if univ_url not in html_cache:\n resp = requests.get(base_url + univ_url, headers=agent)\n html_cache[univ_url] = resp.text\n ... | [
"0.6813396",
"0.6677755",
"0.6292874",
"0.6277937",
"0.62407184",
"0.6227392",
"0.6170943",
"0.60358685",
"0.6008686",
"0.5971656",
"0.5971656",
"0.5909",
"0.5846324",
"0.5824053",
"0.5823654",
"0.5777535",
"0.57656276",
"0.5758727",
"0.5701715",
"0.57001144",
"0.5692121",
... | 0.8051392 | 0 |
get a soup tag, scrape the basic info from tag, return a url directing to detailed info call scrpae_detail for the info | def scrape_overview(self,unvs_tag):
base='https://www.usnews.com'
name_tag=unvs_tag.find('h3',class_='heading-large block-tighter').a
assert(name_tag!=None)
self.name=name_tag.string.strip()
self.page_url=base+name_tag.get('href')
assert(self.page_url!=None)
self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrape_detail(self,url):\n soup=get_soup(url)\n self.zip=soup.find('p',class_='block-normal hide-for-small-only text-small hero-ranking-data-contact').stripped_strings.__next__()[-5::1]\n if self.zip in zips:\n #print('DUPLICATE!')\n zips.append(self.zip)\n info_ta... | [
"0.6728794",
"0.622309",
"0.61702514",
"0.60019004",
"0.59169936",
"0.5884641",
"0.5856371",
"0.577665",
"0.57433057",
"0.5709777",
"0.56357557",
"0.56332",
"0.5629008",
"0.56176746",
"0.5567721",
"0.55604005",
"0.5551275",
"0.5544126",
"0.554374",
"0.5476874",
"0.54727167",
... | 0.54294735 | 25 |
use the url to scrape detailed info | def scrape_detail(self,url):
soup=get_soup(url)
self.zip=soup.find('p',class_='block-normal hide-for-small-only text-small hero-ranking-data-contact').stripped_strings.__next__()[-5::1]
if self.zip in zips:
#print('DUPLICATE!')
zips.append(self.zip)
info_tags=soup.fin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_info_of_url(url):\n pass",
"def get_details(self):\n # For every URL in our list of links that we got from the parser's\n # 'lookup()' method we get the data from that URL, set it in our\n # parser's buffer, and then let the parser do the rest of the work.\n #\n for ... | [
"0.70981526",
"0.6877993",
"0.6745803",
"0.67396754",
"0.66712624",
"0.66406703",
"0.6620889",
"0.6527329",
"0.65011233",
"0.64701736",
"0.6441623",
"0.6421658",
"0.6386159",
"0.63498217",
"0.6328521",
"0.6304512",
"0.62901115",
"0.625672",
"0.6245974",
"0.62436974",
"0.62268... | 0.7324095 | 0 |
This can be done on the fly but I felt like it would be too much nesting. | def filterByCountry(partners) -> dict():
countries = dict()
for partner in partners:
countries.setdefault(partner['country'],[]).append(partner)
return countries | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def less_nested_example_vanilla():\n return",
"def wrapup(self):\n pass",
"def build_nested_blocks(self):\n pass",
"def build(c):",
"def less_nested_example_googlestyle(a):\n return a",
"def less_nested_example_rst():\n\n return",
"def _build(self):",
"def _build(self):",
"de... | [
"0.56387776",
"0.55480915",
"0.55267954",
"0.5501647",
"0.53977764",
"0.5364499",
"0.5355195",
"0.5355195",
"0.5338756",
"0.51850367",
"0.51783925",
"0.5156866",
"0.5146624",
"0.5119881",
"0.5119881",
"0.5119881",
"0.5119881",
"0.5119881",
"0.5090604",
"0.5053077",
"0.5051833... | 0.0 | -1 |
Yield successive nsized chunks from l. | def chunks(l, n):
for i in xrange(0, len(l), n):
yield l[i:i+n] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _chunk(self, l, n):\n for i in range(0, len(l) + 1, n):\n yield l[i:i + n]",
"def chunks(self, l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def __chunks(l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def get_chunks(self, ... | [
"0.80388075",
"0.7924573",
"0.79226995",
"0.788447",
"0.7877021",
"0.781505",
"0.7764907",
"0.77549165",
"0.7743869",
"0.7731126",
"0.77281487",
"0.77241385",
"0.77024275",
"0.7688614",
"0.7688614",
"0.7663561",
"0.7656419",
"0.76552874",
"0.76552874",
"0.76452404",
"0.764524... | 0.0 | -1 |
Swap the byteordering in a packet with N=4 bytes per word | def byteswap(data, word_size=4):
return reduce(lambda x,y: x+''.join(reversed(y)), chunks(data, word_size), '') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def byte_swap(data, word_size):\n \n bs_data = [0]*len(data)\n for ii in range(0, len(data), word_size):\n bs_data[ii:ii+word_size] = data[ii:ii+4][::-1]\n return(bytes(bs_data))",
"def swapNibbles(inputByte):\n return (inputByte << 4 | inputByte >> 4) & 0xff",
"def swap_bytes(word_val):\... | [
"0.6286982",
"0.6104455",
"0.57370377",
"0.5551212",
"0.5419383",
"0.541504",
"0.5307678",
"0.524828",
"0.5197568",
"0.51582634",
"0.5000781",
"0.49925607",
"0.49657536",
"0.49612567",
"0.49490094",
"0.49409774",
"0.49387702",
"0.48654178",
"0.48503458",
"0.48486102",
"0.4845... | 0.62880516 | 0 |
For Cloud Foundry, we need to look in the VCAP_SERVICES environment | def try_atlas():
result = {}
try:
vcap_services = os.getenv('VCAP_SERVICES')
services = json.loads(vcap_services)
for service_name in services.keys():
print(f'service_name={service_name}')
if service_name == "_":
continue
credentials = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_virtual_service(self):\n pass",
"def test_vcap_services(self):\n Pet.init_db()\n self.assertIsNotNone(Pet.redis)",
"def test_virtualservice_get(self):\n pass",
"def YumGetServiceName(vm):\n raise NotImplementedError",
"def test_aws_service_api_vm_get(self):\n ... | [
"0.6299223",
"0.5994557",
"0.5943203",
"0.59000796",
"0.56889766",
"0.5556806",
"0.5556276",
"0.5535231",
"0.5494093",
"0.54824984",
"0.5477889",
"0.5470114",
"0.54561913",
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"0.5373834",
"0.5360619",
"0.53265256",
"0.53149337",
"0.5297183",
"0.52950287",
"0.52896... | 0.5476052 | 11 |
Connects components in UI with their corresponding event handlers in Application Manager | def connectUI(self, obj):
self.ui.bt_search.clicked.connect(obj.player_search)
self.ui.bt_edit_edit_selected.clicked.connect(obj.player_edit_selected)
self.ui.bt_edit_edit_selected.clicked.connect(obj.player_edit_selected)
self.ui.bt_edit_saveedit.clicked.connect(obj.player_edit_saveedit)
self.ui.bt_edi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_event(self):\n self.ui.btn_Open_Image.clicked.connect(self.onclick_open_image)\n self.ui.btn_Home.clicked.connect(self.go_to_home_application)\n self.ui.btn_Open_Cam.clicked.connect(self.onclick_open_camera_button)\n self.ui.btn_Open_Video.clicked.connect(self.onclick_load_v... | [
"0.6218227",
"0.6187797",
"0.60312563",
"0.59892905",
"0.5914981",
"0.59095055",
"0.5853487",
"0.57918596",
"0.5754873",
"0.5754524",
"0.5751479",
"0.5740638",
"0.57377917",
"0.57241845",
"0.57060283",
"0.5671398",
"0.5660528",
"0.5606709",
"0.5601831",
"0.5572997",
"0.555753... | 0.5247654 | 56 |
returns true if response is HTML | def is_good_response(self, resp):
content_type = resp.headers['Content-Type'].lower()
return (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_good_response(self, resp):\r\n\t\tcontent_type = resp.headers['Content-Type'].lower()\r\n\t\treturn (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)",
"def is_html(self):\r\n return self.__content_type == html_ctype",
"def is_html(self):\n return se... | [
"0.7978629",
"0.7896287",
"0.7848669",
"0.7823706",
"0.78176934",
"0.77728",
"0.7765719",
"0.77475446",
"0.7746658",
"0.77149415",
"0.76882756",
"0.76882756",
"0.76882756",
"0.76882756",
"0.7683412",
"0.7676108",
"0.7676108",
"0.7676108",
"0.7676108",
"0.7676108",
"0.7676108"... | 0.80010843 | 0 |
Attempts to get the content at `url` by making an HTTP GET request. If the contenttype of response is some kind of HTML/XML, return the text content, otherwise return None | def simple_get(self, url):
"""
The simple_get function accepts a single url argument.
It then makes a GET request to that url.
If nothing goes wrong, you end up with the raw HTML content for the page you requested.
If there were any problems with your request (like the ur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_url_content(url):\n try:\n r = requests.get(url)\n if r.ok:\n return r.text\n else:\n return None\n except Exception:\n return None",
"def simple_get(url):\n try:\n with closing(requests.get(url, stream=True)) as resp:\n if(is_... | [
"0.82686275",
"0.82643026",
"0.82408965",
"0.82197744",
"0.82143354",
"0.80992573",
"0.8057474",
"0.8057474",
"0.8056611",
"0.80537915",
"0.80537915",
"0.80537915",
"0.80537915",
"0.80537915",
"0.80537915",
"0.80432236",
"0.80328834",
"0.80328834",
"0.80328834",
"0.7998802",
... | 0.79634637 | 20 |
Save the data for the component to be persisted. | def saveData(self):
data = super(SimpleControlComponentGuide, self).saveData()
data["ctrlSize"] = self.ctrlSizeInputAttr.getValue()
data["ctrlXfo"] = self.mainCtrl.xfo
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_data(self):\n pass",
"def saveData(self):\n pass",
"def save(self):\n raise NotImplementedError",
"def save(self):\n raise NotImplementedError",
"def save(self):\n raise NotImplementedError",
"def save(self):\n # TODO (Pierre): code",
"def save(self):\... | [
"0.809109",
"0.78951603",
"0.76420194",
"0.76420194",
"0.76420194",
"0.76066446",
"0.76036537",
"0.7577322",
"0.75646037",
"0.75355595",
"0.75355595",
"0.75355595",
"0.75355595",
"0.75355595",
"0.7532554",
"0.7512014",
"0.7512014",
"0.7512014",
"0.7493771",
"0.74873805",
"0.7... | 0.0 | -1 |
Load a saved guide representation from persisted data. | def loadData(self, data):
super(SimpleControlComponentGuide, self).loadData( data )
self.ctrlSizeInputAttr.setValue(data["ctrlSize"])
self.mainCtrl.xfo = data["ctrlXfo"]
scaleValue = data["ctrlSize"]
self.mainCtrl.setShape('square')
self.mainCtrl.rotatePoints(90, 0, 0)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadData(self, data):\n\n #Grab the guide settings in case we want to use them here (and are not stored in data arg)\n existing_data = self.saveData()\n existing_data.update(data)\n data = existing_data\n\n super(OSSMouthGuide, self).loadData( data )\n\n self.loadAllOb... | [
"0.6494611",
"0.59344995",
"0.5652479",
"0.5652479",
"0.5652479",
"0.5652479",
"0.56253165",
"0.56253165",
"0.562344",
"0.5558141",
"0.5558141",
"0.5526227",
"0.55205995",
"0.54645044",
"0.53951573",
"0.53586185",
"0.5353325",
"0.53121376",
"0.5278196",
"0.5272028",
"0.526720... | 0.0 | -1 |
Returns the Guide data used by the Rig Component to define the layout of the final rig. | def getRigBuildData(self):
data = super(SimpleControlComponentGuide, self).getRigBuildData()
data["ctrlSize"] = self.ctrlSizeInputAttr.getValue()
data["ctrlXfo"] = self.mainCtrl.xfo
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def saveData(self):\n data = super(SimpleControlComponentGuide, self).saveData()\n\n data[\"ctrlSize\"] = self.ctrlSizeInputAttr.getValue()\n data[\"ctrlXfo\"] = self.mainCtrl.xfo\n\n return data",
"def saveData(self):\n\n data = super(OSSMouthGuide, self).saveData()\n\n ... | [
"0.60837376",
"0.57267016",
"0.5652258",
"0.55439556",
"0.55439556",
"0.5307513",
"0.5296474",
"0.52579033",
"0.52306396",
"0.5197011",
"0.51202613",
"0.5104202",
"0.50606996",
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"0.5048612",
"0.5043005",
"0.5043005",
"0.50353324",
"0.50348055",
"0.50302404",
"0.5... | 0.6716383 | 0 |
Enables introspection of the class prior to construction to determine if it is a guide component. | def getComponentType(cls):
return 'Guide' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def abstract(self):\n return self._cls and not self._tool",
"def get_guide_type(guide):\n # Maintained by naming convention in the Blender files. Sub-optimal.\n try:\n return guide.name[guide.name.rindex(\".\") + 1:]\n except:\n return None",
"def setup_class(cls):\n cls.be... | [
"0.57170075",
"0.5539905",
"0.5513718",
"0.5417464",
"0.53752244",
"0.5361038",
"0.53488845",
"0.5335014",
"0.5306125",
"0.5271901",
"0.5271901",
"0.5254923",
"0.5219103",
"0.5219103",
"0.5090291",
"0.5090291",
"0.5021101",
"0.5020824",
"0.49868792",
"0.49730074",
"0.49605048... | 0.6441624 | 1 |
Returns the corresponding rig component class for this guide component class | def getRigComponentClass(cls):
return SimpleControlComponentRig | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getComponentType(cls):\n\n return 'Guide'",
"def getComponentType(cls):\n\n return 'Guide'",
"def getRigComponentClass(cls):\n\n return OSSMouthRig",
"def get_class(self):\n\t\treturn self.CLASS",
"def component(self):\n return self._component",
"def component(self):\n ... | [
"0.72734654",
"0.72734654",
"0.6762508",
"0.6674559",
"0.61377406",
"0.61377406",
"0.61340904",
"0.6081116",
"0.6031022",
"0.59476155",
"0.59440464",
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"0.583399",
"0.5800438",
"0.5774759",
"0.5768571",
"0.57600415",
"0.5756991",
"0.575684... | 0.74637115 | 0 |
Load a saved guide representation from persisted data. | def loadData(self, data=None):
super(SimpleControlComponentRig, self).loadData( data )
ctrlSize = data.get('ctrlSize', 1.0)
ctrlXfo = data.get('ctrlXfo', Xfo())
# ================
# Resize Controls
# ================
self.mainCtrl.setShape('square')
sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadData(self, data):\n\n #Grab the guide settings in case we want to use them here (and are not stored in data arg)\n existing_data = self.saveData()\n existing_data.update(data)\n data = existing_data\n\n super(OSSMouthGuide, self).loadData( data )\n\n self.loadAllOb... | [
"0.6494611",
"0.59344995",
"0.5652479",
"0.5652479",
"0.5652479",
"0.5652479",
"0.56253165",
"0.56253165",
"0.562344",
"0.5558141",
"0.5558141",
"0.5526227",
"0.55205995",
"0.54645044",
"0.53951573",
"0.53586185",
"0.5353325",
"0.53121376",
"0.5278196",
"0.5272028",
"0.526720... | 0.0 | -1 |
Create table with prepared structure | def create_table(drop_if_exists=True) -> None:
connection = ConnectDB.connect()
additional_types_query = """DO
BEGIN
IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'gender') THEN
CREATE TYPE Gender AS ENUM ('female', 'man');
END IF;
IF NOT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_table(self) :\n\n cur = self.con.cursor()\n delete_sql = 'DROP TABLE IF EXISTS \"%s\"' % self.name\n cur.execute(delete_sql)\n\n col_sql = ','.join(['\"%s\" %s' % (self.cols[i], self.types[i])\n for i in range(len(self.cols))])\n create_sql ... | [
"0.74694735",
"0.7446109",
"0.7443517",
"0.7393832",
"0.73920125",
"0.73801327",
"0.7374949",
"0.73692536",
"0.7362258",
"0.7269887",
"0.7257049",
"0.723348",
"0.7213967",
"0.72057986",
"0.71917915",
"0.71851873",
"0.71809405",
"0.715922",
"0.71443105",
"0.71439445",
"0.71288... | 0.0 | -1 |
Load csv file to database. Add `year` column | def from_csv_to_database():
for year, path in FileNamePath.items():
# load csv files
with open(path, encoding='cp1251') as dataset:
print(f"Download {year} data")
get_curr_data(dataset, year) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_catalog(self):\n self.catalog = pd.read_csv(self.catalog_path, \n index_col=0, parse_dates=True)\n self.unique_years = self.catalog.index.year.unique()\n return",
"def load_data(path):\n\n columns = ['Item Year', 'Original Value', 'Standard Value', 'Original Currency',... | [
"0.6482594",
"0.6402999",
"0.6367218",
"0.6283168",
"0.62296",
"0.6219771",
"0.6213001",
"0.6185162",
"0.6111619",
"0.6011145",
"0.60105246",
"0.5978613",
"0.597643",
"0.5974852",
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"0.59363544",
"0.593171",
"0.5929072",
"0.5925405",
"0.5903456",
"0.5902019",
"0.... | 0.6820516 | 0 |
Save csv file with given header and rows into output folder | def to_csv(header, rows):
with open('result.csv', 'w') as result:
result_writer = csv.writer(result, delimiter=';')
result_writer.writerow(header)
result_writer.writerows(rows) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_csv(self, out_file_name, header):\n\n with open(out_file_name, 'wb') as outf:\n writer = csv.writer(outf, quoting=csv.QUOTE_ALL)\n writer.writerow(header)\n writer.writerows(self.records)",
"def write_csv(header_row, data_rows, filename, course_id):\n shared.e... | [
"0.73305476",
"0.7274633",
"0.7113233",
"0.69982344",
"0.68883383",
"0.6880275",
"0.68772936",
"0.6867271",
"0.6855233",
"0.68267447",
"0.67927784",
"0.6781188",
"0.67231953",
"0.67214197",
"0.666982",
"0.6666694",
"0.663893",
"0.6626239",
"0.6602342",
"0.65913993",
"0.657377... | 0.74616706 | 0 |
Collect the data from files in the imgdata directory. | def get_data():
size, intensity, age = [], [], []
def calculate(data, data_top):
"""Return age and the averages of size and intensity."""
size, intensity, age = np.array([data["Size"]]), np.array([data["Intensity"]]), data_top.iat[1,0]
size_avg, intensity_avg = np.average(size), np.aver... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data_in_folder(self):\n print('loading files in data folder')\n n = len(self.filenames)\n idx_max = n // self.batch_size\n for idx in range(0, idx_max-1):\n data = []\n for f in self.filenames[idx:idx+64]:\n img = cv2.imread(f, int(self.colo... | [
"0.71007186",
"0.70617384",
"0.70067984",
"0.70027936",
"0.6990193",
"0.6869805",
"0.6833341",
"0.6772648",
"0.6760607",
"0.6757017",
"0.6753424",
"0.6750051",
"0.66493285",
"0.6612349",
"0.65738165",
"0.6558397",
"0.6531852",
"0.6525636",
"0.6473054",
"0.6431205",
"0.6412979... | 0.67055446 | 12 |
Return age and the averages of size and intensity. | def calculate(data, data_top):
size, intensity, age = np.array([data["Size"]]), np.array([data["Intensity"]]), data_top.iat[1,0]
size_avg, intensity_avg = np.average(size), np.average(intensity)
return size_avg, intensity_avg, age | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_average_age(self):\n return np.mean([agent.age for agent in self.agents])",
"def average_age():\n df = pd.read_csv(config.META_FQN, sep=\"\\t\")\n ages = []\n for _, row in df.iterrows():\n if row[\"asr_test\"]:\n age = row[\"Age_ses1\"]\n if not math.isnan(ag... | [
"0.69467896",
"0.6716017",
"0.64098644",
"0.6313252",
"0.62349457",
"0.60789764",
"0.603415",
"0.5983959",
"0.5971684",
"0.5971354",
"0.596329",
"0.5948338",
"0.59151775",
"0.59151775",
"0.5871947",
"0.5868544",
"0.58433545",
"0.57856715",
"0.5773423",
"0.57728165",
"0.576568... | 0.74258214 | 0 |
Locates the flags in the resource Calls the LineFinder class in order | def getting_flags_locations(self):
print(self.flags)
self.line_finder.find_line(self.html) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _linesearch(self):\n pass",
"def setup_flags(self):\n self.io_args.color = self.io_args.color_full\n self.io_args.rig_in = self.io_args.rig\n self.io_args.matches = os.path.join(self.io_args.output_root, \"matches.json\")\n self.io_args.rig_out = os.path.join(self.io_args.o... | [
"0.6099659",
"0.573128",
"0.5506411",
"0.54494226",
"0.52455074",
"0.52231914",
"0.5129431",
"0.51100206",
"0.5072955",
"0.5024129",
"0.5009258",
"0.4999141",
"0.49835676",
"0.4975226",
"0.49732998",
"0.49641412",
"0.49461514",
"0.4915233",
"0.49087882",
"0.48647398",
"0.4846... | 0.76073164 | 0 |
Save the dictionary with the serendipity values. | def save_serendipity_dic(y, filename):
store = pd.io.pytables.HDFStore(y)
mat = store.matrix
store.close()
n = len(mat.columns)
ser = 1 - mat.sum(axis=1) / n
f = open(filename, "w")
cPickle.dump(ser.to_dict(), f, protocol=2)
f.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_vals (self):\n raise NotImplementedError",
"def save(self) -> None:\n with open(dict_path, 'w', encoding='utf-8') as dictionary_file:\n json.dump(self.data, dictionary_file, indent=2, separators=(',', ':'), ensure_ascii=False)",
"def save(self):\n self.wallet.storage.put(\n ... | [
"0.6690913",
"0.66228455",
"0.6598225",
"0.65902627",
"0.6570407",
"0.65265197",
"0.6378944",
"0.63679206",
"0.6274411",
"0.62738395",
"0.6251937",
"0.624853",
"0.6242614",
"0.62388796",
"0.62336016",
"0.62253934",
"0.618943",
"0.6171405",
"0.61166525",
"0.61027896",
"0.60906... | 0.6463705 | 6 |
Return the serendipity of a RNA. | def get_serendipity_val(dic, key):
# The key was in the training set
try:
return dic[key]
# The key wasn't in the training set, then the serendipity is 1
except KeyError:
return 1.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_serie(probability_of_using_same_serie=0.1):\n if random() < probability_of_using_same_serie and GraphParameters.SERIE:\n return choice(list(GraphParameters.SERIE))\n serie = \"Alpha-\" + ''.join(choice('0123456789ABCDEF-') for i in range(8)).strip(\"-\")\n GraphParameters.SE... | [
"0.5642784",
"0.55564713",
"0.5457908",
"0.54552877",
"0.5367021",
"0.5355116",
"0.53361696",
"0.53177714",
"0.52889764",
"0.52446175",
"0.522932",
"0.5206401",
"0.51484346",
"0.51453054",
"0.5136129",
"0.5101252",
"0.5044812",
"0.50340647",
"0.5026674",
"0.50091314",
"0.5003... | 0.5497885 | 2 |
Add url domain field to each tweet in each user data object. Url domain field contains list of domains corresponding to list of urls. | def modify_user_data(user_d_list):
for user in user_d_list:
for tweet in user['tweets']:
domains = [get_domain_of_url(url) for url in tweet['urls']]
tweet['domains'] = domains
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_domain():\n\n for e in Expr.search() + User.search(): e.set_tld(config.server_name)",
"def fixURLS():\n url_re = re.compile(r'http t co \\S+')\n tweets = Tweet.objects.all()\n for tweet in tweets:\n tweet.text = url_re.sub(' ', tweet.text)\n tweet.text = ' '.join(tweet.text.s... | [
"0.5635619",
"0.55407476",
"0.5532279",
"0.5374472",
"0.537326",
"0.53537875",
"0.53423244",
"0.53104246",
"0.5303713",
"0.5294134",
"0.52771896",
"0.527685",
"0.52617246",
"0.524754",
"0.52349997",
"0.52061516",
"0.5133119",
"0.5130348",
"0.5129113",
"0.5128528",
"0.5121948"... | 0.8328926 | 0 |
Determine the domain that a url redirects to. | def get_domain_of_url(url):
try:
request = Request(url)
request.add_header('User-Agent', 'Resistance is futile')
response = urlopen(request)
response_url = response.url
domain = urlparse(response_url).hostname
return domain.lower()
except:
return "URL_ERRO... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetDomainName(self):\n return urlparse(self._redirectUrl).netloc",
"def get_domain(url):\n parsed_uri = urlparse(url)\n return(parsed_uri.netloc)",
"def get_domain(self, response):\n parts = urllib.parse.urlparse(response.url)\n domain = parts.netloc\n return domain",
"d... | [
"0.7943847",
"0.78226775",
"0.7710047",
"0.7608279",
"0.7555167",
"0.75494635",
"0.7544284",
"0.75333625",
"0.7485427",
"0.7466769",
"0.72855824",
"0.7276844",
"0.71699125",
"0.7098693",
"0.70763904",
"0.7055416",
"0.7030797",
"0.6981147",
"0.6922646",
"0.6882593",
"0.6849095... | 0.74133736 | 10 |
This function takes in all paths that are represented as lists of consecutive nodes [node1, node2,...,nodeN] and converted to paths represented as lists of consecutive relations [rel1, rel2,...,relM] if self.include_entity is false, or as lists of nodes and relations [node1, rel1, node2, rel2,...,relM, nodeN] if self.i... | def expand_paths_by_nodes(self, paths):
paths_formatted = set()
# Expand each path
for path in paths:
if len(path) < 2:
continue
expanded_paths = set()
if self.include_entity:
relations_for_each_step = [[path[0]]]
el... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_paths(self):\n # convert to node sequences, dropping s'\n self.nodeseq_paths = []\n for path in self.paths:\n node_seq = [] # don't include s'\n for arc in path:\n node_seq.append(self.arc_info[arc]['destin'])\n self.nodeseq_paths.ap... | [
"0.6200332",
"0.5851866",
"0.571507",
"0.56179786",
"0.5481076",
"0.5464558",
"0.5461847",
"0.5296667",
"0.5288939",
"0.5283457",
"0.5277893",
"0.5269692",
"0.52677166",
"0.52636945",
"0.5238955",
"0.5237788",
"0.5216618",
"0.52149516",
"0.5190407",
"0.51791054",
"0.5176276",... | 0.6647005 | 0 |
This function is used to write all paths between any two entities that are connected by the input relation to a file. Because this function will go through all paths node by node, this function will also used to filter paths to save computation. | def write_and_filter_paths(self, source, target, relation, label, paths):
file_dir = os.path.join(self.save_dir, relation + "_" + str(self.maximum_length) + "_" + str(self.remaining_percentage) + "_" + str(self.random_seed) + ".txt")
with open(file_dir, "a") as fh:
fh.write(str(label) + "\t"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writePathways( self ):\n\n self.logger.info( 'writePathways: START' )\n\n # Generate inserts for meabolic pathways.\n self.importerPathway.writePathways()\n\n self.logger.info( 'writePathways: DONE' )",
"def filter_paths(self, paths):\n formatted_paths = set()\n for ... | [
"0.6408844",
"0.6286845",
"0.62742597",
"0.619009",
"0.6096018",
"0.5878812",
"0.5735827",
"0.5685394",
"0.56852794",
"0.5661119",
"0.5632934",
"0.5616579",
"0.5599253",
"0.55716807",
"0.556647",
"0.5563934",
"0.5434351",
"0.54200315",
"0.537365",
"0.5366517",
"0.53606457",
... | 0.74876946 | 0 |
This function is used to filter all paths and change paths represented by relation index and entity index to paths represented by relation name and entity name | def filter_paths(self, paths):
formatted_paths = set()
for path in paths:
formatted_path = []
if self.include_entity:
if len(path) == 3:
continue
formatted_path.append(self.idx_to_node[path[0]].get_name())
for rd... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_and_filter_paths(self, source, target, relation, label, paths):\n file_dir = os.path.join(self.save_dir, relation + \"_\" + str(self.maximum_length) + \"_\" + str(self.remaining_percentage) + \"_\" + str(self.random_seed) + \".txt\")\n with open(file_dir, \"a\") as fh:\n fh.write... | [
"0.58268344",
"0.52432096",
"0.5228353",
"0.51687616",
"0.5044035",
"0.5042572",
"0.4956355",
"0.49512407",
"0.4946628",
"0.4930969",
"0.49182546",
"0.49161366",
"0.4869944",
"0.48368976",
"0.4831286",
"0.48303708",
"0.48242262",
"0.48204356",
"0.48135132",
"0.48054898",
"0.4... | 0.6383274 | 0 |
Validates if each elements contain the previous and their are equal | def verify_chain(blockchain):
for index, block in enumerate(blockchain):
if index == 0:
continue
if block.previous_hash != hash_block(blockchain[index - 1]):
return False
if not Verification.valid_proof(block.transactions[:-1], block.previous... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allsame(xs):\n assert len(xs) > 0\n return all(x == xs[0] for x in xs[1:])",
"def check():\r\n for a in l:\r\n for b in l:\r\n assert not(b in comes_after[a] and a in comes_after[b])",
"def all_same(items):\n \n return all(x == items[0] for x in items)",
"def ... | [
"0.67529505",
"0.6488937",
"0.642974",
"0.6424669",
"0.6401508",
"0.63954824",
"0.63903385",
"0.6365632",
"0.6273884",
"0.62532556",
"0.6236504",
"0.61716396",
"0.612882",
"0.6124451",
"0.61181426",
"0.61021763",
"0.61009073",
"0.6067597",
"0.60629946",
"0.60506934",
"0.60454... | 0.0 | -1 |
Initialize the state of the filter with a mean and covariance (uncertainty) Docstring | def _init_state(self, init_state=None, init_cov=None):
## Initialize the BMI state, assuming
nS = self.n_states
if init_state == None: ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.mean = 0.0\n self.std = 1.0",
"def __init__(self, mean, var=2):\n\n self.mean = mean\n self.var = var",
"def __init__(self, mean=None, cov=1):\r\n self.dim, self.mean, self.cov = _process_parameters(None, mean, cov)\r\n self.prec_U, self._log... | [
"0.68799675",
"0.61874145",
"0.6156245",
"0.60888946",
"0.60776407",
"0.60061157",
"0.59723383",
"0.5936516",
"0.58487064",
"0.58445746",
"0.58444643",
"0.57843155",
"0.5782224",
"0.57818365",
"0.5778741",
"0.57758945",
"0.5774353",
"0.5745278",
"0.57012343",
"0.5694429",
"0.... | 0.5583767 | 35 |
Probability of an random event theta given current state s. | def theta_given_s(theta, q):
if q == 0:
return .3333
else:
if theta == 0:
return 0.25
elif theta == 1:
return 0.25
else:
return 0.5 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _starting_prob(self, s):\n return self._starting_state_distribution.pdf(s)",
"def compute_probability_of_state(state):\n p = compute_log_probability_of_text(state[\"text\"], state[\"char_to_ix\"], \n state[\"frequency_statistics\"], state[\"transition_matrix\"... | [
"0.67219484",
"0.6633048",
"0.6455988",
"0.64483786",
"0.6309092",
"0.6300978",
"0.62985826",
"0.6290623",
"0.62792915",
"0.62534195",
"0.62534195",
"0.62534195",
"0.62534195",
"0.62534195",
"0.6252587",
"0.62439626",
"0.623991",
"0.62372935",
"0.62270015",
"0.62248063",
"0.6... | 0.579814 | 59 |
Multiperiod commitments in the next epoch. | def new_w(w, d):
if w.sum() > 0:
next_w = w.copy()
next_w[next_w > 0] -= 1
return next_w
else:
if d[0] == 1:
return np.array([51,0,0])
elif d[1] == 1:
return np.array([0,51,0])
else:
return np.array([0,0,51]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _momentum_update_key_encoder(self) -> None:\n momentum = self.encoder_momentum\n for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):\n param_k.data = param_k.data * momentum + param_q.data * (1.0 - momentum)",
"def bulk_modulus():\n\n return ... | [
"0.5135255",
"0.5123099",
"0.51157635",
"0.50703293",
"0.5054246",
"0.49106103",
"0.4865669",
"0.4844311",
"0.48381487",
"0.48045045",
"0.47987068",
"0.4731387",
"0.4711523",
"0.47011307",
"0.46949154",
"0.4683865",
"0.46607396",
"0.4658189",
"0.46556586",
"0.46524188",
"0.46... | 0.0 | -1 |
Attraction function of resource (h in the paper). | def attraction_h(next_r, a):
if a == 0:
if next_r == 9:
return 0.8
elif next_r == 14:
return 0.1
else:
return 0.1
elif a == 1:
if next_r == 9:
return 0.1
elif next_r == 14:
return 0.1
else:
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _desc_op(attr_name):",
"def attributes(self):\n ...",
"def attributes(self):",
"def attr(*args, **kwargs):\n return Attr(*args, **kwargs)",
"def __getattr__(self, attr):\n for resource in lineage(self):\n if attr in resource.__initial_attrs__:\n value = self._... | [
"0.633073",
"0.6253096",
"0.62399495",
"0.58977604",
"0.5896142",
"0.58748055",
"0.58748055",
"0.5810813",
"0.57991314",
"0.5772088",
"0.57460266",
"0.5722806",
"0.568998",
"0.56712985",
"0.55946434",
"0.55810964",
"0.55440104",
"0.55188745",
"0.5515306",
"0.5512768",
"0.5506... | 0.0 | -1 |
Attraction function of operational conditions (g in the paper). | def attraction_g(next_q, q, d, a):
if a == 0:
if next_q == 0:
xi_D = 8
else:
xi_D = 1
elif a == 1:
xi_D = 1
elif a == 2:
if next_q == 0:
xi_D = 1
else:
xi_D = 3
elif a == 3:
if next_q == 0:
xi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Mutation(self, state):\n changed = False;\n #-------------------------------------------------------\n # MUTATE CONDITION\n #-------------------------------------------------------\n for att in range(cons.env.format_data.numb_attributes): #Each condition specifies different ... | [
"0.59705275",
"0.59240454",
"0.5861633",
"0.57576585",
"0.5615792",
"0.54887545",
"0.543721",
"0.5429981",
"0.5418968",
"0.5392196",
"0.53746855",
"0.5364727",
"0.53537273",
"0.53473896",
"0.5323972",
"0.5315444",
"0.53144354",
"0.52830577",
"0.5280386",
"0.5256036",
"0.52460... | 0.54523236 | 6 |
Probability of decision d from state s to state next_s | def trans_prob(next_s, q, d):
next_q, next_r, next_w = next_s
A_actions = [0, 1, 2, 3, 4]
prob = 0
for a in A_actions:
prob_r = attraction_h(next_r[0], a)
q1 = attraction_g(next_q[0], q, d, a)
q2 = attraction_g(1-next_q[0], q, d, a)
prob_q = q1 / (q1 + q2)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transition_probability(self, s, a, s1):\n unreachable_states = [4, # F with prod_score == 4\n 5] # M with prod_score == 0\n\n if s1 in unreachable_states:\n return 0\n else:\n return 1 / (self.n_states - len(unreachable_states))",
"de... | [
"0.7248015",
"0.7077099",
"0.69555706",
"0.6560675",
"0.6411908",
"0.6363046",
"0.63058686",
"0.6260645",
"0.6218823",
"0.61547095",
"0.61078906",
"0.60890436",
"0.6088",
"0.6061975",
"0.60540265",
"0.6049762",
"0.60051227",
"0.5988864",
"0.59776247",
"0.5952194",
"0.5938377"... | 0.6716084 | 3 |
Convert single line in Instruction instance. | def process_line(line: str) -> Instruction:
register, op, value, _, base, check, limit = line.split()
return Instruction(register, op, int(value), base, check, int(limit)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trans_line(line: str, progname):\r\n splitline = line.split()\r\n command = splitline[0]\r\n if command == 'push':\r\n segment = splitline[1]\r\n index = splitline[2]\r\n out = mem.push(segment, index, progname)\r\n elif command == 'pop':\r\n segment = splitline[1]\r\n ... | [
"0.6149348",
"0.60882974",
"0.60344046",
"0.59739935",
"0.5775252",
"0.5676443",
"0.56726754",
"0.56274086",
"0.5606504",
"0.5577222",
"0.55470794",
"0.5473916",
"0.5454122",
"0.54540783",
"0.54350394",
"0.5411006",
"0.5390487",
"0.5379209",
"0.53752047",
"0.53626865",
"0.535... | 0.6650071 | 0 |
Convert raw data in the easytouse list of Instruction instances. | def process_data(data: str) -> list[Instruction]:
instructions = []
for line in data.strip().split("\n"):
instruction = process_line(line)
instructions.append(instruction)
return instructions | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract(input_data: str) -> list:\n instructions = list()\n for instruction in input_data.split('\\n'):\n op, arg = instruction.split(' ')\n arg = int(arg)\n assert op in ('acc', 'jmp', 'nop')\n instructions.append(Instruction(op, arg))\n return instructions",
"def _perfo... | [
"0.5894083",
"0.580195",
"0.56562746",
"0.5608601",
"0.55650634",
"0.55505216",
"0.55473304",
"0.5376276",
"0.53636885",
"0.5328439",
"0.5309102",
"0.52925634",
"0.52925086",
"0.5272285",
"0.5267069",
"0.5258318",
"0.5257359",
"0.5243541",
"0.5228365",
"0.5214779",
"0.5211774... | 0.63322127 | 0 |
Apply all instructions and return registers + the biggest value seen. | def perform_instructions(
instructions: list[Instruction],
) -> tuple[DefaultDict[str, int], int]:
registers: DefaultDict[str, int] = defaultdict(int)
biggest = 0
for instruction in instructions:
update = OPERATORS[instruction.op]
check = OPERATORS[instruction.check]
register = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_most_valuable(self):\n return self.most_valuable",
"def computeActionFromValues(self, state):\n \"*** YOUR CODE HERE ***\"\n maxvalue = -100000000\n bestaction = None\n for action in self.mdp.getPossibleActions(state):\n valueforthisaction = self.getQValue(stat... | [
"0.5996123",
"0.5894548",
"0.5865219",
"0.5787033",
"0.5779433",
"0.57666355",
"0.56985855",
"0.56887287",
"0.56882644",
"0.5670334",
"0.56451887",
"0.56444883",
"0.5636384",
"0.56336486",
"0.56252307",
"0.55974555",
"0.55864197",
"0.5554566",
"0.55422884",
"0.5532165",
"0.55... | 0.65974396 | 0 |
Find the biggest register. | def solve(task: str) -> int:
instructions = process_data(task)
registers, _ = perform_instructions(instructions)
return max(registers.values()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def return_the_maximum(self):\n\n return self.__max_stack[-1]",
"def find_max(self):\n if self.right:\n return self.right.find_max()\n return self.data",
"def find_max(self):\n return max(self.nodes, key=int)",
"def find_max(self):\n\n if self.right:\n ... | [
"0.65381134",
"0.6531537",
"0.64896965",
"0.6431575",
"0.6377627",
"0.6357117",
"0.6151583",
"0.60980093",
"0.6092156",
"0.60648984",
"0.60378355",
"0.6018988",
"0.6000243",
"0.5955349",
"0.5946053",
"0.5944906",
"0.59375596",
"0.5931247",
"0.5931247",
"0.5929218",
"0.5920487... | 0.0 | -1 |
Convert a URL to IDN notation | def _convert_to_idn(url):
# this function should only be called with a unicode string
# strategy: if the host cannot be encoded in ascii, then
# it'll be necessary to encode it in idn form
parts = list(urllib.parse.urlsplit(url))
try:
parts[1].encode('ascii')
except UnicodeEncodeError:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def url_to_doi(url):\n return url[url.index(prefix):].rstrip(url_suffix).rstrip(INT_URL_SUFFIX)",
"def iri2uri(uri): \r\n if isinstance(uri ,unicode):\r\n (scheme, authority, path, query, fragment) = urlparse.urlsplit(uri)\r\n authority = authority.encode('idna')\r\n # For each charact... | [
"0.7021145",
"0.65356356",
"0.6437324",
"0.6412383",
"0.6395479",
"0.6311997",
"0.61961514",
"0.6158848",
"0.61555415",
"0.6119481",
"0.6110008",
"0.61025923",
"0.60946435",
"0.6066466",
"0.6030167",
"0.6021279",
"0.6020139",
"0.6007491",
"0.60052556",
"0.5983566",
"0.5945938... | 0.8388875 | 0 |
One epoch is a single tournament here | def one_epoch(self, tournament_id: int, epoch=0):
# TODO: tournament pre-fetcher
tournament = Tournament(tournament_id, cache=self.cache)
# Measure correlation before to see whether gradient update took effect
correlation_before = self.get_prediction_correlation(tournament)
cor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tournament(self):\n pass",
"def train_one_epoch(self):\n raise NotImplementedError",
"def train_epoch(self) -> None:\n ct = self.config.training\n total_games = self._get_total_games()\n print(f\"Total Games: {total_games}\")\n train_size = int(0.9 * total_games)\n... | [
"0.6938331",
"0.6872798",
"0.6221495",
"0.61553615",
"0.6041733",
"0.5884427",
"0.58615357",
"0.5858718",
"0.58503413",
"0.584667",
"0.58107626",
"0.5793915",
"0.57814837",
"0.5771732",
"0.57555825",
"0.57402873",
"0.5730085",
"0.5728672",
"0.57039595",
"0.57039595",
"0.57039... | 0.7137898 | 0 |
Get scores for all the teams | def get_scores(self, tournament: Tournament):
self.model.eval()
# collate_fn = lambda x: collate_teams(x, tournament.max_members)
dl_rank = DataLoader(tournament.ranking, num_workers=self.jobs, batch_size=self.bs, shuffle=False)
iterator = tqdm(dl_rank, position=0, desc=f'{tournament.tou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_list_team_scores(self):\n scores = defaultdict(lambda: {\n \"scored_xg\": [],\n \"conceded_xg\": [],\n \"home_adv\": 0,\n \"expected_points\": 0\n })\n\n for g in self.games:\n scores[g.HomeTeam][\"scored_xg\"].append(g.FTHG)\n ... | [
"0.77148676",
"0.6926027",
"0.6908442",
"0.6832203",
"0.68074715",
"0.67282873",
"0.67100906",
"0.6675928",
"0.66030395",
"0.6575878",
"0.6529839",
"0.647783",
"0.63599753",
"0.63421893",
"0.6304855",
"0.63044083",
"0.62909013",
"0.6269161",
"0.62505054",
"0.62181664",
"0.620... | 0.7616093 | 1 |
suit and value should be integers | def __init__(self, value, suit) -> None:
self.value = value
self.suit = suit | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, value, suit):\n self.value = value # A,2,3,4,5,6,7,8,9,10,J,Q, or K\n self.suit = suit # hearts, diamonds, clubs, spades",
"def test_is_suit_integer(self):\n self.assertIsInstance(cardutils.Card(10,1).suit, int)",
"def suit(self):\r\n\t\tsuit = self.n // 13\r... | [
"0.7262717",
"0.7030172",
"0.67238253",
"0.66163784",
"0.65954757",
"0.65391135",
"0.6420939",
"0.6411027",
"0.6410355",
"0.6391943",
"0.6384562",
"0.63782066",
"0.63565224",
"0.63530475",
"0.6339618",
"0.63249195",
"0.62757915",
"0.62429863",
"0.6201983",
"0.61999196",
"0.61... | 0.7071617 | 1 |
Function to mimic the 'fspecial' gaussian MATLAB function | def _tf_fspecial_gauss(size, sigma):
x_data, y_data = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]
x_data = np.expand_dims(x_data, axis=-1)
x_data = np.expand_dims(x_data, axis=-1)
y_data = np.expand_dims(y_data, axis=-1)
y_data = np.expand_dims(y_data, axis=-1)
x = tf.constant(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gaussian(x, mean, sigma):\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))",
"def gaussian(x, mu, sigma):\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\n np.sqrt(2.0 * np.pi) / sigma)",
"def _FSpecialGauss(size, sigma):\n radius = size // 2\n ... | [
"0.76750207",
"0.7655075",
"0.74836683",
"0.7482679",
"0.74079776",
"0.7406864",
"0.7400095",
"0.7362913",
"0.732379",
"0.7293053",
"0.72702783",
"0.72199357",
"0.7076322",
"0.70740974",
"0.70684606",
"0.7040425",
"0.7029026",
"0.7005637",
"0.69755626",
"0.6971254",
"0.693980... | 0.6823641 | 31 |
Return comments tree by entity or root comment | async def get_comments_tree(request):
comment_id = request.match_info.get('comment_id')
if comment_id:
# valitation was in route (\d+)
comment_id = int(comment_id)
tree = CommentsTreeDAO.create_by_parent(comment_id)
else:
entity_type = request.match_info.get('entity_type')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_by_entity(entity_type, entity_id, only_roots=False):\n\n return CommentsTreeDAO(entity_type=entity_type, entity_id=entity_id,\n only_roots=only_roots)",
"async def fetch(self, conn, page=None, fdt=None, tdt=None):\n\n sql = \"\"\"SELECT\n ... | [
"0.65842324",
"0.63664144",
"0.63229674",
"0.6065913",
"0.6050549",
"0.6045573",
"0.60114443",
"0.5975179",
"0.5946544",
"0.58939505",
"0.587878",
"0.58299065",
"0.58249295",
"0.5780401",
"0.57469136",
"0.5738545",
"0.57361287",
"0.57340264",
"0.5731925",
"0.5722351",
"0.5712... | 0.7140785 | 0 |
Chains together managment commands for procurement work flow python manage.py reset products noinput python manage.py get_products python manage.py reset ordering noinput python manage.py get_orders python manage.py create_procurement_items | def shops_support_commands(request):
messages.add_message(request, messages.INFO, 'Starting Procurements. An SMS will be sent to 0402 231 007 when complete.')
shops_support_commands_task.delay()
return HttpResponseRedirect('/') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\n parser = argparse.ArgumentParser(\n description='Manage third-party software.'\n )\n\n parser.add_argument('-d', '--debug', action='store_true',\n help='Enable debug output.')\n parser.add_argument('-v', '--verbose', action='store_true',\n ... | [
"0.587289",
"0.5801472",
"0.57531935",
"0.565767",
"0.5591324",
"0.5529541",
"0.5521418",
"0.5504237",
"0.54831237",
"0.54783577",
"0.54764533",
"0.54698676",
"0.5404664",
"0.537019",
"0.5349617",
"0.53430337",
"0.53267956",
"0.53138334",
"0.52388555",
"0.5222266",
"0.5183527... | 0.52462137 | 18 |
A Shortcut View to create procurements for all Shops, resetting and fetching Products & Orders | def shops_procurement_email_csv(request):
Order.objects.all().delete()
Product.objects.all().delete()
procurements = Procurement.objects.all()
if procurements:
response = HttpResponse(mimetype='text/csv')
response['Content-Disposition'] = 'attachment; filename=procurement_%s.csv' % pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _action_procurement_create(self):\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\n new_procs = self.env['procurement.order'] #Empty recordset\n for line in self:\n if line.state != 'sale' or not line.product_id._need_procurement():\n ... | [
"0.5863821",
"0.55736905",
"0.54319197",
"0.53814894",
"0.5258747",
"0.5124642",
"0.50894547",
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"0.5004225",
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"0.49435654",
"0.4941564",
"0.4911613",
"0.48936176",
"0.4875826",
"0.484... | 0.46033427 | 53 |
Yield successive nsized chunks from l. | def chunks(l, n):
for i in range(0, len(l), n):
yield l[i:i + n] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _chunk(self, l, n):\n for i in range(0, len(l) + 1, n):\n yield l[i:i + n]",
"def chunks(self, l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def __chunks(l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def get_chunks(self, ... | [
"0.80388075",
"0.7924573",
"0.79226995",
"0.788447",
"0.7877021",
"0.781505",
"0.7764907",
"0.77549165",
"0.7743869",
"0.7731126",
"0.77281487",
"0.77241385",
"0.77024275",
"0.7688614",
"0.7688614",
"0.7663561",
"0.7656419",
"0.76552874",
"0.76552874",
"0.76452404",
"0.764524... | 0.7448291 | 79 |
read (filename) and build a dictionary that maps from each word to a string that describes its primary pronunciation. Secondary pronunciations are added to the dictionary with a number, in parentheses, at the end of the key, so the key for the second pronunciation of "abdominal" is "abdominal(2)". | def read_dictionary(filename='/Users/Paul/Documents/c06d.txt'):
d = dict()
fin = open(filename)
for line in fin:
# skip over the comments
if line[0] == '#': continue
t = line.split()
word = t[0].lower()
pron = ' '.join(t[1:])
d[word] = pron
return d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_pronunciation(pronunciation_file):\n # file = open('dictionary.txt', 'r')\n #\n # for line in file:\n # print line\n\n ################# https://m.reddit.com/r/CompSciPortfolio/comments/303fyo/assignment_3_poetry_reader/\n\n pronunciation_dictionary = {}\n line = pronunciation_f... | [
"0.7332753",
"0.6848692",
"0.6703922",
"0.6669032",
"0.65965074",
"0.6547224",
"0.6499856",
"0.64841783",
"0.62861496",
"0.62773955",
"0.60968024",
"0.60919213",
"0.60109735",
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"0.5963126",
"0.5958272",
"0.5946235",
"0.5933027",
"0.5931318",
"0.5922631",
"0.590980... | 0.65141857 | 6 |
Checks to see if two words can be pronounced the same way | def homophones(a, b):
if a not in phonetic or b not in phonetic:
return False
return phonetic[a] == phonetic[b] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def one_away(w1, w2):\n\n if abs(len(w1) - len(w2) > 1):\n return False\n\n # i = 0\n # w1_d = {}\n # w2_d = {}\n\n # for i in w1:\n # w1_d[i] = w1.count(i)\n\n # for j in w2:\n # w2_d[j] = w2.count(j)\n\n # unmatched = set(w1_d.items())^set(w2_d.items())\n \n # ... | [
"0.7271517",
"0.71494365",
"0.709981",
"0.7089381",
"0.70110524",
"0.6998903",
"0.6914212",
"0.68869525",
"0.68457186",
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"0.667189",
"0.65579796",
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"0.6532438",
"0.6524997",
"0.6474966",
"0.6412077",
"0.6397953",
"0.63940746",
"0.63526577... | 0.59207153 | 78 |
checks to see if word is in dictionary, then checks if homophones | def word_check(word):
word1 = word[1:]
if word1 not in word_dict: return False
if not homophones (word, word1): return False
word2 = word[0] + word[2:]
if word2 not in word_dict: return False
if not homophones(word, word2): return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def homophone_words(word_one, word_two, pron_dict):\n if word_one not in pron_dict or word_two not in pron_dict:\n return False\n return pron_dict[word_one] == pron_dict[word_two]",
"def homophones():\n pron = pronounce.read_dictionary('c06d')\n words = mkwrddct('words.txt')\n\n for word in... | [
"0.7909334",
"0.7649232",
"0.7379768",
"0.70193833",
"0.66508675",
"0.6501066",
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"0.62256026",
"0.6210163",
"0.6180701",
"0.6136245",
"0.6133826",
"0.61200... | 0.80829525 | 0 |
r""" Bayesian Optimizer that uses a neural network employing a MultiLayer Perceptron with Batchnorm layers. | def __init__(self, batch_size=10, mlp_params=None, normalize_input=True,
normalize_output=True, rng=None, debug=False, tb_logging=False, tb_log_dir="runs/", tb_exp_name="exp",
learn_affines=True, running_stats=True, bn_momentum=0.1):
super(BatchNorm, self).__init__(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_bayesian_optimization(self):\n bounds = {'hunits': (self.hunits_lower, self.hunits_upper),\n 'embedding_dim': (self.embedding_dim_lower, self.embedding_dim_upper)}\n optimizer = BayesianOptimization(f=self.lstm_score, pbounds=bounds, random_state=1)\n optimizer.max... | [
"0.71689683",
"0.63594383",
"0.63594383",
"0.62631476",
"0.62388253",
"0.6200204",
"0.6180111",
"0.61708254",
"0.6165025",
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"0.6114282",
"0.61016905",
"0.60585445",
"0.6051677",
"0.6002721",
"0.5985357",
"0.5974796",
"0.59617877",
"0.59446186",
"0.59385335",
"0.59... | 0.0 | -1 |
r""" Fit the model to the given dataset (X, Y). | def fit(self, X, y, **kwargs):
start_time = time.time()
self.X = X
self.y = y
# Normalize inputs and outputs if the respective flags were set
self.normalize_data()
self.y = self.y[:, None]
# Create the neural network
# self._init_nn()
optimize... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, X, Y):\n ...",
"def fit(self, X, y):",
"def fit(self, X, y):",
"def fit(self, X, y):",
"def fit(self, X,y):\n pass",
"def fit(self, X, y=...):\n ...",
"def fit(self, X, y=...):\n ...",
"def fit(self, X, y=...):\n ...",
"def fit(self, X, y=...):\n ... | [
"0.8406436",
"0.8188824",
"0.8188824",
"0.8188824",
"0.81622016",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.80797446",
"0.8041557",
"0.80190223",
"0.7940643",
"0.7909361",
"0.7909361",
"0.7... | 0.0 | -1 |
r""" Returns the predicted output for a trained network on the given test set. | def predict(self, X_test, **kwargs):
# Normalize inputs
if self.normalize_input:
X_, _, _ = zero_mean_unit_var_normalization(X_test, self.X_mean, self.X_std)
else:
X_ = X_test
# Sample a number of predictions for each given point
# Generate mean and vari... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(self, test_set, test_labels):\n\n with tf.Session() as self.tf_session:\n self.tf_saver.restore(self.tf_session, self.models_dir + self.model_name)\n return self.accuracy.eval({self.input_data: test_set, self.input_labels: test_labels})",
"def predict(self, X_test):\n ... | [
"0.7535142",
"0.74656874",
"0.7193928",
"0.7173669",
"0.7117496",
"0.7112967",
"0.7091167",
"0.708785",
"0.7062996",
"0.706108",
"0.7033131",
"0.7005197",
"0.6984002",
"0.69106984",
"0.68712544",
"0.67788756",
"0.6778434",
"0.6761069",
"0.6746445",
"0.6740832",
"0.6730758",
... | 0.6451093 | 43 |
Generate the positions from trace | def posns_from_trace(trace):
posns = []
for i in range((len(trace.variables)-1)//2):
var_x = trace.variables[2*i]
var_y = trace.variables[2*i+1]
car_i = int(var_x.name.split('_')[2])
xy = (var_x.value.item(), var_y.value.item())
if len(posns) <= car_i:
pos... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def positions(self, tileID, numSamples):",
"def BeamPosition():\n \n XPOS, YPOS = [], []\n\n x=0\n for j in range(0,6,1):\n x += 0.1\n y=0\n for k in range(0,6,1):\n y += 0.2\n XPOS.append(x)\n YPOS.append(y)\n\n return XPOS, YPOS",
"def gene... | [
"0.6406588",
"0.62758404",
"0.62750536",
"0.61463076",
"0.611264",
"0.6014426",
"0.6004055",
"0.5968917",
"0.5884226",
"0.5875474",
"0.5816131",
"0.5784888",
"0.5737535",
"0.5731324",
"0.5729159",
"0.5626728",
"0.56128687",
"0.5607636",
"0.5580684",
"0.5576681",
"0.5570371",
... | 0.706652 | 0 |
generate the posterior based on an input. | def get_posterior(args, observation):
model = models[args.model_name](args)
model.load_inference_network(args.loadnetwork)
posterior = model.posterior(
num_traces=500,
inference_engine=pyprob.InferenceEngine.IMPORTANCE_SAMPLING_WITH_INFERENCE_NETWORK,
observe={'depth_image_sample':... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_posterior(self):\n raise NotImplementedError('Abstract Method')",
"def build_posterior(self):\n # It looks like GPytorch builds posterior every time eval is made.\n pass",
"def posterior_sample(self):\n pass",
"def build_posterior(self):\n if self.gp_core.alpha is... | [
"0.7398055",
"0.7344519",
"0.6750742",
"0.673047",
"0.6572502",
"0.6513751",
"0.64071006",
"0.6303324",
"0.6276649",
"0.6235326",
"0.62301993",
"0.621928",
"0.6217684",
"0.61377573",
"0.6078299",
"0.60650116",
"0.60024667",
"0.59992296",
"0.59911585",
"0.59453493",
"0.5909928... | 0.6548378 | 5 |
make a box for plotting | def get_box(x_tr, y_tr, d=1.):
xs = np.array([-1., 1., 1., -1., -1.])
ys = np.array([-1., -1., 1., 1., -1.])
xs = xs*d/2 + x_tr
ys = ys*d/2 + y_tr
return xs, ys | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plotBox(box):\n plt.plot([box.xll, box.xur, box.xur, box.xll, box.xll]\n ,[box.yll, box.yll, box.yur, box.yur, box.yll]\n , '-'\n )",
"def boxPlot(self):\n clf()\n boxplot(self.y,positions=self.x,widths=0.5)\n xlabel('X Label (units)')\n ylabel(... | [
"0.80755407",
"0.7761877",
"0.74262625",
"0.7113986",
"0.7050683",
"0.69609755",
"0.69441116",
"0.6927388",
"0.6886567",
"0.68664503",
"0.68428904",
"0.6782563",
"0.6713756",
"0.66915447",
"0.66915447",
"0.66470635",
"0.65735435",
"0.6524072",
"0.6489653",
"0.6466975",
"0.645... | 0.0 | -1 |
provide a KDE for where the car is | def kde(posterior, kernel=car_kernel):
x = np.linspace(-10, 10, 256)
y = np.linspace(-10, 10, 256)
XX, YY = np.meshgrid(x, y)
XX = torch.tensor(XX)
YY = torch.tensor(YY)
def expectation_func(trace):
Z = torch.zeros_like(XX)
posns = posns_from_trace(trace)
for xy in pos... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _kde_example(data):\n # Plot the data\n ch = chartify.Chart(blank_labels=True, y_axis_type=\"density\")\n ch.set_title(\"KDE plot\")\n ch.plot.kde(data_frame=data, values_column=\"unit_price\", color_column=\"fruit\")\n ch.show(_OUTPUT_FORMAT)",
"def _kde_example2(data):\n # Plot the data\n... | [
"0.6838983",
"0.62949854",
"0.6099632",
"0.60312885",
"0.5927605",
"0.5870046",
"0.5711485",
"0.5612323",
"0.5606829",
"0.55595857",
"0.5485947",
"0.5443569",
"0.5423928",
"0.54207367",
"0.5415106",
"0.53608185",
"0.5336951",
"0.5311593",
"0.52499145",
"0.5243915",
"0.5241838... | 0.5098719 | 34 |
Create an observation using seed. | def generate_observation(args):
model = models[args.model_name](args)
pyprob.set_random_seed(args.seed)
trace = model.get_trace(generate_samples=True, verbose=True)
number_of_vehicles, image_obs = trace.result
posns = posns_from_trace(trace)
return posns, model.x, model.y, image_obs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def seed():",
"def create_observation(self):\n return self._user_state.create_observation()",
"def seed(*args, **kwargs): # real signature unknown\n pass",
"def seed():\n pass",
"def seed():\n pass",
"def seed(self, seed=None):\n raise NotImplementedError()",
"def seed(self, seed=Non... | [
"0.70108116",
"0.66705865",
"0.66530013",
"0.6639101",
"0.6639101",
"0.646656",
"0.646656",
"0.6427266",
"0.6306611",
"0.6306611",
"0.6264364",
"0.61795664",
"0.61635995",
"0.61292714",
"0.61187625",
"0.5996392",
"0.5968281",
"0.59193254",
"0.5916746",
"0.591524",
"0.5906138"... | 0.0 | -1 |
This fn prints and plots the confusion matrix. Normalization can be applied by setting 'normalize=True'. | def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, class... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n... | [
"0.79747516",
"0.7972701",
"0.7916397",
"0.78010035",
"0.77952605",
"0.7757487",
"0.7756975",
"0.775108",
"0.77393556",
"0.7737718",
"0.7737082",
"0.7719532",
"0.7714036",
"0.7700674",
"0.7696943",
"0.7685643",
"0.76693594",
"0.76639557",
"0.766282",
"0.766219",
"0.7659201",
... | 0.767042 | 16 |
Create and show the display on the screen. After that, obtain the positions for all the parts in the display so other mods (namely ClickManager) can figure out where mouse events are. | def pysweep_before_finish_init(self):
self.displaycanvas = DisplayCanvas(self.pysweep.master, self.boardsize, self.lcounterlength, self.rcounterlength, self.images)
self.displaycanvas.pack()
self.pysweep.master.update_idletasks()
self.displaycanvas.update_idletasks()
# enode = s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_display (self, *display_args, **kw):\n # first display argument is always rect; crop it to fit on the screen\n rect = pygame.Rect(display_args[0]).clip(self.screen.get_rect())\n if any(rect.colliderect(d.rect) for d in self.displays):\n raise ValueError('rect overlaps other... | [
"0.68203926",
"0.6793573",
"0.66642714",
"0.6488153",
"0.64703375",
"0.64546627",
"0.6423563",
"0.6343933",
"0.6342656",
"0.63396937",
"0.6333554",
"0.6326806",
"0.6315218",
"0.6275464",
"0.6239657",
"0.62251455",
"0.62184066",
"0.62117213",
"0.616774",
"0.6149395",
"0.614371... | 0.0 | -1 |
Parts should call draw on its child parts. It should determine if a change has been made, and if so, make the change and call update. If a part has pasted outside its region, it should return True Parts should not make changes to the display until draw has been called! This is because the order parts are drawn is impor... | def draw(self, force=False):
self.display.draw(force) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw(self, force = False):\n\t\tpass",
"def _onPaint(self, evt):\n if not self._isRealized:\n self.realize()\n if self._drawn < 2:\n self.draw(repaint = False)\n self._drawn += 1\n self.gui_repaint(drawDC=wx.PaintDC(self))",
"def draw(self, force=False)... | [
"0.6380005",
"0.593526",
"0.5855134",
"0.572638",
"0.56879467",
"0.564566",
"0.56153196",
"0.5614835",
"0.5576688",
"0.55423486",
"0.5540616",
"0.55378747",
"0.55355537",
"0.5513865",
"0.55125195",
"0.54864395",
"0.5464399",
"0.5464399",
"0.54607123",
"0.5451927",
"0.5438394"... | 0.5939307 | 1 |
Call this to trigger an update (lazily) Call with True to force a redraw of all its children | def draw(self, force=False):
for child in self.children.values():
child.draw(force) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n self.redraw()\n self._changed = False",
"def update(self):\n self.redraw()\n self._changed = False",
"def redraw(self):\r\n self.c.update()",
"def _refresh(self):\n self._need_display_update = True\n self._update()",
"def _DoUpdateRedraw(... | [
"0.76514983",
"0.76514983",
"0.72221994",
"0.71962154",
"0.71209574",
"0.70450413",
"0.7022925",
"0.7000538",
"0.6958265",
"0.6921717",
"0.69070524",
"0.6898503",
"0.6888555",
"0.68308216",
"0.67871916",
"0.6729949",
"0.67185277",
"0.6715382",
"0.6713235",
"0.66742736",
"0.66... | 0.6394662 | 35 |
Returns True if the coord is in Part or any of its children. May be a better idea to call the get_part_containing function instead though, which returns the lowest level Part that contains the coord (none of its children contain the coord, but the Part does) | def contains(self, coord):
# print(coord, self.position, self.size)
return (0 <= coord[0] - self.position[0] < self.size[0] and
0 <= coord[1] - self.position[1] < self.size[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_part_containing(self, coord):\n # print('in', self)\n for k, child in self.children.items():\n # print('try', k, child)\n if child.ignore:\n # print('ignore', k, child)\n continue\n if child.contains(coord):\n # pri... | [
"0.75447255",
"0.6645123",
"0.66114485",
"0.6500257",
"0.6442654",
"0.64163774",
"0.6345653",
"0.63166255",
"0.61932963",
"0.6176176",
"0.6176057",
"0.61623186",
"0.61566186",
"0.6145891",
"0.6117253",
"0.60773057",
"0.60773057",
"0.6069949",
"0.6057143",
"0.6003461",
"0.5990... | 0.7134822 | 1 |
Returns the lowest Part that contains the coord (a part that contains the coord where none of its children contain the coord) Assumes that self already contains coord! Please check this if you are not sure! | def get_part_containing(self, coord):
# print('in', self)
for k, child in self.children.items():
# print('try', k, child)
if child.ignore:
# print('ignore', k, child)
continue
if child.contains(coord):
# print('contained... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findMin(self):\n curr = self\n while curr.hasLeftChild():\n curr = curr.leftChild\n return curr",
"def get_parent_by_coord(x, y, w, h, states: [State]) -> State:\n parents = [state for state in states if is_state_a_child_by_coord(x, y, w, h, state)]\n if not parents:\n ... | [
"0.65753007",
"0.64252496",
"0.6329845",
"0.6284443",
"0.6221933",
"0.6202329",
"0.61960083",
"0.61666095",
"0.6122884",
"0.60579133",
"0.601533",
"0.59646225",
"0.59528434",
"0.5939711",
"0.58846384",
"0.5878549",
"0.5874045",
"0.5835615",
"0.58301",
"0.5770619",
"0.5761702"... | 0.7808834 | 0 |
perform splink score histogram calculations / internal function Compute a histogram using the provided buckets. | def _calc_probability_density(
df_e: DataFrame,
spark: SparkSession,
buckets=None,
score_colname="match_probability",
symmetric=True,
):
if score_colname == "match_probability":
extent = (0.0, 1.0)
else:
weight_max = df_e.agg({score_colname: "max"}).collect()[0][0]
w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_histograms_from_assignments(self, funcs, bin_size=1):\n result = defaultdict(lambda: defaultdict(\n lambda: Histogram(bin_size)\n ))\n for assignment in self.get_assignment_reader():\n for name, func in funcs.iteritems():\n value = func(assign... | [
"0.63164794",
"0.6300944",
"0.6296864",
"0.62763315",
"0.6250284",
"0.6250284",
"0.6125742",
"0.6084646",
"0.6065625",
"0.60559726",
"0.6052646",
"0.60354984",
"0.59535027",
"0.5911998",
"0.58973366",
"0.58958775",
"0.58551866",
"0.5843063",
"0.5798115",
"0.57924175",
"0.5762... | 0.5945126 | 13 |
splink score histogram diagnostic plot public API function Compute a histogram using the provided buckets and plot the result. | def splink_score_histogram(
df_e: DataFrame,
spark: SparkSession,
buckets=None,
score_colname=None,
symmetric=True,
):
rows = _calc_probability_density(
df_e,
spark=spark,
buckets=buckets,
score_colname=score_colname,
symmetric=symmetric,
)
retur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeHistogram(values, numBins, xLabel, yLabel, title=None):",
"def plot_hitstogram_graph(data_values, title,\r\n number_of_keys,\r\n max_val,\r\n file_in):\r\n\r\n # bins = max(data_values)\r\n # pylab.hist(data_values, facecolo... | [
"0.65873337",
"0.65499824",
"0.6416419",
"0.6398828",
"0.6303546",
"0.6264724",
"0.6263202",
"0.6211154",
"0.6202789",
"0.61877424",
"0.6169316",
"0.6159507",
"0.61091715",
"0.6084146",
"0.60816425",
"0.6069394",
"0.6049768",
"0.603087",
"0.60236806",
"0.6020201",
"0.6001724"... | 0.6923532 | 0 |
Returns event dictionary for given run, has form | def get_events(fname, x_axis='step'):
result = {}
events = summary_iterator.summary_iterator(fname)
try:
for event in events:
if x_axis == 'step':
x_val = event.step
elif x_axis == 'time':
x_val = event.wall_time
else:
assert False, f"Unknown x_axis ({x_axis})"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def event_message(iden: int, event: Any) -> dict[str, Any]:\n return {\"id\": iden, \"type\": \"event\", \"event\": event}",
"def create_events():\n events = {}\n events[\"Workers_can_proceed\"] = mp.Event()\n for i in range(NUM_WORKERS):\n events[i] = mp.Event()\n return events",
"def ge... | [
"0.5938505",
"0.5936111",
"0.57033306",
"0.5693008",
"0.5664735",
"0.5663289",
"0.562416",
"0.559962",
"0.55431575",
"0.55160534",
"0.55051327",
"0.54927003",
"0.5491153",
"0.54728276",
"0.54675347",
"0.545969",
"0.5454533",
"0.54335296",
"0.54042554",
"0.537985",
"0.5371194"... | 0.0 | -1 |
Convert an ascii format PSD to XML. | def _convert_psd(self, ascii_format, ifo):
command = ["convert_psd_ascii2xml",
"--fname-psd-ascii", f"{ascii_format}",
"--conventional-postfix",
"--ifo", f"{ifo}"]
pipe = subprocess.Popen(command,
std... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exportXml ( w, xml ):\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n rawText = xml\n pattern = re.compile (r'[^\\S ]+')\n text = re.sub ( pattern, \"\", rawText )\n reparsed = MD.parseString ( text )\n w.write ( reparsed.toprettyxml ( indent = \"\\t\", encoding = \"UTF-8\" ) )",
"def t... | [
"0.55738395",
"0.54602534",
"0.54489726",
"0.53523225",
"0.52660984",
"0.52540344",
"0.5241616",
"0.5230467",
"0.5184351",
"0.51782966",
"0.5153558",
"0.51349664",
"0.50608534",
"0.5040427",
"0.50386435",
"0.5021166",
"0.5018161",
"0.49920428",
"0.49768257",
"0.49715346",
"0.... | 0.6901742 | 0 |
Convert the textbased PSD to an XML psd if the xml doesn't exist already. | def before_submit(self):
event = self.production.event
category = config.get("general", "calibration_directory")
current = os.getcwd()
if len(self.production.get_psds("xml"))==0:
for ifo in self.production.meta['interferometers']:
os.chdir(f"{event.re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _convert_psd(self, ascii_format, ifo):\n command = [\"convert_psd_ascii2xml\",\n \"--fname-psd-ascii\", f\"{ascii_format}\",\n \"--conventional-postfix\",\n \"--ifo\", f\"{ifo}\"]\n \n pipe = subprocess.Popen(command, \n ... | [
"0.54699063",
"0.54270697",
"0.5086973",
"0.4949817",
"0.49382555",
"0.48763314",
"0.4858184",
"0.48364475",
"0.4789056",
"0.47216293",
"0.46984264",
"0.46747768",
"0.46554214",
"0.46397245",
"0.4639464",
"0.46221876",
"0.46042663",
"0.45990068",
"0.45884767",
"0.4573318",
"0... | 0.0 | -1 |
Construct a DAG file in order to submit a production to the condor scheduler using util_RIFT_pseudo_pipe.py | def build_dag(self, user=None):
cwd = os.getcwd()
#os.chdir(self.production.event.meta['working directory'])
#os.chdir(os.path.join(self.production.event.repository.directory,
# self.category))
if self.production.event.repository:
gps_file = self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(\n metadata: ProjectMetadata, pipeline_name, env, target_path\n): # pylint: disable=too-many-locals\n loader = jinja2.FileSystemLoader(str(Path(__file__).parent))\n jinja_env = jinja2.Environment(autoescape=True, loader=loader, lstrip_blocks=True)\n jinja_env.filters[\"slugify\"] = slugify\... | [
"0.6791086",
"0.66254175",
"0.6543903",
"0.6439742",
"0.62908417",
"0.6201106",
"0.5974608",
"0.5915672",
"0.58422476",
"0.57603854",
"0.56926197",
"0.5625026",
"0.5587469",
"0.556881",
"0.55680734",
"0.5542769",
"0.54487556",
"0.5446099",
"0.54422826",
"0.5420124",
"0.540782... | 0.67467165 | 1 |
Submit a DAG file to the condor cluster (using the RIFT dag name). This is an overwrite of the near identical parent function submit_dag() | def submit_dag(self):
os.chdir(self.production.rundir)
os.system("cat *_local.cache > local.cache")
for psdfile in self.production.get_psds("xml"):
ifo = psdfile.split("/")[-1].split("_")[1].split(".")[0]
os.system(f"cp {psdfile} {ifo}-psd.xml.gz")
self.before_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def submit_dag(config, dag_file):\n with SUBMIT_LOCK:\n try:\n condor_dag_cmd = osp.join(get_condor_bin_dir(config),\n CONDOR_COMMAND['dag'])\n\n pipe = subprocess.Popen(args=(condor_dag_cmd, '-force', dag_file),\n ... | [
"0.7314224",
"0.709528",
"0.63085747",
"0.6191946",
"0.60718346",
"0.5973721",
"0.59433025",
"0.5939774",
"0.5474602",
"0.5430673",
"0.53907543",
"0.53773415",
"0.5344598",
"0.52862775",
"0.52801114",
"0.52279294",
"0.5198155",
"0.5184064",
"0.5162414",
"0.5139023",
"0.512411... | 0.7345251 | 0 |
Attempt to ressurrect a failed job. | def resurrect(self):
try:
count = self.production.meta['resurrections']
except:
count = 0
count = len(glob.glob(os.path.join(self.production.rundir, "marginalize_intrinsic_parameters_BasicIterationWorkflow.dag.rescue*")))
if "allow ressurect" in self.production.me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fail(self):\n self.cleanup()\n self.runner.report_job_fail(self.id)",
"def fail_job( self, job_state ):\n self.stop_job( self.sa_session.query( self.app.model.Job ).get( job_state.job_wrapper.job_id ) )\n job_state.job_wrapper.fail( getattr( job_state, \"fail_message\", GENERIC_RE... | [
"0.67493665",
"0.6399685",
"0.632808",
"0.629788",
"0.62523544",
"0.6112573",
"0.6107305",
"0.6102979",
"0.6093228",
"0.6066026",
"0.60121393",
"0.600223",
"0.5928469",
"0.5924552",
"0.5916698",
"0.5915556",
"0.58674157",
"0.58567256",
"0.58405894",
"0.5821266",
"0.58085227",... | 0.5971702 | 12 |
Collect all of the log files which have been produced by this production and return their contents as a dictionary. | def collect_logs(self):
logs = glob.glob(f"{self.production.rundir}/*.err") #+ glob.glob(f"{self.production.rundir}/*/logs/*")
logs += glob.glob(f"{self.production.rundir}/*.out")
messages = {}
for log in logs:
with open(log, "r") as log_f:
message = log_f.rea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAllEntries(self):\n \n log_entries_dict = collections.defaultdict(list)\n for logfile in os.listdir(self.log_folder):\n log = os.path.join(self.log_folder, logfile)\n with open(log, 'rb') as l:\n logCSVreader = csv.reader(l, delimiter=\"|\")\n ... | [
"0.7099511",
"0.685938",
"0.6689108",
"0.65805835",
"0.654927",
"0.65117294",
"0.6469987",
"0.6433183",
"0.6433183",
"0.63312954",
"0.63312954",
"0.6329445",
"0.626463",
"0.6235582",
"0.6214215",
"0.61539227",
"0.61492366",
"0.6038331",
"0.60342073",
"0.6025179",
"0.5985445",... | 0.8644327 | 0 |
Check for the production of the posterior file to signal that the job has completed. | def detect_completion(self):
results_dir = glob.glob(f"{self.production.rundir}")
if len(results_dir)>0: # dynesty_merge_result.json
if len(glob.glob(os.path.join(results_dir[0], f"extrinsic_posterior_samples.dat"))) > 0:
return True
else:
return F... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_for_finished_job(self):\n raise NotImplementedError",
"def _check_results(self):\n if not 'EXECUTION OF GAMESS TERMINATED NORMALLY' in self.file_dic['output']:\n print self.job_name + \" didn't finish\"\n raise TypeError('Calculation didn\\'t finish')",
"def isFin... | [
"0.61166656",
"0.6105869",
"0.6094812",
"0.6077702",
"0.60449344",
"0.6024275",
"0.5960014",
"0.59353745",
"0.58733517",
"0.5869293",
"0.5852132",
"0.5831765",
"0.58316183",
"0.58316183",
"0.58316183",
"0.57644093",
"0.57264334",
"0.5707587",
"0.5698898",
"0.56910706",
"0.568... | 0.5851303 | 11 |
Collect the combined samples file for PESummary. | def samples(self):
return glob.glob(os.path.join(self.production.rundir, "extrinsic_posterior_samples.dat")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gather_sample(self, my_file, collector=None):\n\n pass",
"def per_sample_dot_files(self):\n # The output directory #\n directory = DirectoryPath(self.a.out_dir+'per_sample_ontology/')\n directory.create_if_not_exists()\n # Main loop #\n for i, sample in self.df_sampl... | [
"0.6286086",
"0.61118436",
"0.5901913",
"0.5859824",
"0.5801021",
"0.57871133",
"0.57731056",
"0.57341135",
"0.5628486",
"0.5589119",
"0.55838966",
"0.5580174",
"0.55663544",
"0.5565593",
"0.5554349",
"0.5533319",
"0.5505845",
"0.5455812",
"0.54384106",
"0.54338044",
"0.54219... | 0.57615197 | 7 |
Builds the sbatch file in order to combine genomics.vcf samples contained in current_batch in a single one. | def build_GenotypeGVCFs_sbatch(working_dir, combined_gvcf_files, scratch=False, interval=None):
name_batch1 = os.path.basename([item for item in combined_gvcf_files if "batch1" in item][0])
interval_name = ""
#there must be at least one batch so look for it, not elegant but works
if name_batch1.s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GenotypeGVCFs():\n #creates sbatch files to merge batches of batch_size genomics vcf\n cwd = os.getcwd()\n sbatch_files = []\n if not os.path.isdir(os.path.join(cwd, \"01_CombineGVCFs\")):\n sys.exit(\"Directory 01_CombineGVCFs does not exits exists, something went wrong here.\")\n if os.... | [
"0.672763",
"0.58610225",
"0.5566432",
"0.5434747",
"0.5414289",
"0.5365546",
"0.53649545",
"0.5345907",
"0.5329101",
"0.52768123",
"0.52546614",
"0.5217361",
"0.5212177",
"0.5211676",
"0.5201498",
"0.5170869",
"0.5153864",
"0.5102112",
"0.5095486",
"0.509299",
"0.5088659",
... | 0.7443647 | 0 |
Runs GenotypeGVCFs on all combined files produced previosuly (assumes folder structure) | def GenotypeGVCFs():
#creates sbatch files to merge batches of batch_size genomics vcf
cwd = os.getcwd()
sbatch_files = []
if not os.path.isdir(os.path.join(cwd, "01_CombineGVCFs")):
sys.exit("Directory 01_CombineGVCFs does not exits exists, something went wrong here.")
if os.path.isdir(os.p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def genotype_gvcfs(gatk, xmx, cores,\n inputs, output,\n reference, bed_file=None):\n commands = []\n command = GENOTYPEGVCFS_TEMPLATE.format(xmx, gatk, reference, output)\n command = command + ' --variant ' + ' --variant '.join(inputs)\n if bed_file is not None:\n command = comma... | [
"0.6848684",
"0.6842689",
"0.66581684",
"0.65295035",
"0.6394109",
"0.6241394",
"0.62080246",
"0.6191771",
"0.6087738",
"0.60208416",
"0.5983195",
"0.59366655",
"0.5929118",
"0.5920719",
"0.5885988",
"0.58807164",
"0.5872283",
"0.5867078",
"0.5824099",
"0.5782577",
"0.5766673... | 0.77105707 | 0 |
Returns a duplicate of the profile instance. | def duplicate(self):
duplicate = Profile()
for i in self.__dict__:
if type(getattr(self, i)) is dict:
setattr(duplicate, i, getattr(self, i).copy())
else:
setattr(duplicate, i, getattr(self, i))
return duplicate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy(self):\n return Population(self)",
"def copy(self):\n return self.__class__(dict(self))",
"def copy(self):\n return self.__class__(self)",
"def copy(self):\n return self.__class__(self)",
"def strip_copy(self):\n return strip_profiles_copy(self)",
"def get_full... | [
"0.65038234",
"0.6457992",
"0.6320341",
"0.6320341",
"0.6286995",
"0.62803096",
"0.6278374",
"0.62589824",
"0.6257265",
"0.62398297",
"0.62353045",
"0.6209959",
"0.6207495",
"0.61983556",
"0.61954135",
"0.61781174",
"0.61590487",
"0.6154005",
"0.6154005",
"0.6154005",
"0.6138... | 0.81918967 | 0 |
To save this profile intance to xml file using a XmlWriter. xwriter>should be a XmlWriter instance. | def save_to_xml(self, xwriter):
xwriter.WriteStartElement("Profile")
xwriter.WriteAttributeString("Name", self.Name)
xwriter.WriteStartAttribute("Version")
xwriter.WriteValue(self.Version)
xwriter.WriteEndAttribute()
for var_name in self.__dict__:
v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured wri... | [
"0.75539035",
"0.66137195",
"0.6487311",
"0.64123094",
"0.61329263",
"0.6101124",
"0.60908806",
"0.60279024",
"0.60243684",
"0.59840643",
"0.59765226",
"0.58507067",
"0.585065",
"0.58390087",
"0.58205575",
"0.58169997",
"0.5809403",
"0.58001035",
"0.56534475",
"0.5646809",
"0... | 0.7909768 | 0 |
Writes a dictionary to an xml file in the form of etc. attribute_name>The name of the dictonary attribute to write. xmlwriter>The xml writer to write with. write_empty>A bool of whether to write empty values to the xml file. Default is don't write them. | def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):
if attribute_name in ("IllegalCharacters", "Months"):
write_empty = True
dictionary = getattr(self, attribute_name)
xmlwriter.WriteStartElement(attribute_name)
for key in dictionary:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeDictToXMLFile(outfile, target, dict):\n targetStr = \"\\t\\t<Target>%s</Target>\\n\" % (escape(target),)\n for key in dict.keys():\n outfile.write('\\t<AVU>\\n')\n outfile.write(targetStr)\n outfile.write(\"\\t\\t<Attribute>%s</Attribute>\\n\" % (escape(key),) )\n outfile... | [
"0.6729531",
"0.65415585",
"0.6262941",
"0.6016264",
"0.5983849",
"0.5939113",
"0.58700234",
"0.58539236",
"0.5813194",
"0.5684836",
"0.56377923",
"0.56335074",
"0.5623472",
"0.56098765",
"0.55822074",
"0.5524061",
"0.5433924",
"0.5403624",
"0.5400973",
"0.53912425",
"0.53563... | 0.80239266 | 0 |
Writes a list to an xml file in the form of value value etc. attribute_name>The name of the list attribute to write. xmlwriter>The xml writer to write with. write_empty>A bool of whether to write empty values to the xml file. Default is don't write them. | def write_list_to_xml(self, attribute_name, xmlwriter, write_empty=False):
attribute_list = getattr(self, attribute_name)
xmlwriter.WriteStartElement(attribute_name)
for item in attribute_list:
if item or write_empty:
xmlwriter.WriteElementString("Item", item)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(lst):\n # TODO",
"def write(self, data, filename):\n id_ = 1\n weightlist_el = Element('weight-list')\n for dataset in data:\n weight_el = SubElement(weightlist_el, 'weight')\n id_el = SubElement(weight_el, 'id')\n id_el.text = str(id_)\n ... | [
"0.6497926",
"0.6048247",
"0.60110986",
"0.5987103",
"0.59648246",
"0.5957363",
"0.5939886",
"0.57804435",
"0.5734696",
"0.57158196",
"0.5674186",
"0.5656518",
"0.56491786",
"0.55900544",
"0.55637085",
"0.55263245",
"0.546913",
"0.54400545",
"0.54337585",
"0.54084957",
"0.539... | 0.8491795 | 0 |
Writes a string to an xml file in the form of string attribute_name>The name of the string attribute to write. xmlwriter>The xml writer to write with. write_empty>A bool of whether to write empty strings to the xml file. Default is write empty strings. | def write_string_to_xml(self, attribute_name, xmlwriter, write_empty=True):
string = getattr(self, attribute_name)
if string or write_empty:
xmlwriter.WriteElementString(attribute_name, string) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_write_string():\n buf = make_buffer()\n writer = XmlWriter(buf)\n writer.write_element('value', 'myvalue')\n writer.flush()\n assert_equals(decode_buffer(buf), '<value>myvalue</value>')",
"def write_dict_to_xml(self, attribute_name, xmlwriter, write_empty=False):\r\n if attribute_n... | [
"0.6247003",
"0.58558315",
"0.5849161",
"0.57865465",
"0.5765893",
"0.5722448",
"0.5617337",
"0.5580295",
"0.55794567",
"0.554683",
"0.55136317",
"0.55108947",
"0.54859346",
"0.54719436",
"0.5470503",
"0.54250485",
"0.53961504",
"0.5380822",
"0.5374993",
"0.53526664",
"0.5339... | 0.8222901 | 0 |
Writes a boolean to an xml file in the form of true/false attribute_name>The name of the attribute to write. xmlwriter>The xml writer to write with. | def write_bool_to_xml(self, attribute_name, xmlwriter):
xmlwriter.WriteStartElement(attribute_name)
xmlwriter.WriteValue(getattr(self, attribute_name))
xmlwriter.WriteEndElement() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeAttribute(self, *args):\n if type(args[1]) == type(True): return _libsbml.XMLOutputStream_writeAttributeBool(self, *args)\n\n\n return _libsbml.XMLOutputStream_writeAttribute(self, *args)",
"def writeAttributeBool(self, *args):\n return _libsbml.XMLOutputStream_writeAttributeBool(se... | [
"0.74465925",
"0.73603874",
"0.7117586",
"0.67680955",
"0.66733044",
"0.63848156",
"0.62347335",
"0.59897983",
"0.591663",
"0.5848001",
"0.57468945",
"0.564458",
"0.5640521",
"0.5636943",
"0.561803",
"0.5609525",
"0.5577795",
"0.557284",
"0.55520767",
"0.5486341",
"0.54855955... | 0.8478797 | 0 |
Loads the profile instance from the Xml. Xml>should be a XmlNode/XmlDocument containing a profile node. | def load_from_xml(self, Xml):
try:
#Text vars
self.Name = Xml.Attributes["Name"].Value
if "Version" in Xml.Attributes:
self.Version = float(Xml.Attributes["Version"].Value)
for var_name in self.__dict__:
if type(getattr(s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.Docume... | [
"0.60300845",
"0.5605986",
"0.55443424",
"0.5518848",
"0.5350278",
"0.5345203",
"0.53076",
"0.52905643",
"0.5275819",
"0.52674824",
"0.521211",
"0.5208215",
"0.5208047",
"0.51756513",
"0.51629275",
"0.51257974",
"0.50973666",
"0.5068245",
"0.50489193",
"0.5018405",
"0.5017763... | 0.5006978 | 21 |
Load profiles from a xml file. If no profiles are found it creates a blank profile. file_path>The absolute path to the profile file Returns a dict of the found profiles and a list of the lastused profile(s) | def load_profiles(file_path):
profiles, lastused = load_profiles_from_file(file_path)
if len(profiles) == 0:
#Just in case
profiles["Default"] = Profile()
profiles["Default"].Name = "Default"
#Some default templates
profiles["Default"].FileTemplate = "{<series>}{... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.Docume... | [
"0.81233364",
"0.77697146",
"0.67844415",
"0.6566505",
"0.636305",
"0.62747896",
"0.6168049",
"0.6128615",
"0.61190236",
"0.6096743",
"0.5969697",
"0.5946075",
"0.59331304",
"0.5897405",
"0.58071595",
"0.57577217",
"0.5669829",
"0.564856",
"0.55757815",
"0.5557105",
"0.553909... | 0.7774989 | 1 |
Loads profiles from a file. file_path>The absolute path the xml file Returns a dict of the profiles | def load_profiles_from_file(file_path):
profiles = {}
lastused = ""
if File.Exists(file_path):
try:
with StreamReader(file_path) as xmlfile:
xmldoc = XmlDocument()
xmldoc.Load(xmlfile)
if xmldoc.DocumentElement.Name == "Profiles":... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n return profiles",
"def load_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n if len(profiles) == 0:\r\n #Just in case\r\n profiles[\"Default\"] = P... | [
"0.7816373",
"0.7494791",
"0.69231015",
"0.68052244",
"0.669596",
"0.6337234",
"0.6331917",
"0.60487854",
"0.6018389",
"0.59737855",
"0.59296596",
"0.59177107",
"0.57330143",
"0.57177866",
"0.5666612",
"0.5610871",
"0.56013894",
"0.5478734",
"0.54241526",
"0.5402424",
"0.5382... | 0.755472 | 1 |
Load profiles from a xml file. If no profiles are found it returns an empty dict. file_path>The absolute path to the profile file Returns a dict of the found profiles. | def import_profiles(file_path):
profiles, lastused = load_profiles_from_file(file_path)
return profiles | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.Docume... | [
"0.7626121",
"0.73465997",
"0.66420317",
"0.64548403",
"0.6422596",
"0.61153334",
"0.60498744",
"0.59523565",
"0.5855331",
"0.57868946",
"0.5670626",
"0.56380814",
"0.5517114",
"0.5462929",
"0.54440254",
"0.54290825",
"0.5421971",
"0.5367816",
"0.53443223",
"0.53429097",
"0.5... | 0.74111307 | 1 |
Saves the profiles to an xml file. | def save_profiles(file_path, profiles, lastused=""):
try:
xSettings = XmlWriterSettings()
xSettings.Indent = True
with XmlWriter.Create(file_path, xSettings) as writer:
writer.WriteStartElement("Profiles")
if lastused:
writer.WriteAttributeStrin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_profile(file_path, profile):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n profile.save_to_xml(writer)\r\n except Exception, ex:\r\n MessageBox.Show(\"An error occured wri... | [
"0.77402055",
"0.7153504",
"0.68901503",
"0.65989304",
"0.6585187",
"0.6567334",
"0.65197617",
"0.64477056",
"0.6292227",
"0.62706214",
"0.62006843",
"0.61452764",
"0.6128959",
"0.610915",
"0.6092176",
"0.6072949",
"0.60326505",
"0.60216737",
"0.5989765",
"0.5988323",
"0.5958... | 0.76685476 | 1 |
Saves a single profile to an xml file. | def save_profile(file_path, profile):
try:
xSettings = XmlWriterSettings()
xSettings.Indent = True
with XmlWriter.Create(file_path, xSettings) as writer:
profile.save_to_xml(writer)
except Exception, ex:
MessageBox.Show("An error occured writing the settings fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_profile(self):\n self.save()",
"def save_profiles(file_path, profiles, lastused=\"\"):\r\n try:\r\n xSettings = XmlWriterSettings()\r\n xSettings.Indent = True\r\n with XmlWriter.Create(file_path, xSettings) as writer:\r\n writer.WriteStartElement(\"Profiles\")\... | [
"0.720293",
"0.71852773",
"0.6845692",
"0.6742488",
"0.6558711",
"0.6505063",
"0.645329",
"0.64384377",
"0.6373711",
"0.63664985",
"0.6296266",
"0.6280068",
"0.62594324",
"0.6223609",
"0.6212394",
"0.6104943",
"0.60820746",
"0.60820746",
"0.6042483",
"0.60255325",
"0.60242546... | 0.8002179 | 0 |
Main program to load data into the db. | def main():
option_parser = optparse.OptionParser(usage='usage: %prog [options] filename')
option_parser.add_option('-d', '--debug',
dest='debug',
default=False,
action='store_true',
help='E... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\n\tgdl = TwitterDataLoader()\t\n\tgdl.load_twitter_data_to_db(truncate_table=False, skip_loaded_files=True)",
"def populate_db_command():\n print(\"Populating DB with sample data.\")\n populate_db()\n print \"Done\"",
"def main():\r\n\r\n # delete the database file if it already exist... | [
"0.7243006",
"0.69598734",
"0.6931684",
"0.68877065",
"0.68784547",
"0.6848689",
"0.6814924",
"0.6806881",
"0.67778087",
"0.6770427",
"0.6755996",
"0.6755282",
"0.6713464",
"0.6702432",
"0.6670045",
"0.6666217",
"0.6662877",
"0.6647903",
"0.6627729",
"0.6620025",
"0.6592809",... | 0.0 | -1 |
This function returns the softmax value for the given input | def softmax(X):
if len(X) > 1:
expo = np.exp(X)
expo_sum = np.sum(np.exp(X))
ret = expo/expo_sum
print(f'The softmax result on the given input {ret}')
return ret
else:
raise ValueError("Sorry, lenght of the list should be more than one") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def softmax(x):\n #pass # TODO: Compute and return softmax(x)\n return np.exp(x) / np.sum(np.exp(x), axis=0)",
"def softmax(x):\n \"\"\"\"\"\"\n return exp(x) / sum(exp(x), axis=0)",
"def softmax(x): \n e_x = np.exp(x - np.max(x)) \n return e_x / e_x.sum()",
"def softmax(x)... | [
"0.85517555",
"0.85172",
"0.85039085",
"0.85021806",
"0.8486158",
"0.8471101",
"0.84642345",
"0.84226495",
"0.84178317",
"0.84178317",
"0.84136945",
"0.84076935",
"0.83944535",
"0.8392644",
"0.8392644",
"0.8392644",
"0.8392644",
"0.8392644",
"0.8392644",
"0.8392644",
"0.83926... | 0.0 | -1 |
This function returns the softmax derivative value for the given input | def softmax_derivative(x):
der = derivative(softmax,x,dx=1e-9)
return der | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def softmax_derivative(Z):\n\treturn None",
"def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference",
"def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum(axis=0) # only difference\r",
"def softmax(x):\r\n e_x = np.exp(x - np... | [
"0.78823423",
"0.7861486",
"0.7818649",
"0.78063107",
"0.7801125",
"0.7801125",
"0.7801125",
"0.7801125",
"0.77747434",
"0.7771414",
"0.7755482",
"0.7754241",
"0.76962405",
"0.7687193",
"0.7682934",
"0.76776224",
"0.76399696",
"0.7632133",
"0.7632133",
"0.76276577",
"0.762613... | 0.8873611 | 0 |
pos word id; i embedding dim id; d_model embedding dim | def get_angles(pos, i, d_model):
angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
return pos * angle_rates | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_model(allidx,MAX_LENGTH,onlyArg):\n wordidx, labelidx, featuresidx, extraidx=allidx\n posidx, neridx, depidx, distanceidx, chnkidx, wikineridx, dbpedianeridx, subneridx = featuresidx\n\n main_input = Input(shape=(MAX_LENGTH,), name='main_input', dtype='int32')\n inputNodes=[main_input]\n\n ... | [
"0.65528405",
"0.644471",
"0.62645143",
"0.6260667",
"0.6213953",
"0.605486",
"0.6034496",
"0.5999264",
"0.59976137",
"0.59722745",
"0.595033",
"0.59265804",
"0.592424",
"0.58655846",
"0.58250475",
"0.58209103",
"0.5806985",
"0.5793572",
"0.5783189",
"0.5767571",
"0.57570547"... | 0.0 | -1 |
Construct a layernorm module in the TF style (epsilon inside the square root). | def __init__(self, hidden_size, eps=1e-12):
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
self.bias.data.zero_()
self.weight.data.fill_(1.0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verhulst(nb_init, t0, tf, eps, methode, gamma, K) :\n f=lambda y,t : gamma*y*(1-y/K)\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y",
"def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\n\n f=lambda y, t : gamma*y\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n ... | [
"0.62754345",
"0.60140204",
"0.5792275",
"0.57416606",
"0.55366963",
"0.55194634",
"0.54221153",
"0.5412391",
"0.5388891",
"0.53688395",
"0.5351537",
"0.5287469",
"0.5258017",
"0.5232318",
"0.5227428",
"0.52260095",
"0.5220578",
"0.5220438",
"0.5192999",
"0.5192457",
"0.51924... | 0.0 | -1 |
AppendCols(numCols=1) > bool Exactly the same as AppendRows() but for columns. | def AppendCols(self, numCols=1): # real signature unknown; restored from __doc__
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_new_cols(cat, prefix=\"\", floatcols=None, boolcols=None):\n\t\n\tif floatcols != None:\n\t\tfor col in floatcols:\n\t\t\tcat.add_column(astropy.table.MaskedColumn(name=prefix+col, dtype=float, length=len(cat)))\n\t\t\tcat[prefix+col].mask = [True] * len(cat)\n\tif boolcols != None:\n\t\tfor col in boolcol... | [
"0.6017477",
"0.5631939",
"0.55924374",
"0.5548952",
"0.5529502",
"0.5455568",
"0.5444327",
"0.54101974",
"0.53786486",
"0.53754896",
"0.53557205",
"0.5345275",
"0.53391767",
"0.53190124",
"0.5316484",
"0.5307784",
"0.528852",
"0.5280459",
"0.5254802",
"0.525032",
"0.5224798"... | 0.89480335 | 3 |
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